Philippa Kaur

Data collectionBetween June 27 and August 25, 2016, 6600 km of simultaneous visual and passive acoustic line transect surveys were completed on the National Oceanic and Atmospheric Administration (NOAA) ship Henry B. Bigelow5. Survey effort was distributed along saw tooth track lines spanning the continental slope from Virginia (US) to the southern tip of Nova Scotia (Canada) (36–42 N) and on several larger track lines over the abyssal plain. Two teams of visual observers independently recorded sightings of marine mammals using high-powered Fujinon binoculars (25 × 150; Fujifilm, Valhalla, NY) as well as environmental conditions (e.g. sea state) every 30 min.The speed of sound in water was collected three times each day (morning, noon, evening) by measuring conductivity, temperature, and depth (CTD) at specific intervals in the water column. The sound speed closest to the depth of the towed hydrophone array was extracted. On alternating survey days, Simrad EK60 single beam scientific echosounders operating at frequencies of 18, 38, 70, 120 and 200 kHz were used to collect active acoustic data.When possible during daylight hours (06:00–18:00 ET), passive acoustic data were collected continuously using a custom-built linear array composed of eight hydrophone elements and a depth sensor (Keller America Inc. PA7FLE, Newport News, VA) within two oil-filled modular sections separated by 30 m of cable (Fig. 1). The array was towed 300 m behind the vessel at approximately 5–10 m depth while the vessel was in waters more than 100 m deep and underway at speeds of 16–20 km/h. For more details see DeAngelis et al.31, with the only change being that two APC hydrophones and one Reson hydrophone in the aft section were replaced with HTI-96-Min hydrophones (High Tech, Inc., Long Beach, MS). The HTI’s had a flat frequency response from 1 to 30 kHz (− 167 dB re V/uPa ± 1.5 dB). Recordings were made using the acoustical software PAMGuard (v.1.15.02)34. This analysis used the data recorded by the last two 192 kHz sampled hydrophones in the array (MF5 and MF6).Figure 1The linear towed array included eight hydrophone elements and a depth sensor within two oil-filled modular sections separated by 30 m of cable. Six hydrophones sampled at 192 kHz (MF1–MF6) and two sampled at 500 kHz. The hydrophones were connected to two National Instruments sound cards (NI-USB-6356). A high pass filter of 1 kHz was applied by the recording system to reduce the amount of vessel noise in the recordings. This analysis used the passive acoustic data from MF5 and MF6. The schematic is not to scale.Full size imageClick detection and 2D event localizationThe passive acoustic data were filtered using a Butterworth band pass filter (4th order) between 2 and 20 kHz and decimated to 96 kHz to improve sperm whale click resolution. Clicks were automatically detected using the PAMGuard (v.2.01.03) general sperm whale click detector with a trigger threshold of 12 dB.Using PAMGuard’s bearing time display, all detections were reviewed to classify click types and mark click trains as “events” based on consistent changes in bearing, audible sound, ICI and spectral characteristics. Each event was marked to an individual level, tracking a whale from the first to the last detected click15,35. All events containing usual clicks were localized with PAMGuard’s Target Motion Analysis (TMA) module’s 2D simplex optimization algorithm. For further analysis, events were truncated at a slant range of 6500 m (Supplementary Fig. S1).Echosounder analysisA regression analysis was run using the R package MASS36. To account for overdispersion, a negative binomial generalized linear model (GLM) with a log link function was applied to a dataset of the daily acoustic detections33. Echosounder state (active versus passive), month (June, July, August), and habitat type (slope or abyssal) were included as covariates, with the total number of daily detections as the response variable. The track line distance covered per day was used as an offset for effort. The best fitting model was selected based on backwards stepwise selection using Akaike’s information criterion (AIC) and the single-term deletion method using Chi-squared goodness-of-fit tests37.3D localizationExtracting a .wav clip for each click and attributing metadataAn automated process was developed using the R package PAMpal38 (v. 0.14.0) to extract the time of each click in the marked events from PAMGuard databases, generate a .wav clip for each click, and attribute all metadata (e.g., event 2D localization, array depth, radial distance, sea state, and sound speed) necessary for estimating the click depth.Slant delayUsing the methods established by DeAngelis et al.31 and custom Matlab R2021a (MathWorks Inc., Natick, NA) scripts, the multipath arrival of clicks and surface reflected echoes were used to mathematically convert the linear array into a 2D planar array and estimate 3D localizations. Using the .wav clips exported from PAMPal, the time delay between the click and the corresponding surface reflected echo, known as the slant delay, was measured via autocorrelation. Within the autocorrelation solution’s envelope of correlation values, the optimal slant delay was measured using the peak with the highest correlation value above a threshold of 0.02 and within an expected time window after the direct click of 0.0005–0.015 s. Although theoretically a surface reflected echo could have arrived less than a millisecond ( 5 min) were categorized as U shaped, and as shallow ( 1600 m) based on the maximum click depth (Fig. 3).Figure 3Example of click depths (m) over time (min) for events categorized as (a) U shaped and shallow ( 1600 m).Full size imageClick depths were then binned at 400 m intervals to account for an animal’s unknown horizontal movement over time as well as uncertainty in the estimated click depths, and the total time an animal spent within each depth bin was calculated. For each event with a U shaped click depth pattern, the depth bin in which the bottom phase occurred6 was determined. Finally, to assess if a whale was diving in the water column or close to the seafloor, the depth bin in which the 90th percentile of the click depths was recorded was compared to the bin including the seafloor depth. If the whale was more than 400 m above the seafloor, it was determined to be diving in the water column.Distance samplingDepth-corrected average horizontal perpendicular distancesFor each event, a depth-corrected average horizontal perpendicular distance was calculated using the TMA derived perpendicular slant range and the average depth or an assumed depth in the Pythagorean theorem19,31. The weighted mean, first quartile, and third quartile of the average depths were tested as assumed depths for events excluded from 3D localization. If depth was greater than or equal to the slant range the perpendicular distance was coerced to 0, indicating the whale was diving directly below the track line. The resulting distribution of depth-corrected perpendicular distances that aligned most with distance sampling theory was used in the final distance analysis.Acoustic density and abundance estimationThe R package Distance41,42 (v.1.0.4) was used to estimate two separate detection functions based on the uncorrected slant ranges and the depth-corrected perpendicular distances. Half-normal, uniform, and hazard rate key functions were tested with cosine, simple polynomial, and Hermite polynomial adjustment terms. The best fitting models were selected based on the AIC, the Kolmogorov–Smirnov (K–S) test, the Cramer-von Mises (CvM) test, quantile–quantile plots, and visual review of the fitted models43. The probability of detection, abundance, and effective strip half width (ESW) were then estimated for foraging sperm whales.Permitting authorityData used in this manuscript were collected during surveys that were completed under U.S. Marine Mammal Protection Act permit numbers 17355 and 21371 issued to the Northeast Fisheries Science Center. More

Attention combination mechanismDue to the difficulty in extracting features from target areas in images, the high computational effort of the model and the low accuracy of detection are addressed. As shown in Fig. 3, we introduce a lightweight feedforward convolutional attention module CBAM after the backbone network Focus module of the YOLOv5s network model. The SE-Net (Squeeze and Excitation Networks) channel attention module is posted at the end of the backbone network. We propose an attention combination mechanism based on the YOLOv5s network model and name the improved network model YOLOv5s-CS. Where the CBAM module has a channel number of 128, a convolutional kernel size of 3 and a step size of 2, the SELayer has a channel number of 1024 and a step size of 4.Figure 3YOLOv5 backbone network structure before and after improvement.Full size imageConvolutional block attention module networkIn 2018, Woo et al.25 proposed the lightweight feedforward convolutional attention module CBAM. The CBAM module focuses on feature information from both channels and space dimensions and combines feature information to some extent to obtain more comprehensive reliable attentional information26. CBAM consists of two submodules, the channel attention module (CAM) and spatial attention module (SAM), and its overall module structure is shown in Fig. 4a.Figure 4Principle of CBAM.Full size imageThe working principle of the CAM is shown in Fig. 4b. First, the feature map F is input at the input entrance. Second, the global maximum pooling operation and the global average pooling operation are applied to the width and height of the feature map respectively to obtain two feature maps of the same size. Third, two feature maps of the same size are input to the shared parameter network MLP at the same time. Finally, the new feature map output from the shared parameter network is subjected to a summation operation and a sigmoid activation function to obtain the channel attention features ({M}_{c}).The channel attention module CAM is calculated as shown in Formula (1):$${text{M}}_{rm{c}}({text{F}}){=sigma}({text{MLP (AvgPool (F))}}+ {text MLP (MaxPool (F)))}{=sigma}({rm{W}}_{1}({text{W}}_{0}({text{F}}_{{{rm{avg}}}^{rm{c}}}))+{rm{W}}_{0}({rm{W}}_{1}({rm{F}}_{{{rm{max}}}^{rm{c}}})))$$
(1)
where σ represents the sigmoid function, MLP represents the shared parameter network, ({text{W}}_{0}) and ({text{W}}_{1}) represent the shared weights, ({text{F}}_{text{avg}}^{text{c}}) is the result of feature map F after global average pooling, and ({text{F}}_{text{max}}^{text{c}}) is the result of feature map F after global maximum pooling.The working principle of SAM is shown in Fig. 4c. The feature map F’ is regarded as the input of the SAM. F’ is obtained by multiplying the input of SAM with the output of CAM. First, the global maximum pooling operation and the global average pooling operation are applied to the channels of the feature map to obtain two feature maps of the same size. Second, two feature maps that have completed the pooling operation are stitched at the channels and the feature channels are dimensioned down using the convolution operation to obtain a new feature map. Finally, spatial attention features ({text{M}}_{text{s}}) are generated using the sigmoid activation function.The spatial attention module (SAM) is calculated, as shown in Formula (2):$${text{M}}_{text{s}}left({text{F}}right) {=sigma}left({text{f}}^{7 times 7}left(left[{text{AvgPool}}left({text{F}}right)text{;MaxPool}left({text{F}}right)right]right)right) {=sigma}left({text{f}}^{7 times 7}left(left[{text{F}}_{text{avg}}^{text{s}} ; {text{F}}_{text{max}}^{text{s}}right]right)right)$$
(2)
where σ is the sigmoid function, ({text{f}}^{7 times 7}) denotes the convolution operation with a filter size of 7 × 7, ({text{F}}_{text{avg}}^{text{s}}) is the result of the feature map after global average pooling, and ({text{F}}_{text{max}}^{text{s}}) is the result of the feature map after global maximum pooling.Squeeze and excitation networkIn 2018, Hu et al.27 proposed a single-path attention network structure SE-Net. SE-Net uses the idea of an attention mechanism to analyze the relationship feature maps by modeling and adaptively learning to obtain the importance of each feature map28 and then assigns different weights to the original feature map for updating according to the importance. In this way, SE-Net pays more attention to the features that are useful for the target task while suppressing useless feature information and allocates computational resources rationally to different channels to train the model to achieve better results.The SE-Net attention module is mainly composed of two parts: the squeeze operation and excitation operation. The structure of the SE-Net module is shown in Fig. 5.Figure 5The SE-Net module structure.Full size imageThe squeeze operation uses global average pooling to encode all spatial features on the channel as local features. Second, each feature map is compressed into a real number that has global information on the feature maps. Finally, the squeeze results of each feature map are combined into a vector as the weights of each group of feature maps. It is calculated as shown in Eq. (3):$${text{Z}}_{text{c}}={text{F}}_{text{sq}}left({text{u}}_{text{c}}right)=frac{1}{text{H} times {text{W}}}sum_{text{i=1}}^{text{H}}sum_{text{j=1}}^{text{W}}{{text{u}}}_{text{c}}left(text{i,j}right) , , , $$
(3)
where H is the height of the feature map, W is the feature map width, u is the result after convolution, z is the global attention information of the corresponding feature map, and the subscript c indicates the number of channels.After completing the squeeze operation to obtain the channel information, the feature vector is subjected to the excitation operation. First, it passes through two fully connected layers. Second, it uses the sigmoid function. Finally, the output weights are assigned to the original features. It is calculated as follows:$$text{s} = {text{F}}_{text{ex}}left(text{z,W}right){=sigma}left({text{g}}left(text{z,W}right)right){=sigma}left({text{W}}_{2}{delta}left({text{W}}_{1}{text{z}}right)right)$$
(4)
$$widetilde{{text{x}}_{rm{c}}}={text{F}}_{rm{scale}}left({text{u}}_{rm{c}}, {text{s}}_{rm{c}}right)={text{s}}_{rm{c}}{{text{u}}}_{rm{c}}$$
(5)
where σ is the ReLU activation function, δ represents the sigmoid activation function, and ({text{W}}_{1}) and ({text{W}}_{2}) represent two different fully connected layers. The vector s represents the set of feature mapping weights obtained through the fully connected layer and the activation function. (widetilde{{x}_{c}}) is the feature mapping of the x feature channel, ({text{s}}_{text{c}}) is a weight, and ({text{u}}_{text{c}}) is a two-dimensional matrix.Target detection layerThe garbage in rural areas is a smaller target and has fewer pixel characteristics, such as capsule, button butteries. Therefore, we insert a small target detection layer to improve the head network structure based on the original YOLOv5s network model for detecting objects with small targets to optimize the problem of missed detection in the original network model. The YOLOv5s network structure with the addition of the small target detection layer is shown in Fig. 6 and named YOLOv5s-STD.Figure 6The YOLOv5s-STD network structure.Full size imageIn the seventeenth layer of the neck network, operations such as upsampling are performed on the feature maps so that the feature maps continue to expand. Meanwhile, in the twentieth layer, the feature maps obtained from the neck network are fused with the feature maps extracted from the backbone network. We insert a detection layer capable of predicting small targets in the thirty-first layer. To improve the detection accuracy, we use a total of four detection layers for the output feature maps, which are capable of detecting smaller target objects. In addition to the three initial anchor values based on the original model, an additional set of anchor values is added as a way to detect smaller targets. The anchor values of the improved YOLOv5s network model are set to [5, 6, 8, 14, 15, 11], [10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119] and [116, 90, 156, 198, 373, 326].Bounding box regression loss functionThe loss function is an important indicator of the generalization ability of a model. In 2016, Yu et al.29 proposed a new joint intersection loss function IoU for bounding box prediction. IoU stands for intersection over union, which is a frequently used metric in target detection. It is used not only to determine the positive and negative samples, but also to determine the similarity between the predicted bounding box and the ground truth bounding box. It can be described as shown in the Eq. (6):$$text{IoU} = frac{left|text{A} capleft.{text{B}}right|right.}{left|{text{A}} cupleft.{text{B}}right|right.}$$
(6)
where the value domain of IoU ranges from [0,1]. A and B are the areas of arbitrary regions. Additionally, when IoU is used as a loss function, it has to scale invariance, as shown in Eq. (7):$$text{IoU_Loss} = 1-frac{left|text{A} cap left.{text{B}}right|right.}{left|{text{A}} cup left.{text{B}}right|right.}$$
(7)
However, when the prediction bounding box and the ground truth bounding box do not intersect, namely IoU = 0, the distance between the arbitrary region area of A and B cannot be calculated. The loss function at this point is not derivable and cannot be used to optimize the two disjoint bounding boxes. Alternatively, when there are different intersection positions, where the overlapping parts are the same but in different overlapping directions, the IoU loss function cannot be predicted.To address these issues, the idea of GIoU (Generalized Intersection over Union)30, in which a minimum rectangular Box C of A and B is added, was proposed in 2019 by Rezatofighi et al. Suppose the prediction bounding box is B, the ground truth bounding box is A, the area where A and B intersect is D, and the area containing two bounding boxes is C, as shown in Fig. 7.Figure 7GIoU evaluation chart.Full size imageThen, the GIoU calculation, as shown in Formula (8), is:$$text{GIoU}= text{IoU}-frac{text{|C}-left({text{A}} cup {text{B}}right)text{|}}{text{|C|}}$$
(8)
The GIoU_Loss is calculated as (9):$$text{GIoU_Loss=1}-{text{IoU}}-frac{text{|C}-left({text{A}} cup {text{B}}right)text{|}}{text{|C|}}$$
(9)
The original YOLOv5 algorithm uses GIoU_Loss as the loss function. Comparing Eqs. (6) and (8), it can be seen that GIoU is a new penalty term (frac{text{|C}-left({text{A}} cup {text{B}}right)text{|}}{text{|C|}}) that is added to IoU and is clearly represented by Fig. 7.Although the GIoU loss function solves the problem that the gradient of the IoU loss function cannot be updated in time and the prediction bounding box, the direction of the ground truth bounding box is not consistent when predicting, but there are still disadvantages, as shown in Fig. 8.Figure 8Comparsion of loss values.Full size imageFigure 8 shows three different position relationships formed when the predicted bounding box and the ground truth bounding box overlap exactly. Among them, the ratio of the length to width of the green grounding truth bounding box is 1:2, and the red predicted bounding box has the same aspect ratio as the ground truth bounding box, but the size is only one-half of the green ground truth bounding box. When the prediction bounding box and the ground truth bounding box completely overlap, the GIoU degenerates to the IoU, and the GIoU value and IoU value for the three different position cases are 0.45 at this time. The GIoU loss function does not directly reflect the distance between the prediction bounding box and the ground truth bounding box. Therefore, we introduce the CIoU (Complete Intersection over Union)31 loss function to replace the original GIoU loss function in the YOLOv5 algorithm and continue to optimize the prediction bounding box.Therefore, the CIoU is calculated as (10):$$text{GIoU_Loss}=1-text{IoU}-frac{text{|C}-left({text{A}} cup {text{B}}right)text{|}}{text{|C|}}$$
(10)
where b and ({text{b}}^{text{gt}}) denote the centroids of the prediction bounding box and the ground truth bounding box, respectively, ({rho}) is the Euclidean distance between the two centroids, and c is the diagonal length of the minimum closed area formed by the prediction bounding box and the ground truth bounding box.(alpha) is the parameter used to balance the scale, and v is the scale consistency used to measure the aspect ratio between the prediction bounding box and the ground truth bounding box, as shown in Eqs. (11) and (12).$$alpha =frac{text{v}}{left(1-text{IoU}right)+{text{v}}^{{prime}}}$$
(11)
$$text{v} = frac{4}{{pi}^{2}}{left({text{arctan}}frac{{omega}^{text{gt}}}{{text{h}}^{text{gt}}}- text{arctan}frac{{omega}^{text{p}}}{{text{h}}^{text{p}}}right)}^{2}$$
(12)
Therefore, the expression of CIoU_Loss can be obtained according to Eqs. (10), (11) and (12).$$text{CIoU_Loss} =1-text{CIoU}=1-text{IoU}+frac{{rho}^{2}left(text{b,}{text{b}}^{text{gt}}right)}{{text{c}}^{2}}{+ alpha v }$$
(13)
Optimization algorithmAfter optimizing the loss function of the network model, the next step is to optimize the hyperparameters of the network model. The function of the optimizer is to adjust the hyperparameters to the most appropriate values while making the loss function converge as much as possible32. In the target detection algorithm, the optimizer is mainly used to calculate the gradient of the loss function and to iteratively update the parameters.The optimizer used in YOLOv5 is stochastic gradient descent (SGD). Since a large number of problems in deep learning satisfy the strict saddle function, all the local optimal solutions obtained are almost as ideal. Therefore, SGD algorithm is not trapped in the saddle point and has strong generality. However, the slow convergence speed and the number of iterations of SGD algorithm are still problems that need to be improved. Adam algorithm has both the first-order momentum in the SGD algorithm and combines the second-order momentum in AdaGrad algorithm and AdaDelta algorithm, Adaptive&Momentum. Adam formula can be described as follows:$${m}_{t}={beta }_{1}{m}_{t-1}+left(1-{beta }_{1}right){g}_{t}$$
(14)
$${v}_{t}={beta }_{2}{v}_{t-1}+left(1-{beta }_{2}right){g}_{t}^{2}$$
(15)
$${widehat{m}}_{t}=frac{{m}_{t}}{1-{beta }_{1}^{t}}$$
(16)
$${widehat{v}}_{t}=frac{{v}_{t}}{1-{beta }_{2}^{t}}$$
(17)
where ({beta }_{1}) and ({beta }_{2}) parameters are hyperparameters and g is the current gradient value of the error function, ({m}_{t}) is the gradient of the first-order momentum and ({v}_{t}) is the gradient of the second-order momentum.Adam is an adaptive one-step random objective function optimization algorithm based on a low-order moment. It can replace the traditional first-order optimization algorithm for the stochastic gradient descent process. It is able to update the weights of the neural network adaptively based on the data trained during the iterative process. The Adam optimizer occupies fewer memory resources during the training process and is suitable for solving the problems of sparse gradients and large fluctuations in loss values33. Therefore, we use the Adam optimization algorithm instead of the SGD optimization algorithm to train the network model based on the YOLOv5s network model. The calculation is shown in Table 3.Table 3 Computing method of the Adam optimizer.Full size tablewhere ({alpha}) is a factor controlling the learning rate of the network, ({beta}^{{prime}}) is the exponential decay rate of the first-order moment estimate, ({beta}^{{primeprime}}) is the exponential decay rate of the second-order moment estimate, and ({varepsilon}) is a constant that tends to zero infinitely as the denominator. More

Luiselli, L., Akani, G. & Capizzi, D. Food resource partitioning of a community of snakes in a swamp rainforest of south-eastern Nigeria. J. Zool. 246(2), 125–133. https://doi.org/10.1111/j.1469-7998.1998.tb00141.x (1998).Article 

Google Scholar 
Rouag, R., Djilali, H., Gueraiche, H. & Luiselli, L. Resource partitioning patterns between two sympatric lizard species from Algeria. J. Arid Environ. 69, 158–168. https://doi.org/10.1016/j.jaridenv.2006.08.008 (2007).ADS 
Article 

Google Scholar 
Bergeron, R. & Blouin-Demers, G. Niche partitioning between two sympatric lizards in the Chiricahua Mountains of Arizona. Copeia 108(3), 570–577. https://doi.org/10.1643/CH-19-268 (2020).Article 

Google Scholar 
Lucek, K., Butlin, R. K. & Patsiou, T. Secondary contact zones of closely-related Erebia butterflies overlap with narrow phenotypic and parasitic clines. J. Evol. Biol. 33(9), 1152–1163. https://doi.org/10.1111/jeb.13669 (2020).CAS 
Article 
PubMed 

Google Scholar 
Freeman, B. G. Competitive interaction upon secondary contact drive elevational divergence in tropical birds. Am. Nat. 186(4), 470–479. https://doi.org/10.5061/dryad.6qg3g (2015).Article 
PubMed 

Google Scholar 
Schoener, T. W. Resource partitioning in ecological communities. Science 185(4145), 27–39 (1974).ADS 
CAS 
Article 

Google Scholar 
Rivas, L. R. A Reinterpretation of the concepts “sympatric” and “allopatric” with proposal of the additional terms “syntopic” and “allotopic”. Syst. Zool. 13(1), 42 (1964).Article 

Google Scholar 
Macarthur, R. & Levins, R. The limiting similarity, convergence, and divergence of coexisting species. Am. Nat. 101(921), 377–385 (1967).Article 

Google Scholar 
Dayan, T. & Simberloff, D. Ecological and community-wide character displacement: The next generation. Ecol. Lett. 8(8), 875–894. https://doi.org/10.1111/j.1461-0248.2005.00791.x (2005).Article 

Google Scholar 
Holomuzki, J. R., Feminella, J. W. & Power, M. E. Biotic interactions in freshwater benthic habitats. J. N. Am. Benthol. Soc. 29(1), 220–244. https://doi.org/10.1899/08-044.1 (2010).Article 

Google Scholar 
Ferretti, F. et al. Competition between wild herbivores: Reintroduced red deer and Apennine chamois. Behav. Ecol. 26(2), 550–559. https://doi.org/10.1093/beheco/aru226 (2015).Article 

Google Scholar 
Takada, H., Yano, R., Katsumata, A., Takatsuki, S. & Minami, M. Diet compositions of two sympatric ungulates, the Japanese serow (Capricornis crispus) and the sika deer (Cervus nippon), in a montane forest and an alpine grassland of Mt. Asama central, Japan. Mamm. Biol. 101, 681–694. https://doi.org/10.1007/s42991-021-00122-5 (2021).Article 

Google Scholar 
Hubbel, S. P. The Unified Neutral Theory of Biodiversity and Biogeography (Princeton University Press, 2001) (ISBN 9780691021287).
Google Scholar 
Bell, G. Neutral macroecology. Science 293, 2413–2418. https://doi.org/10.1126/science.293.5539.2413 (2001).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Rosindell, J., Hubbel, S. P. & Etienne, R. S. The unified neutral theory of biodiversity and biogeography at age ten. Trends Ecol. Evol. 26(7), 340–348. https://doi.org/10.1016/j.tree.2011.03.024 (2011).Article 
PubMed 

Google Scholar 
Cowie, R. H. & Holland, B. S. Dispersal is fundamental to biogeography and the evolution of biodiversity on oceanic islands. J. Biogeogr. 33, 193–198. https://doi.org/10.1111/j.1365-2699.2005.01383.x (2006).Article 

Google Scholar 
Amarasekare, P. & Nisbet, R. M. Spatial heterogeneity, source-sink dynamics, and the local coexistence of competing species. Am. Nat. 158(6), 572–584. https://doi.org/10.1086/323586 (2001).CAS 
Article 
PubMed 

Google Scholar 
Kumar, K., Gentile, G. & Grant, T. D. Conolophus subcristatus. The IUCN Red List of Threatened Species 2020, e.T5240A3014082 (2020). https://doi.org/10.2305/IUCN.UK.2020-2.RLTS.T5240A3014082.enGentile, G. Conolophus marthae. The IUCN Red List of Threatened Species 2012, e. T174472A1414375 (2012). https://doi.org/10.2305/IUCN.UK.2012-1.RLTS.T174472A1414375.enGentile, G., Marquez, C., Snell, H. L., Tapia, W. & Izurieta, A. Conservation of a New Flagship Species: The Galápagos Pink Land Iguana (Conolophus marthae Gentile and Snell, 2009). In Problematic Wildlife: A Cross-Disciplinary Approach (ed. Angelici, F. M.) 315–336 (Springer International Publishing, 2016). https://doi.org/10.1007/978-3-319-22246-2_15.Chapter 

Google Scholar 
Gentile, G. & Snell, H. L. Conolophus marthae sp. Nov. (Squamata, iguanidae), a new species of land iguana from the Galápagos Archipelago. Zootaxa 2201, 1–10 (2009).Article 

Google Scholar 
Colosimo, G. et al. Chemical signatures of femoral pore secretions in two syntopic but reproductively isolated species of Galápagos land iguanas (Conolophus marthae and C. subcristatus). Sci. Rep. 10(1), 14314. https://doi.org/10.1038/s41598-020-71176-7 (2020).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Jackson, M. Galápagos: A Natural History, Revised and Expanded (University of Calgary Press, 1994).
Google Scholar 
Traveset, A. et al. Galápagos land iguana (Conolophus subcristatus) as a seed disperser. Integr. Zool. 11(3), 207–213. https://doi.org/10.1111/1749-4877.12187 (2016).Article 
PubMed 

Google Scholar 
Di Giambattista, L. et al. Molecular data exclude current hybridization between iguanas Conolophus marthae and C. subcristatus on Wolf volcano (Galápagos islands). Conserv. Genet. 19(6), 1461–1469. https://doi.org/10.1007/s10592-018-1114-3 (2018).Article 

Google Scholar 
MacLeod, A. et al. Hybridization masks speciation in the evolutionary history of the Galápagos marine iguana. Proc. R. Soc. B 282, 1–9. https://doi.org/10.1098/rspb.2015.0425 (2015).Article 

Google Scholar 
Gause, G. F. The Struggle for Existence (Williams and Wilkins Company, 1934).Book 

Google Scholar 
Hardin, G. The competitive exclusion principle. Science 131(3409), 1292–1297 (1960).ADS 
CAS 
Article 

Google Scholar 
Ashrafi, S., Beck, A., Rutishauser, M., Arlettaz, R. & Bontadina, F. Trophic niche partitioning of cryptic species of long-eared bats in Switzerland: Implications for conservation. Eur. J. Wildl. Res. 57, 843–849. https://doi.org/10.1007/s10344-011-0496-z (2011).Article 

Google Scholar 
Bleyhl, B. et al. Assessing niche overlap between domestic and threatened wild sheep to identify conservation priority areas. Divers. Distrib. 25(1), 129–141. https://doi.org/10.1111/ddi.12839 (2019).Article 

Google Scholar 
Newsome, S. D., del Rio, C. M., Bearhop, S. & Phillips, D. L. A niche for isotopic ecology. Front. Ecol. Environ. 5(8), 429–436. https://doi.org/10.1890/060150.1 (2007).Article 

Google Scholar 
Riera, P., Stal, L. J. & Nieuwenhuize, J. δ13C versus δ15N of co-occurring mollusks within a community dominated by Crassostrea gigas and Crepidula ornicate (Oossterschelde, The Netherlands). Mar. Ecol. Prog. Ser. 240, 291–295 (2002).ADS 
Article 

Google Scholar 
Page, B., McKenzie, J. & Goldsworthy, S. D. Dietary resources partitioning among sympatric New Zealand and Australian fur seals. Mar. Ecol. Prog. Ser. 293, 283–302 (2005).ADS 
Article 

Google Scholar 
DeNiro, M. J. & Epstein, S. Influence of diet on the distribution of carbon isotopes in animals. Geochim. Cosmochim. Acta 42(5), 495–506 (1978).ADS 
CAS 
Article 

Google Scholar 
DeNiro, M. J. & Epstein, S. Influence of diet on the distribution of nitrogen isotopes in animals. Geochim. Cosmochim. Acta 45(3), 341–351 (1981).ADS 
CAS 
Article 

Google Scholar 
Post, D. M. Using stable isotopes to estimate trophic position: Models, methods, and assumptions. Ecology 83(3), 703–718. https://doi.org/10.1890/0012-9658(2002)083[0703:USITET]2.0.CO;2 (2002).Article 

Google Scholar 
Crawford, K., McDonald, R. A. & Bearhop, S. Applications of stable isotope techniques to the ecology of mammals. Mammal. Rev. 38(1), 87–107. https://doi.org/10.1111/j.1365-2907.2008.00120.x (2008).Article 

Google Scholar 
Trueman, M. & d’Ozouville, N. Characterizing the Galápagos terrestrial climate in the face of global climate change. Gala Res. 67, 26–37 (2010).
Google Scholar 
Paltán, H. A. et al. Climate and sea surface trends in the Galápagos Islands. Sci. Rep. 11(1), 1–13. https://doi.org/10.1038/s41598-021-93870-w (2021).CAS 
Article 

Google Scholar 
Rivas-Torres, G. F., Benítez, F. L., Rueda, D., Sevilla, C. & Mena, C. F. A methodology for mapping native and invasive vegetation coverage in archipelagos: An example from the Galápagos islands. Prog. Phys. Geogr. 42(1), 83–111. https://doi.org/10.1177/0309133317752278 (2018).Article 

Google Scholar 
Gentile, G., Ciambotta, M. & Tapia, W. Illegal wildlife trade in Galápagos: Molecular tools help taxonomic identification and guide rapid repatriation of confiscated iguanas. Conserv. Genet. Resour. 5, 867–872. https://doi.org/10.1007/s12686-013-9915-7 (2013).Article 

Google Scholar 
Stephens, R. B., Ouimette, A. P., Hobbie, E. A. & Rowe, R. J. Re-evaluating trophic discrimination factors (Δδ13C and Δδ15N) for diet reconstruction. Ecol. Mono 92, e1525. https://doi.org/10.1002/ecm.1525 (2022).CAS 
Article 

Google Scholar 
Hobson, K. A. & Clark, R. G. Assessing avian diets using stable isotopes I: Turnover of 13C in tissues. The Condor 94(1), 181–188. https://doi.org/10.2307/1368807 (1992).Article 

Google Scholar 
Li, C.-H., Roth, J. D. & Detwiler, J. T. Isotopic turnover rates and diet-tissue discrimination depend on feeding habits of freshwater snails. PLoS ONE 13(7), e0199713. https://doi.org/10.1371/journal.pone.0199713 (2018).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Steinitz, R., Lemm, J., Pasachnik, S. & Kurle, C. Diet-tissue stable isotope (δ13C and δ15N) discrimination factors for multiple tissues from terrestrial reptiles. Rapid Commun. Mass Spectrom. 30(1), 9–21. https://doi.org/10.1002/rcm.7410 (2016).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Ethier, D. M., Kyle, C. J., Kyser, T. K. & Nocera, J. J. Variability in the growth patterns of the cornified claw sheath among vertebrates: Implications for using biogeochemistry to study animal movement. Can. J. Zool. 88(11), 1043–1051. https://doi.org/10.1139/Z10-073 (2010).Article 

Google Scholar 
Aresco, M. J. & James, F. C. Ecological relationships of turtles in northern Florida lakes: A study of omnivory and the structure of a lake food web. Florida Fish and Wildlife Conservation Commission (2005). https://www.semanticscholar.org/paper/ECOLOGICAL-RELATIONSHIPS-OF-TURTLES-IN-NORTHERN-A-A-Aresco-James/f6d59265eb6494aa19cfde7d2d80bb165e6432acLourenço, P. M., Granadeiro, J. P., Guilherme, J. L. & Catry, T. Turnover rates of stable isotopes in avian blood and toenails: Implications for dietary and migration studies. J. Exp. Mar. Biol. Ecol. 472, 89–96. https://doi.org/10.1016/j.jembe.2015.07.006 (2015).CAS 
Article 

Google Scholar 
Jackson, A. L., Inger, R., Parnell, A. C. & Bearhop, S. Comparing isotopic niche widths among and within communities: SIBER—Stable isotope Bayesian ellipses in r. J. Animal Ecol. 80(3), 595–602. https://doi.org/10.1111/j.1365-2656.2011.01806.x (2011).Article 

Google Scholar 
Wikelski, M. & Romero, L. M. Body size, performance and fitness in Galápagos marine iguanas. Integr Comp Biol 43(3), 376–386. https://doi.org/10.1093/icb/43.3.376 (2003).Article 
PubMed 

Google Scholar 
Iverson, J., Smith, G. & Pieper, L. Factors Affecting Long-Term Growth of the Allen Cays Rock Iguana in the Bahamas. In Iguanas: Biology and Conservation (eds Alberts, A. et al.) 176–192 (University of California Press, 2004). https://doi.org/10.1525/9780520930117-018.Chapter 

Google Scholar 
Smith, G. R. & Iverson, J. B. Effects of tourism on body size, growth, condition, and demography in the Allen Cay Iguana. Herpetol. Conserv. Biol. 11, 214–221 (2016).
Google Scholar 
Wikelski, M., Carrillo, V. & Trillmich, F. Energy limits to body size in a grazing reptile, the Galápagos Marine Iguana. Ecology 78(7), 2204–2217. https://doi.org/10.2307/2265956 (1997).Article 

Google Scholar 
Bulakhova, N. A. et al. Inter-observer and intra-observer differences in measuring body length: A test in the common lizard, Zootoca vivipara. Amphibia-Reptilia 32(4), 477–484. https://doi.org/10.1163/156853811X601636 (2011).Article 

Google Scholar 
R Development Core Team. R: A language and environment for statistical computing (2021). https://cran.r-project.orgGoslee, S. C. & Urban, D. L. The ecodist package for dissimilarity-based analysis of ecological data. J. Stat. Softw. 22(7), 1–19. https://doi.org/10.18637/jss.v022.i07 (2007).Article 

Google Scholar 
Randin, C. F., Jaccard, H., Vittoz, P., Yoccoz, N. G. & Guisan, A. Land use improves spatial predictions of mountain plant abundance but not presence–absence. J. Veg. Sci. 20, 996–1008. https://doi.org/10.1111/j.1654-1103.2009.01098.x (2009).Article 

Google Scholar 
Broennimann, O., Di Cola, V. & Guisan, A. ecospat: Spatial Ecology Miscellaneous Methods. R package version 3.2.1 (2022) https://CRAN.R-project.org/package=ecospatBorcard, D., Legendre, P. & Drapeau, P. Partialling out the spatial component of ecological variation. Ecology 73(3), 1045–1055. https://doi.org/10.2307/1940179 (1992).Article 

Google Scholar 
Wood, S. N. Generalized Additive Models: An Introduction with R 2nd edn. (Chapman and Hall/CRC, 2017). https://doi.org/10.1201/9781315370279.Book 
MATH 

Google Scholar 
Van Marken Lichtenbelt, W. D. Optimal foraging of a herbivorous lizard, the green iguana in a seasonal environment. Oecologia 95, 246–256. https://doi.org/10.1007/BF00323497 (1993).ADS 
Article 
PubMed 

Google Scholar 
Pasachnik, S. A. & Martin-Velez, V. An evaluation of the diet of Cyclura iguanas in the Dominican Republic. Herpetol. Bull. 140, 6–12 (2017).
Google Scholar 
Cerling, T. E. et al. Global vegetation change through the Miocene/Pliocene boundary. Nature 389(6647), 153–158. https://doi.org/10.1038/38229 (1997).ADS 
CAS 
Article 

Google Scholar 
O’Leary, M. H. Carbon isotopes in photosynthesis. Bioscience 38(5), 328–336. https://doi.org/10.2307/1310735 (1988).Article 

Google Scholar 
Snell, H. L. & Tracy, C. R. Behavioral and morphological adaptations by Galapagos land iguanas (Conolophus subcristatus) to water and energy requirements of eggs and neonates. Am. Zool. 25(4), 1009–1018. https://doi.org/10.1093/icb/25.4.1009 (1985).Article 

Google Scholar 
Christian, K., Tracy, C. R. & Porter, W. P. Diet, digestion, and food preferences of Galápagos land iguanas. Herpetologica 40(2), 205–212 (1984).
Google Scholar 
Mallona, I., Egea-Cortines, M. & Weiss, J. Conserved and divergent rhythms of crassulacean acid metabolism-related and core clock gene expression in the cactus Opuntia ficus-indica. Plant Physiol. 156, 1978–1989. https://doi.org/10.1104/pp.111.179275 (2011).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
San Sebastián, O., Navarro, J., Llorente, G. A. & Richter-Boix, Á. Trophic strategies of a non-native and a native amphibian species in shared ponds. PLoS ONE 10(6), 1–17. https://doi.org/10.1371/journal.pone.0130549 (2015).CAS 
Article 

Google Scholar 
Perga, M. E. & Grey, J. Laboratory measures of isotope discrimination factors: Comments on Caut, Angulo & Courchamp (2008, 2009). J. Appl. Ecol. 47(4), 942–947. https://doi.org/10.1111/j.1365-2664.2009.01730.x (2010).CAS 
Article 

Google Scholar 
Freeman, B. Sexual niche partitioning in two species of new Guinean Pachycephala whistlers. J. Field Ornithol. 85(1), 23–30. https://doi.org/10.1111/jofo.12046 (2014).Article 

Google Scholar 
Werner, D. I. Social Organization and Ecology of Land Iguanas, Conolophus subcristatus, on Isla Fernandina, Galápagos. In Iguanas of the World: Their Behavior, Ecology, and Conservation (eds Burghardt, G. M. & Rand, A. S.) 342–365 (Noyes Publications, 1982).
Google Scholar 
Doi, H., Akamatsu, F. & González, A. L. Starvation effects on nitrogen and carbon stable isotopes of animals: An insight from meta-analysis of fasting experiments. R. Soc. Open Sci. 4(8), 170633. https://doi.org/10.1098/rsos.170633 (2017).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Persaud, A., Dillon, P., Molot, L. & Hargan, K. Relationships between body size and trophic position of consumers in temperate freshwater lakes. Aquat. Sci. 74(1), 203–212. https://doi.org/10.1007/s00027-011-0212-9 (2012).Article 

Google Scholar 
Keppeler, F. W. et al. Body size, trophic position, and the coupling of different energy pathways across a saltmarsh landscape. Limnol. Oceanogr. Lett. 6(6), 360–368. https://doi.org/10.1002/lol2.10212 (2021).Article 

Google Scholar 
Hanson, J. O. et al. Feeding across the food web: The interaction between diet, movement and body size in estuarine crocodiles (Crocodylus porosus). Austral. Ecol. 40(3), 275–286. https://doi.org/10.1111/aec.12212 (2015).Article 

Google Scholar 
Gustavino, B., Terrinoni, S., Paglierani, C. & Gentile, G. Conolophus marthae vs. Conolophus subcristatus: Does the skin pigmentation pattern exert a protective role against DNA damaging effect induced by UV light exposure? Analysis of blood smears through the micronucleus test. Paper presented at the Galápagos Land and Marine Iguanas Workshop, IUCN SSC Iguana Specialist Group Meeting, Puerto Ayora, 28–29 October 2014.Di Giacomo, C. et al. 25–Hydroxivitamin D plasma levels in natural populations of pigmented and partially pigmented land iguanas from Galápagos (Conolophus spp.). Hind 2022, 1–9. https://doi.org/10.1155/2022/7741397 (2022).CAS 
Article 

Google Scholar 
Percie du Sert, N. et al. Reporting animal research: Explanation and elaboration for the ARRIVE guidelines 2.0. PLoS Biol. 18(7), e3000411. https://doi.org/10.1371/journal.pbio.3000411 (2020).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar  More

Song, X.-P. et al. Global land change from 1982 to 2016. Nature 560, 639–643 (2018).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Houghton, R. A. & Nassikas, A. A. Global and regional fluxes of carbon from land use and land cover change 1850–2015. Glob. Biogeochem. Cycles 31, 456–472 (2017).ADS 
CAS 
Article 

Google Scholar 
Phelps, L. N. & Kaplan, J. O. Land use for animal production in global change studies: Defining and characterizing a framework. Glob. Change Biol. 23, 4457–4471 (2017).ADS 
Article 

Google Scholar 
Curtis, P. G., Slay, C. M., Harris, N. L., Tyukavina, A. & Hansen, M. C. Classifying drivers of global forest loss. Science 361, 1108–1111 (2018).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Hong, C. et al. Global and regional drivers of land-use emissions in 1961–2017. Nature 589, 554–561 (2021).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Knorr, W., Prentice, I. C., House, J. & Holland, E. Long-term sensitivity of soil carbon turnover to warming. Nature 433, 298–301 (2005).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Shi, Z. et al. The age distribution of global soil carbon inferred from radiocarbon measurements. Nat. Geosci. 13, 555–559 (2020).ADS 
CAS 
Article 

Google Scholar 
Sanderman, J., Hengl, T. & Fiske, G. J. Soil carbon debt of 12,000 years of human land use. Proc. Natl. Acad. Sci. 114, 9575–9580 (2017).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Lal, R. Soil carbon sequestration impacts on global climate change and food security. Science 304, 1623–1627 (2004).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Friedlingstein, P. et al. Global carbon budget 2020. Earth Syst. Sci. Data 12, 3269–3340 (2020).ADS 
Article 

Google Scholar 
Yue, C., Ciais, P., Houghton, R. A. & Nassikas, A. A. Contribution of land use to the interannual variability of the land carbon cycle. Nat. Commun. 11, 1–11 (2020).Article 

Google Scholar 
Zomer, R. J. et al. Global tree cover and biomass carbon on agricultural land: The contribution of agroforestry to global and national carbon budgets. Sci. Rep. 6, 1–12 (2016).Article 

Google Scholar 
De Stefano, A. & Jacobson, M. G. Soil carbon sequestration in agroforestry systems: a meta-analysis. Agrofor. Syst. 92, 285–299 (2018).
Google Scholar 
Bossio, D. et al. The role of soil carbon in natural climate solutions. Nat. Sustain. 3, 391–398 (2020).Article 

Google Scholar 
England, J. R., O’Grady, A. P., Fleming, A., Marais, Z. & Mendham, D. Trees on farms to support natural capital: An evidence-based review for grazed dairy systems. Sci. Total Environ. 704, 135345 (2020).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Ma, Z., Chen, H. Y., Bork, E. W., Carlyle, C. N. & Chang, S. X. Carbon accumulation in agroforestry systems is affected by tree species diversity, age and regional climate: A global meta-analysis. Glob. Ecol. Biogeogr. 29, 1817–1828 (2020).Article 

Google Scholar 
FAOSTAT. Data/Inputs/land use. In: Food Agriculture Organization. http://www.fao.org/faostat/en/#data/RL. (2020). Accessed 12 Sept 2020.Shukla, P. R. et al. Climate Change and Land: an IPCC special report on climate change, desertification, land degradation, sustainable land management, food security, and greenhouse gas fluxes in terrestrial ecosystems. (Intergovernmental Panel on Climate Change, 2019).Galdino, S. et al. Large-scale modeling of soil erosion with RUSLE for conservationist planning of degraded cultivated Brazilian pastures. Land Degrad. Dev. 27, 773–784 (2016).Article 

Google Scholar 
Stanimirova, R. et al. Sensitivity of global pasturelands to climate variation. Earth’s Future 7, 1353–1366 (2019).ADS 
Article 

Google Scholar 
Tolimir, M. et al. The conversion of forestland into agricultural land without appropriate measures to conserve SOM leads to the degradation of physical and rheological soil properties. Sci. Rep. 10, 1–12 (2020).Article 

Google Scholar 
Mendoza-Ponce, A., Corona-Núñez, R., Kraxner, F., Leduc, S. & Patrizio, P. Identifying effects of land use cover changes and climate change on terrestrial ecosystems and carbon stocks in Mexico. Glob. Environ. Change. 53, 12–23 (2018).Article 

Google Scholar 
Castillo-Santiago, M., Hellier, A., Tipper, R. & De Jong, B. Carbon emissions from land-use change: An analysis of causal factors in Chiapas, Mexico. Mitig. Adapt. Strat. Glob. Change 12, 1213–1235 (2007).Article 

Google Scholar 
Kolb, M. & Galicia, L. Scenarios and story lines: drivers of land use change in southern Mexico. Environ. Dev. Sustain. 20, 681–702 (2018).Article 

Google Scholar 
Aryal, D. R. et al. Biomass accumulation in forests with high pressure of fuelwood extraction in Chiapas, Mexico. Revista Árvore 42, e420307 (2018).Article 

Google Scholar 
Aryal, D. R. et al. Soil organic carbon depletion from forests to grasslands conversion in Mexico: A review. Agriculture 8, 181 (2018).CAS 
Article 

Google Scholar 
Griscom, B. W. et al. Natural climate solutions. Proc. Natl. Acad. Sci. 114, 11645–11650 (2017).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Chapman, M. et al. Large climate mitigation potential from adding trees to agricultural lands. Glob. Change Biol. 26, 4357–4365 (2020).ADS 
Article 

Google Scholar 
Hayek, M. N., Harwatt, H., Ripple, W. J. & Mueller, N. D. The carbon opportunity cost of animal-sourced food production on land. Nat. Sustain. 4, 21–24 (2021).Article 

Google Scholar 
Kothandaraman, S., Dar, J. A., Sundarapandian, S., Dayanandan, S. & Khan, M. L. Ecosystem-level carbon storage and its links to diversity, structural and environmental drivers in tropical forests of Western Ghats, India. Sci. Rep. 10, 1–15 (2020).Article 

Google Scholar 
Havlík, P. et al. Climate change mitigation through livestock system transitions. Proc. Natl. Acad. Sci. 111, 3709–3714 (2014).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Resende, L. O. et al. Silvopastoral management of beef cattle production for neutralizing the environmental impact of enteric methane emission. Agroforestry Syst. 94, 893–903 (2020).Article 

Google Scholar 
Sans, G. H. C., Verón, S. R. & Paruelo, J. M. Forest strips increase connectivity and modify forests’ functioning in a deforestation hotspot. J. Environ. Manage. 290, 112606 (2021).Article 

Google Scholar 
Searchinger, T. D., Wirsenius, S., Beringer, T. & Dumas, P. Assessing the efficiency of changes in land use for mitigating climate change. Nature 564, 249–253 (2018).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Lawson, G., Dupraz, C. & Watté, J. Can silvoarable systems maintain yield, resilience, and diversity in the face of changing environments? in Agroecosystem Diversity 145–168 (Elsevier, 2019).Ramakrishnan, S. et al. Silvopastoral system for resilience of key soil health indicators in semi-arid environment. Arch. Agron. Soil Sci. 67, 1834–1847 (2021).CAS 
Article 

Google Scholar 
Gerber, P. J. et al. Tackling Climate Change Through Livestock: A Global Assessment of Emissions and Mitigation Opportunities (Food and Agriculture Organization of the United Nations (FAO), 2013).
Google Scholar 
Haberl, H. Method précis: Human appropriation of net primary production (HANPP). In Social Ecology. Society-Nature Relations across Time and Space (eds Haberl, H. et al.) 332–334 (Springer Nature, 2016).
Google Scholar 
Smith, P. et al. Global change pressures on soils from land use and management. Glob. Change Biol. 22, 1008–1028 (2016).ADS 
Article 

Google Scholar 
Herrero, M. et al. Greenhouse gas mitigation potentials in the livestock sector. Nat. Clim. Change. 6, 452–461 (2016).ADS 
Article 

Google Scholar 
Lorenz, K. & Lal, R. Soil organic carbon sequestration in agroforestry systems. A review. Agron. Sustain. Develop. 34, 443–454 (2014).CAS 
Article 

Google Scholar 
Michalk, D. L. et al. Sustainability and future food security—A global perspective for livestock production. Land Degrad. Dev. 30, 561–573 (2019).Article 

Google Scholar 
Bardgett, R. D. et al. Combatting global grassland degradation. Nat. Rev. Earth Environ. 2, 720–735 (2021).ADS 
Article 

Google Scholar 
Pinheiro, F. M., Nair, P. R., Nair, V. D., Tonucci, R. G. & Venturin, R. P. Soil carbon stock and stability under Eucalyptus-based silvopasture and other land-use systems in the Cerrado biodiversity hotspot. J. Environ. Manage. 299, 113676 (2021).CAS 
PubMed 
Article 

Google Scholar 
Jose, S., Walter, D. & Kumar, B. M. Ecological considerations in sustainable silvopasture design and management. Agrofor. Syst. 93, 317–331 (2019).Article 

Google Scholar 
Oldfield, E. E. et al. Crediting agricultural soil carbon sequestration. Science 375, 1222–1225 (2022).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Udawatta, R. P., Walter, D. & Jose, S. Carbon sequestration by forests and agroforests: A reality check for the United States. Carbon Footprints 1, 8 (2022).Article 

Google Scholar 
Adame-Castro, D. E. et al. Diurnal and seasonal variations on soil CO2 fluxes in tropical silvopastoral systems. Soil Use Manag. 36, 671–681 (2020).Article 

Google Scholar 
Contosta, A. R., Asbjornsen, H., Orefice, J., Perry, A. & Smith, R. G. Climate consequences of temperate forest conversion to open pasture or silvopasture. Agric. Ecosyst. Environ. 333, 107972 (2022).CAS 
Article 

Google Scholar 
Vargas-Zeppetello, L. R. et al. Consistent cooling benefits of silvopasture in the tropics. Nat. Commun. 13, 1–9 (2022).
Google Scholar 
Casanova-Lugo, F. et al. Effect of tree shade on the yield of Brachiaria brizantha grass in tropical livestock production systems in Mexico. Rangel. Ecol. Manage. 80, 31–38 (2022).Article 

Google Scholar 
Valenzuela Que, F. G. et al. Silvopastoral systems improve carbon stocks at livestock ranches in Tabasco, Mexico. Soil Use Manag. 38, 1237–1249 (2022).Article 

Google Scholar 
Nair, P. R. Classification of agroforestry systems. Agrofor. Syst. 3, 97–128 (1985).Article 

Google Scholar 
Somarriba, E., Kass, D. & Ibrahim, M. Definition and classification of agroforestry systems. Agroforestry Prototypes for Belize. Agroforestry Project. CATIE (Tropical Agricultural Research and Higher Education Center), Costa rica 3 (1998).Schroth, G. et al. Agroforestry and Biodiversity Conservation in Tropical Landscapes (Island Press, 2004).
Google Scholar 
Harvey, C. A. et al. Patterns of animal diversity in different forms of tree cover in agricultural landscapes. Ecol. Appl. 16, 1986–1999 (2006).PubMed 
Article 

Google Scholar 
Cardinael, R., Mao, Z., Chenu, C. & Hinsinger, P. Belowground functioning of agroforestry systems: Recent advances and perspectives. Plant Soil. 1–13 (2020).Ibrahim, M. & Beer, J. Agroforestry Prototypes for Belize Vol. 28 (CATIE, 1998).
Google Scholar 
Ibrahim, M., Villanueva, C., Casasola, F. & Rojas, J. Sistemas silvopastoriles como una herramienta para el mejoramiento de la productividad y restauración de la integridad ecológica de paisajes ganaderos. Pastos y Forrajes 29, 383–419 (2006).
Google Scholar 
Phalan, B., Onial, M., Balmford, A. & Green, R. E. Reconciling food production and biodiversity conservation: Land sharing and land sparing compared. Science 333, 1289–1291 (2011).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Van Zanten, H. H. et al. Defining a land boundary for sustainable livestock consumption. Glob. Change Biol. 24, 4185–4194 (2018).ADS 
Article 

Google Scholar 
Torres, C. M. M. E. et al. Greenhouse gas emissions and carbon sequestration by agroforestry systems in southeastern Brazil. Sci. Rep. 7, 1–7 (2017).Article 

Google Scholar 
Haile, S. G., Nair, V. D. & Nair, P. R. Contribution of trees to carbon storage in soils of silvopastoral systems in Florida, USA. Glob. Change Biol. 16, 427–438 (2010).ADS 
Article 

Google Scholar 
Chatterjee, N., Nair, P. R., Chakraborty, S. & Nair, V. D. Changes in soil carbon stocks across the Forest-Agroforest-Agriculture/Pasture continuum in various agroecological regions: A meta-analysis. Agric. Ecosyst. Environ. 266, 55–67 (2018).Article 

Google Scholar 
Aynekulu, E. et al. Carbon storage potential of silvopastoral systems of Colombia. Land 9, 309 (2020).Article 

Google Scholar 
Birkhofer, K. et al. Land-use type and intensity differentially filter traits in above-and below-ground arthropod communities. J. Anim. Ecol. 86, 511–520 (2017).PubMed 
Article 

Google Scholar 
Dahlsjö, C. A. et al. The local impact of macrofauna and land-use intensity on soil nutrient concentration and exchangeability in lowland tropical Peru. Biotropica 52, 242–251 (2020).Article 

Google Scholar 
Vizcaíno-Bravo, Q., Williams-Linera, G. & Asbjornsen, H. Biodiversity and carbon storage are correlated along a land use intensity gradient in a tropical montane forest watershed, Mexico. Basic Appl. Ecol. 44, 24–34 (2020).Article 

Google Scholar 
Villanueva-López, G., Martínez-Zurimendi, P., Ramírez-Avilés, L., Aryal, D. R. & Casanova-Lugo, F. Live fences reduce the diurnal and seasonal fluctuations of soil CO 2 emissions in livestock systems. Agron. Sustain. Dev. 36, 23 (2016).Article 

Google Scholar 
López-Santiago, J. G. et al. Carbon storage in a silvopastoral system compared to that in a deciduous dry forest in Michoacán, Mexico. Agroforestry Syst. 93, 199–211 (2019).Article 

Google Scholar 
Aryal, D. R., Gómez-González, R. R., Hernández-Nuriasmú, R. & Morales-Ruiz, D. E. Carbon stocks and tree diversity in scattered tree silvopastoral systems in Chiapas, Mexico. Agroforestry Syst. 93, 213–227 (2019).Article 

Google Scholar 
Beckert, M. R., Smith, P., Lilly, A. & Chapman, S. J. Soil and tree biomass carbon sequestration potential of silvopastoral and woodland-pasture systems in North East Scotland. Agrofor. Syst. 90, 371–383 (2016).Article 

Google Scholar 
Cárdenas, A., Moliner, A., Hontoria, C. & Ibrahim, M. Ecological structure and carbon storage in traditional silvopastoral systems in Nicaragua. Agrofor. Syst. 93, 229–239 (2019).Article 

Google Scholar 
Lehmann, J. et al. Persistence of soil organic carbon caused by functional complexity. Nat. Geosci. 13, 529–534 (2020).ADS 
CAS 
Article 

Google Scholar 
Amézquita, M. C., Ibrahim, M., Llanderal, T., Buurman, P. & Amézquita, E. Carbon sequestration in pastures, silvo-pastoral systems and forests in four regions of the Latin American tropics. J. Sustain. For. 21, 31–49 (2004).Article 

Google Scholar 
Rosenstock, T. S. et al. Making trees count: Measurement and reporting of agroforestry in UNFCCC national communications of non-Annex I countries. Agric. Ecosyst. Environ. 284, 106569 (2019).Article 

Google Scholar 
Junior, M. A. L., Fracetto, F. J. C., da Silva Ferreira, J., Silva, M. B. & Fracetto, G. G. M. Legume-based silvopastoral systems drive C and N soil stocks in a subhumid tropical environment. CATENA 189, 104508 (2020).Article 

Google Scholar 
Villanueva-Partida, C. et al. Influence of the density of scattered trees in pastures on the structure and species composition of tree and grass cover in southern Tabasco, Mexico. Agric. Ecosyst. Environ. 232, 1–8 (2016).Article 

Google Scholar 
Morantes-Toloza, J. L. & Renjifo, L. M. Live fences in tropical production systems: A global review of uses and perceptions. Rev. Biol. Trop. 66, 739–753 (2018).Article 

Google Scholar 
MoralesRuiz, D. E. et al. Carbon contents and fine root production in tropical silvopastoral systems. Land Degrad. Develop. 32, 738–756 (2021).Article 

Google Scholar 
Hoosbeek, M. R., Remme, R. P. & Rusch, G. M. Trees enhance soil carbon sequestration and nutrient cycling in a silvopastoral system in south-western Nicaragua. Agrofor. Syst. 92, 263–273 (2018).
Google Scholar 
Aryal, D. R. et al. Fine wood decomposition rates decline with the sge of tropical successional forests in Southern Mexico: Implications to ecosystem carbon storage. Ecosystems 25, 661–677 (2022).CAS 
Article 

Google Scholar 
Dignac, M.-F. et al. Increasing soil carbon storage: Mechanisms, effects of agricultural practices and proxies. A review. Agron. Sustain. Develop. 37, 1–27 (2017).CAS 
Article 

Google Scholar 
Sánchez-Silva, S. et al. Fine root biomass stocks but not the production and turnover rates vary with the age of tropical successional forests in Southern Mexico. Rhizosphere 21, 100474 (2022).Article 

Google Scholar 
Montejo-Martínez, D. et al. Fine root density and vertical distribution of Leucaena leucocephala and grasses in silvopastoral systems under two harvest intervals. Agrofor. Syst. 94, 843–855 (2020).Article 

Google Scholar 
Sánchez-Silva, S., De Jong, B. H., Aryal, D. R., Huerta-Lwanga, E. & Mendoza-Vega, J. Trends in leaf traits, litter dynamics and associated nutrient cycling along a secondary successional chronosequence of semi-evergreen tropical forest in South-Eastern Mexico. J. Trop. Ecol. 34, 364–377 (2018).Article 

Google Scholar 
Waters, C. M., Orgill, S. E., Melville, G. J., Toole, I. D. & Smith, W. J. Management of grazing intensity in the semi-arid rangelands of Southern Australia: Effects on soil and biodiversity. Land Degrad. Dev. 28, 1363–1375 (2017).Article 

Google Scholar 
Baldassini, P. & Paruelo, J. M. Deforestation and current management practices reduce soil organic carbon in the semi-arid Chaco, Argentina. Agric. Syst. 178, 102749 (2020).Article 

Google Scholar 
Abdalla, M. et al. Critical review of the impacts of grazing intensity on soil organic carbon storage and other soil quality indicators in extensively managed grasslands. Agric. Ecosyst. Environ. 253, 62–81 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Lange, M. et al. Plant diversity increases soil microbial activity and soil carbon storage. Nat. Commun. 6, 1–8 (2015).ADS 
Article 

Google Scholar 
Wiesmeier, M. et al. Soil organic carbon storage as a key function of soils—A review of drivers and indicators at various scales. Geoderma 333, 149–162 (2019).ADS 
CAS 
Article 

Google Scholar 
Lim, S.-S. et al. Soil organic carbon stocks in three Canadian agroforestry systems: From surface organic to deeper mineral soils. For. Ecol. Manage. 417, 103–109 (2018).ADS 
Article 

Google Scholar 
Nair, P. Carbon sequestration studies in agroforestry systems: A reality-check. Agrofor. Syst. 86, 243–253 (2012).Article 

Google Scholar 
Montagnini, F., Ibrahim, M. & Murgueitio, E. Silvopastoral systems and climate change mitigation in Latin America. Bois et forêts des tropiques 316, 3–16 (2013).Article 

Google Scholar 
Allison, S. D., Wallenstein, M. D. & Bradford, M. A. Soil-carbon response to warming dependent on microbial physiology. Nat. Geosci. 3, 336–340 (2010).ADS 
CAS 
Article 

Google Scholar 
Sarto, M. V. et al. Soil microbial community and activity in a tropical integrated crop-livestock system. Appl. Soil. Ecol. 145, 103350 (2020).Article 

Google Scholar 
Malik, A. A. et al. Land use driven change in soil pH affects microbial carbon cycling processes. Nat. Commun. 9, 1–10 (2018).ADS 
CAS 
Article 

Google Scholar 
Bautista, F., Palacio-Aponte, G., Quintana, P. & Zinck, J. A. Spatial distribution and development of soils in tropical karst areas from the Peninsula of Yucatan, Mexico. Geomorphology 135, 308–321 (2011).ADS 
Article 

Google Scholar 
Kaiser, M. et al. The influence of mineral characteristics on organic matter content, composition, and stability of topsoils under long‐term arable and forest land use. J. Geophys. Res. Biogeosci. 117, (2012).Castillo, M. S., Tiezzi, F. & Franzluebbers, A. J. Tree species effects on understory forage productivity and microclimate in a silvopasture of the Southeastern USA. Agric. Ecosyst. Environ. 295, 106917 (2020).Article 

Google Scholar 
Yang, Y., Tilman, D., Furey, G. & Lehman, C. Soil carbon sequestration accelerated by restoration of grassland biodiversity. Nat. Commun. 10, 1–7 (2019).
Google Scholar 
Grass, I. et al. Land-sharing/-sparing connectivity landscapes for ecosystem services and biodiversity conservation. People Nat. 1, 262–272 (2019).
Google Scholar 
Orefice, J., Smith, R. G., Carroll, J., Asbjornsen, H. & Howard, T. Forage productivity and profitability in newly-established open pasture, silvopasture, and thinned forest production systems. Agrofor. Syst. 93, 51–65 (2019).Article 

Google Scholar 
Aryal, D. R. et al. Potencial de almacenamiento de carbono en áreas forestales en un sistema ganadero. Revista mexicana de ciencias forestales 9, 150–180 (2018).Article 

Google Scholar 
Gobierno de la Republica. Intended Nationally Determined Contribution, Mexico. (Instituto Nacional de Ecología y Cambio Climático, Mexico City, 2015).Bonilla-Moheno, M. & Aide, T. M. Beyond deforestation: Land cover transitions in Mexico. Agric. Syst. 178, 102734 (2020).Article 

Google Scholar 
INEGI. Mapa de uso de suelo y vegetación de México: Series I–VII. Instituto Nacional de Estadística y Geografía (INEGI), Aguascalientes, Mexico. https://www.inegi.org.mx/temas/usosuelo/#Map (2018). Accessed 17 Aug 2022.Gosling, E., Reith, E., Knoke, T. & Paul, C. A goal programming approach to evaluate agroforestry systems in Eastern Panama. J. Environ. Manage. 261, 110248 (2020).PubMed 
Article 

Google Scholar 
Bergier, I. et al. Could bovine livestock intensification in Pantanal be neutral regarding enteric methane emissions?. Sci. Total Environ. 655, 463–472 (2019).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Barkin, D. E. uso de la tierra agrícola en Mexico. Problemas del Desarrollo 12, 59–85 (1981).
Google Scholar 
Valdivieso-Pérez, I. A., García-Barrios, L. E., Álvarez-Solís, D. & Nahed-Toral, J. From cornfields to grasslands: Change in the quality of soil. Terra Latinoamericana. 30, 363–374 (2012).
Google Scholar 
Goldstein, A. et al. Protecting irrecoverable carbon in Earth’s ecosystems. Nat. Clim. Chang. 10, 287–295 (2020).ADS 
CAS 
Article 

Google Scholar 
CONAFOR. Acciones Tempranas REDD+ Mexico. https://www.gob.mx/conafor/documentos/acciones-tempranas-redd (2017). Accessed 04 Oct 2020.CATIE. Bidiversidad y paisajes ganaderos agrosilvopastoriles sostenibles. https://www.biopasos.com (2020). Accessed 04 Oct 2020.Freire-Santos, P. Z. F., Crouzeilles, R. & Sansevero, J. B. B. Can agroforestry systems enhance biodiversity and ecosystem service provision in agricultural landscapes? A meta-analysis for the Brazilian Atlantic Forest. For. Ecol. Manage. 433, 140–145 (2019).Article 

Google Scholar 
Zanne, A. et al. Data from: Towards a worldwide wood economics spectrum. (2009). 10.5061/dryad.234.Chave, J. et al. Improved allometric models to estimate the aboveground biomass of tropical trees. Glob. Change Biol. 20, 3177–3190 (2014).ADS 
Article 

Google Scholar 
Bojórquez, A. et al. Improving the accuracy of aboveground biomass estimations in secondary tropical dry forests. For. Ecol. Manage. 474, 118384 (2020).Article 

Google Scholar 
Cairns, M. A., Brown, S., Helmer, E. H. & Baumgardner, G. A. Root biomass allocation in the world’s upland forests. Oecologia 111, 1–11 (1997).ADS 
PubMed 
Article 

Google Scholar 
Shannon, C.E., Weaver. A Mathematical Theory of Communication Vol. 27 (University of Illinois Press, 1964).Sorensen, T. A. A method of establishing groups of equal amplitude in plant sociology based on similarity of species content and its application to analyses of the vegetation on Danish commons. Biol. Skar. 5, 1–34 (1948).
Google Scholar 
Pielou, E. C. The measurement of diversity in different types of biological collections. J. Theor. Biol. 13, 131–144 (1966).ADS 
Article 

Google Scholar 
Van Wagner, C. Practical Aspects of the Line Intersect Method Vol. 12 (Canadian Forestry Service, 1982).
Google Scholar 
Heanes, D. Determination of total organic-C in soils by an improved chromic acid digestion and spectrophotometric procedure. Commun. Soil Sci. Plant Anal. 15, 1191–1213 (1984).CAS 
Article 

Google Scholar  More

Doney, S. C. et al. Climate change impacts on marine ecosystems. Annu. Rev. Mar. Sci. 4, 11–37. https://doi.org/10.1146/annurev-marine-041911-111611 (2012).ADS 
Article 

Google Scholar 
Hoegh-Guldberg, O., Poloczanska, E. S., Skirving, W. & Dove, S. Coral reef ecosystems under climate change and ocean acidification. Front. Mar. Sci. 4, 158 (2017).Article 

Google Scholar 
Weis, V. M. Cellular mechanisms of Cnidarian bleaching: stress causes the collapse of symbiosis. J. Exp. Biol. 211, 3059–3066 (2008).CAS 
PubMed 
Article 

Google Scholar 
Fitt, W., Brown, B., Warner, M. & Dunne, R. Coral bleaching: interpretation of thermal tolerance limits and thermal thresholds in tropical corals. Coral Reefs 20, 51–65. https://doi.org/10.1007/s003380100146 (2001).Article 

Google Scholar 
Fujise, L., Yamashita, H., Suzuki, G. & Koike, K. Expulsion of zooxanthellae (Symbiodinium) from several species of scleractinian corals: comparison under non-stress conditions and thermal stress conditions. Galaxea, JCRS 15, 29–36. https://doi.org/10.3755/galaxea.15.29 (2013).Article 

Google Scholar 
Rädecker, N. et al. Heat stress destabilizes symbiotic nutrient cycling in corals. PNAS USA https://doi.org/10.1073/pnas.2022653118 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
LaJeunesse, T. C. et al. Systematic revision of Symbiodiniaceae highlights the antiquity and diversity of coral endosymbionts. Curr. Biol. 28, 2570-2580.e6. https://doi.org/10.1016/j.cub.2018.07.008 (2018).CAS 
Article 
PubMed 

Google Scholar 
Wooldridge, S. A. Breakdown of the coral-algae symbiosis. Towards formalising a linkage between warm-water bleaching thresholds and the growth rate of the intracellular zooxanthellae. Biogeosciences 10, 1647–1658 (2013).ADS 
Article 

Google Scholar 
Wiedenmann, J. et al. Nutrient enrichment can increase the susceptibility of reef corals to bleaching. Nat. Clim. Change 3, 160–164 (2013).ADS 
CAS 
Article 

Google Scholar 
Peña-García, D., Ladwig, N., Turki, A. J. & Mudarris, M. S. Input and dispersion of nutrients from the Jeddah Metropolitan Area, Red Sea. Mar. Pollut. Bull. 80, 41–51. https://doi.org/10.1016/j.marpolbul.2014.01.052 (2014).CAS 
Article 
PubMed 

Google Scholar 
Morris, L. A., Voolstra, C. R., Quigley, K. M., Bourne, D. G. & Bay, L. K. Nutrient availability and metabolism affect the stability of coral–Symbiodiniaceae symbioses. Trends Microbiol. 27, 678–689 (2019).CAS 
PubMed 
Article 

Google Scholar 
Ferrier-Pagés, C., Gattuso, J.-P., Dallot, S. & Jaubert, J. Effect of nutrient enrichment on growth and photosynthesis of the zooxanthellate coral Stylophora pistillata. Coral Reefs 19, 103–113. https://doi.org/10.1007/s003380000078 (2000).Article 

Google Scholar 
Rosset, S., Wiedenmann, J., Reed, A. J. & D’angelo, C. Phosphate deficiency promotes coral bleaching and is reflected by the ultrastructure of symbiotic dinoflagellates. Mar. Pollut. Bull. 118, 180–187. https://doi.org/10.1016/j.marpolbul.2017.02.044 (2017).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Patterson, K. et al. Distinct signalling pathways and transcriptome response signatures differentiate ammonium- and nitrate-supplied plants. Plant Cell Environ. 33, 1486–1501 (2010).CAS 
PubMed 
PubMed Central 

Google Scholar 
Ezzat, L., Maguer, J.-F., Grover, R. & Ferrier-Pagès, C. New insights into carbon acquisition and exchanges within the coral–dinoflagellate symbiosis under NH 4+ and NO 3− supply. Proc. R. Soc. B. 282, 20150610 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
Guan, Y., Hohn, S., Wild, C. & Merico, A. Vulnerability of global coral reef habitat suitability to ocean warming, acidification and eutrophication. Glob. Change Biol. 26, 5646–5660 (2020).ADS 
Article 

Google Scholar 
Roff, G. & Mumby, P. J. Global disparity in the resilience of coral reefs. Trends Ecol. Evol. 27, 404–413 (2012).PubMed 
Article 

Google Scholar 
Knowlton, N. & Jackson, J. B. C. Shifting baselines, local impacts, and global change on coral reefs. PLoS Biol. 6, e54 (2008).PubMed 
PubMed Central 
Article 

Google Scholar 
Vollstedt, S., Xiang, N., Simancas-Giraldo, S. M. & Wild, C. Organic eutrophication increases resistance of the pulsating soft coral Xenia umbellata to warming. PeerJ 8, e9182 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Fabricius, K. E., Cséke, S., Humphrey, C. & De’ath, G. Does trophic status enhance or reduce the thermal tolerance of scleractinian corals? A review, experiment and conceptual framework. PloS one 8, e54399 (2013).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Cardini, U. et al. Functional significance of dinitrogen fixation in sustaining coral productivity under oligotrophic conditions. Proc. Biol. Sci. 282, 20152257 (2015).PubMed 
PubMed Central 

Google Scholar 
Baker, D. M., Freeman, C. J., Wong, J. C. Y., Fogel, M. L. & Knowlton, N. Climate change promotes parasitism in a coral symbiosis. ISME J. 12, 921–930. https://doi.org/10.1038/s41396-018-0046-8 (2018).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
de Barros, F. et al. Unravelling the different causes of nitrate and ammonium effects on coral bleaching. Sci. Rep. 10, 11975 (2020).ADS 
Article 

Google Scholar 
Steinberg, R. K., Dafforn, K. A., Ainsworth, T. & Johnston, E. L. Know thy anemone. A review of threats to octocorals and anemones and opportunities for their restoration. Front. Mar. Sci. 7, 590 (2020).Article 

Google Scholar 
Norström, A. V., Nyström, M., Lokrantz, J. & Folke, C. Alternative states on coral reefs. Beyond coral–macroalgal phase shifts. Mar. Ecol. Prog. Ser. 376, 295–306 (2009).ADS 
Article 

Google Scholar 
van de Water, J. A. J. M., Allemand, D. & Ferrier-Pagès, C. Host-microbe interactions in octocoral holobionts—recent advances and perspectives. Microbiome 6, 64 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Syms, C. & Jones, G. P. Dysturbance, habitat structure, and the dynamics of a coral-reef fish community. Ecology 81, 2714–2729 (2000).Article 

Google Scholar 
Syms, C. & Jones, G. P. Soft corals exert no direct effects on coral reef fish assemblages. Oecologia 127, 560–571. https://doi.org/10.1007/s004420000617 (2001).ADS 
Article 
PubMed 

Google Scholar 
Epstein, H. E. & Kingsford, M. J. Are soft coral habitats unfavourable? A closer look at the association between reef fishes and their habitat. Environ. Biol. Fishes 102, 479–497 (2019).Article 

Google Scholar 
Janes, M. P. Distribution and diversity of the soft coral family Xeniidae (Coelenterata: Octocorallia) in Lembeh Strait, Indonesia. Galaxea, JCRS 15, 195–200 (2013).Article 

Google Scholar 
Fox, H. E., Pet, J. S., Dahuri, R. & Caldwell, R. L. Recovery in rubble fields. Long-term impacts of blast fishing. Mar. Pollut. Bull. 46, 1024–1031 (2003).CAS 
PubMed 
Article 

Google Scholar 
Al-Sofyani, A. A. & Niaz, G. R. A comparative study of the components of the hard coral Seriatopora hystrix and the soft coral Xenia umbellata along the Jeddah coast, Saudi Arabia. Rev. Biol. Mar. Oceanogr. 42, 207–219 (2007).Article 

Google Scholar 
Kremien, M., Shavit, U., Mass, T. & Genin, A. Benefit of pulsation in soft corals. PNAS USA 110, 8978–8983 (2013).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Swart, P. K., Saied, A. & Lamb, K. Temporal and spatial variation in the δ 15 N and δ 13 C of coral tissue and zooxanthellae in Montastraea faveolata collected from the Florida reef tract. Limnol. Oceanogr. 50, 1049–1058 (2005).ADS 
CAS 
Article 

Google Scholar 
Grottoli, A. G., Tchernov, D. & Winters, G. Physiological and biogeochemical responses of super-corals to thermal stress from the northern gulf of Aqaba, Red Sea. Front. Mar. Sci. 4, 215 (2017).Article 

Google Scholar 
Tanaka, Y., Miyajima, T., Koike, I., Hayashibara, T. & Ogawa, H. Imbalanced coral growth between organic tissue and carbonate skeleton caused by nutrient enrichment. Limnol. Oceanogr. 52, 1139–1146 (2007).ADS 
CAS 
Article 

Google Scholar 
Marubini, F. & Davies, P. S. Nitrate increases zooxanthellae population density and reduces skeletogenesis in corals. Mar. Biol. 127, 319–328 (1996).CAS 
Article 

Google Scholar 
Dagenais-Bellefeuille, S. & Morse, D. Putting the N in dinoflagellates. Front. Microbiol. https://doi.org/10.3389/fmicb.2013.00369 (2013).Article 
PubMed 
PubMed Central 

Google Scholar 
Wooldridge, S. A. A new conceptual model for the warm-water breakdown of the coral—algae endosymbiosis. Mar. Freshwater Res. 60, 483 (2009).CAS 
Article 

Google Scholar 
Moed, J. R. & Hallegraeff, G. M. Some problems in the estimation of chlorophyll-a and phaeopigments from pre- and post-acidification spectrophotometrie measurements. Int. Revue Ges. Hydrobiol. Hydrogr. 63, 787–800 (1978).CAS 
Article 

Google Scholar 
Redfield, A. C. The biological control of chemical factors in the environment. Am. Sci. 46, A221-230A (1958).
Google Scholar 
Pupier, C. A., Bednarz, V. N. & Ferrier-Pagès, C. Studies with soft corals—recommendations on sample processing and normalization metrics. Front. Mar. Sci. 5, 2620 (2018).Article 

Google Scholar 
Pupier, C. A. et al. Dissolved nitrogen acquisition in the symbioses of soft and hard corals with Symbiodiniaceae: A key to understanding their different nutritional strategies?. Front. Microbiol. 12, 657759 (2021).PubMed 
PubMed Central 
Article 

Google Scholar 
Bednarz, V. N., Naumann, M. S., Niggl, W. & Wild, C. Inorganic nutrient availability affects organic matter fluxes and metabolic activity in the soft coral genus Xenia. J. Exp. Biol. 215, 3672–3679 (2012).CAS 
PubMed 

Google Scholar 
Béraud, E., Gevaert, F., Rottier, C. & Ferrier-Pagès, C. The response of the scleractinian coral Turbinaria reniformis to thermal stress depends on the nitrogen status of the coral holobiont. J. Exp. Biol. 216, 2665–2674 (2013).PubMed 

Google Scholar 
Ezzat, L., Towle, E., Irisson, J.-O., Langdon, C. & Ferrier-Pagès, C. The relationship between heterotrophic feeding and inorganic nutrient availability in the scleractinian coral T. reniformis under a short-term temperature increase. Limnol. Oceanogr. 61, 89–102 (2016).ADS 
Article 

Google Scholar 
Dobson, K. L. et al. Moderate nutrient concentrations are not detrimental to corals under future ocean conditions. Mar. Biol. https://doi.org/10.1007/s00227-021-03901-3 (2021).Article 

Google Scholar 
Strychar, K. B., Coates, M., Sammarco, P. W., Piva, T. J. & Scott, P. T. Loss of Symbiodinium from bleached soft corals Sarcophyton ehrenbergi, Sinularia sp. and Xenia sp.. J. Exp. Mar. Biol. Ecol. 320, 159–177. https://doi.org/10.1016/j.jembe.2004.12.039 (2005).Article 

Google Scholar 
Sammarco, P. W. & Strychar, K. B. Responses to high seawater temperatures in zooxanthellate octocorals. PloS one 8, e54989 (2013).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Osman, E. O. et al. Thermal refugia against coral bleaching throughout the northern Red Sea. Glob. Change Biol. 24, e474–e484. https://doi.org/10.1111/gcb.13895 (2018).Article 

Google Scholar 
Fine, M., Gildor, H. & Genin, A. A coral reef refuge in the Red Sea. Glob. Change Biol. 19, 3640–3647 (2013).ADS 
Article 

Google Scholar 
Evensen, N. R., Fine, M., Perna, G., Voolstra, C. R. & Barshis, D. J. Remarkably high and consistent tolerance of a Red Sea coral to acute and chronic thermal stress exposures. Limnol. Oceanogr. 66, 1718–1729 (2021).ADS 
Article 

Google Scholar 
Sawall, Y. et al. Extensive phenotypic plasticity of a Red Sea coral over a strong latitudinal temperature gradient suggests limited acclimatization potential to warming. Sci. Rep. 5, 8940 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Carpenter, E. J., Harvey, H., Fry, B. & Capone, D. G. Biogeochemical tracers of the marine cyanobacterium Trichodesmium. Deep-Sea Res. I: Oceanogr. Res. Pap. 44, 27–38 (1997).ADS 
CAS 
Article 

Google Scholar 
Kürten, B. et al. Influence of environmental gradients on C and N stable isotope ratios in coral reef biota of the Red Sea, Saudi Arabia. J. Sea Res. 85, 379–394 (2014).ADS 
Article 

Google Scholar 
Karcher, D. B. et al. Nitrogen eutrophication particularly promotes turf algae in coral reefs of the central Red Sea. PeerJ 8, e8737 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Sterner, R. W. & Elser, J. J. Ecological Stoichiometry. The Biology of Elements from Molecules to the Biosphere (Princeton University Press, 2002).Tilstra, A. et al. Light induced intraspecific variability in response to thermal stress in the hard coral Stylophora pistillata. PeerJ 5, e3802 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Siebeck, U. E., Marshall, N. J., Klüter, A. & Hoegh-Guldberg, O. Monitoring coral bleaching using a colour reference card. Coral Reefs 25, 453–460 (2006).ADS 
Article 

Google Scholar 
Venn, A. A., Wilson, M. A., Trapido-Rosenthal, H. G., Keely, B. J. & Douglas, A. E. The impact of coral bleaching on the pigment profile of the symbiotic alga, Symbiodinium. Plant Cell Environ. 29, 2133–2142 (2006).CAS 
PubMed 
Article 

Google Scholar 
Dubinsky, Z. V. Y. et al. The effect of external nutrient resources on the optical properties and photosynthetic efficiency of Stylophora pistillata. Proc. R. Soc. B.: Biol. Sci. 239, 231–246 (1990).ADS 

Google Scholar 
Fabricius, K. E. Effects of irradiance, flow, and colony pigmentation on the temperature microenvironment around corals: Implications for coral bleaching?. Limnol. Oceanogr. 51, 30–37 (2006).ADS 
Article 

Google Scholar 
Nordemar, I., Nyström, M. & Dizon, R. Effects of elevated seawater temperature and nitrate enrichment on the branching coral Porites cylindrica in the absence of particulate food. Mar. Biol. 142, 669–677 (2003).CAS 
Article 

Google Scholar 
Lewis, J. B. Feeding behaviour and feeding ecology of the Octocorallia (Coelenterata: Anthozoa). J. Zool. 196, 371–384 (1982).Article 

Google Scholar 
Studivan, M. S., Hatch, W. I. & Mitchelmore, C. L. Responses of the soft coral Xenia elongata following acute exposure to a chemical dispersant. SpringerPlus 4, 80 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
Parrin, A. P. et al. Symbiodinium migration mitigates bleaching in three octocoral species. J. Exp. Mar. Biol. Ecol. 474, 73–80 (2016).Article 

Google Scholar 
Parrin, A. P. et al. Within-colony migration of symbionts during bleaching of octocorals. Biol. Bull. 223, 245–256 (2012).PubMed 
Article 

Google Scholar 
Bourne, D. G., Morrow, K. M. & Webster, N. S. Insights into the coral microbiome. Underpinning the health and resilience of reef ecosystems. Annu. Rev. Microbiol. 70, 317–340 (2016).CAS 
PubMed 
Article 

Google Scholar 
Furnas, M., Mitchell, A., Skuza, M. & Brodie, J. In the other 90%: phytoplankton responses to enhanced nutrient availability in the Great Barrier Reef Lagoon. Mar. Pollut. Bull. 51, 253–265 (2005).CAS 
PubMed 
Article 

Google Scholar 
Ziegler, M. et al. Coral microbial community dynamics in response to anthropogenic impacts near a major city in the central Red Sea. Mar. Pollut. Bull. https://doi.org/10.1016/j.marpolbul.2015.12.045 (2016).Article 
PubMed 

Google Scholar 
Gruber, R. et al. Marine monitoring program: Annual report for inshore water quality monitoring 2018–19. Report for the Great Barrier Reef Marine Park Authority. GBRMPA, Townsville (2020).Dinesen, Z. D. Patterns in the distribution of soft corals across the central Great Barrier Reef. Coral Reefs 1, 229–236. https://doi.org/10.1007/BF00304420 (1983).ADS 
Article 

Google Scholar 
Benayahu, Y. et al. Octocorals of the Indo-Pacific. In Mesophotic Coral Ecosystems Vol. 12 (eds Loya, Y. et al.) 709–728 (Springer International Publishing, Cham, 2019).Chapter 

Google Scholar 
Tilot, V., Leujak, W., Ormond, R. F. G., Ashworth, J. A. & Mabrouk, A. Monitoring of South Sinai coral reefs: Influence of natural and anthropogenic factors. Aquat. Conserv. 18, 1109–1126 (2008).Article 

Google Scholar 
D’Angelo, C. & Wiedenmann, J. Impacts of nutrient enrichment on coral reefs. New perspectives and implications for coastal management and reef survival. Curr. Opin. Environ. Sustain. 7, 82–93 (2014).Article 

Google Scholar 
Wooldridge, S. A. & Done, T. J. Improved water quality can ameliorate effects of climate change on corals. Ecol. Appl. 19, 1492–1499 (2009).PubMed 
Article 

Google Scholar 
Nugues, M. M. & Roberts, C. M. Partial mortality in massive reef corals as an indicator of sediment stress on coral reefs. Mar. Pollut. Bull. 46, 314–323 (2003).CAS 
PubMed 
Article 

Google Scholar 
LeGresley, M. & McDermott, G. Counting chamber methods for quantitative phytoplankton analysis – haemocytometer, Palmer-Maloney cell and Sedgewick-Rafter cell. In Microscopic and Molecular Methods for Quantitative Phytoplankton Analysis, edited by B. Karlson, C. Cusack & E. Bresnan (IOC UNESCO, Paris, France, 2010), pp. 25–30.Jeffrey, S. W. & Humphrey, G. F. New spectrophotometric equations for determining chlorophylls a, b, c1 and c2 in higher plants, algae and natural phytoplankton. Biochem. Physiol. Pflanz. 167, 191–194 (1975).CAS 
Article 

Google Scholar 
D’Angelo, C. et al. Blue light regulation of host pigment in reef-building corals. Mar. Ecol. Prog. Ser. 364, 97–106 (2008).ADS 
Article 

Google Scholar 
Feys, J. Nonparametric tests for the interaction in two-way factorial designs using R. R J. 8, 367 (2016).Article 

Google Scholar 
Noguchi, K., Gel, Y. R., Brunner, E. & Konietschke, F. nparLD An R software package for the nonparametric analysis of longitudinal data in factorial experiments. J. Stat. Soft. 50, 1–23 (2012).Article 

Google Scholar 
Schlöder, C. & D’Croz, L. Responses of massive and branching coral species to the combined effects of water temperature and nitrate enrichment. J. Exp. Mar. Biol. Ecol. 313, 255–268 (2004).Article 

Google Scholar 
Faxneld, S., Jörgensen, T. L. & Tedengren, M. Effects of elevated water temperature, reduced salinity and nutrient enrichment on the metabolism of the coral Turbinaria mesenterina. Estuar. Coast. Shelf Sci. 88, 482–487 (2010).ADS 
CAS 
Article 

Google Scholar 
Chumun, P. K. et al. High nitrate levels exacerbate thermal photo-physiological stress of zooxanthellae in the reef-building coral Pocillopora damicornis. Eco-Eng. 25, 1–9 (2013).
Google Scholar 
Higuchi, T., Yuyama, I. & Nakamura, T. The combined effects of nitrate with high temperature and high light intensity on coral bleaching and antioxidant enzyme activities. Reg. Stud. Mar. Sci. 2, 27–31 (2015).
Google Scholar  More

Orcutt BN, Sylvan JB, Knab NJ, Edwards KJ. Microbial ecology of the dark ocean above, at, and below the seafloor. Microbiol Mol Biol Rev. 2011;75:361–422.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Whitman WB, Coleman DC, Wiebe WJ. Prokaryotes: the unseen majority. Proc Natl Acad Sci USA. 1998;95:6578–83.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Arístegui J, Gasol JM, Duarte CM, Herndl GJ. Microbial oceanography of the dark ocean’s pelagic realm. Limnol Oceanogr. 2009;54:1501–29.Article 

Google Scholar 
Ebrahimi A, Schwartzman J, Cordero OX. Cooperation and spatial self-organization determine rate and efficiency of particulate organic matter degradation in marine bacteria. Proc Natl Acad Sci USA. 2019;116:23309–16.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Fontanez KM, Eppley JM, Samo TJ, Karl DM, DeLong EF. Microbial community structure and function on sinking particles in the North Pacific Subtropical Gyre. Front Microbiol. 2015;6:469.PubMed 
PubMed Central 
Article 

Google Scholar 
Simon M, Grossart HP, Schweitzer B, Ploug H. Microbial ecology of organic aggregates in aquatic ecosystems. Aquat Microb Ecol. 2002;28:175–211.Article 

Google Scholar 
Alldredge AL, Silver MW. Characteristics, dynamics and significance of marine snow. Progr Oceanogr. 1988;20:41–82.Article 

Google Scholar 
Zehr JP, Kudela RM. Nitrogen cycle of the open ocean: from genes to ecosystems. Ann Rev Mar Sci. 2011;3:197–225.PubMed 
Article 

Google Scholar 
Bergauer K, Fernandez-Guerra A, Garcia JAL, Sprenger RR, Stepanauskas R, Pachiadaki MG, et al. Organic matter processing by microbial communities throughout the Atlantic water column as revealed by metaproteomics. Proc Natl Acad Sci USA. 2018;115:E400–8.CAS 
PubMed 
Article 

Google Scholar 
Acinas SG, Sánchez P, Salazar G, Cornejo-Castillo FM, Sebastián M, Logares R, et al. Deep ocean metagenomes provide insight into the metabolic architecture of bathypelagic microbial communities. Commun Biol. 2021;4:604.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Teira E, Lebaron P, van Aken H, Herndl GJ. Distribution and activity of Bacteria and Archaea in the deep water masses of the North Atlantic. Limnol Oceanogr. 2006;51:2131–44.CAS 
Article 

Google Scholar 
Herndl GJ, Reinthaler T, Teira E, van Aken H, Veth C, Pernthaler A, et al. Contribution of Archaea to total prokaryotic production in the deep Atlantic Ocean. Appl Environ Microbiol. 2005;71:2303–9.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Gasol JM, Alonso-Sáez L, Vaqué D, Baltar F, Calleja ML, Duarte CM, et al. Mesopelagic prokaryotic bulk and single-cell heterotrophic activity and community composition in the NW Africa-Canary Islands coastal-transition zone. Progr Oceanogr. 2009;83:189–96.Article 

Google Scholar 
Dekas AE, Parada AE, Mayali X, Fuhrman JA, Wollard J, Weber PK, et al. Characterizing chemoautotrophy and heterotrophy in marine archaea and bacteria with single-cell multi-isotope NanoSIP. Front Microbiol. 2019;10:2682.PubMed 
PubMed Central 
Article 

Google Scholar 
Musat N, Foster R, Vagner T, Adam B, Kuypers MMM. Detecting metabolic activities in single cells, with emphasis on nanoSIMS. FEMS Microbiol Rev. 2012;36:486–511.CAS 
PubMed 
Article 

Google Scholar 
Orphan VJ, House CH. Geobiological investigations using secondary ion mass spectrometry: microanalysis of extant and paleo-microbial processes. Geobiology. 2009;7:360–72.CAS 
PubMed 
Article 

Google Scholar 
Pett-Ridge J, Weber PK. NanoSIP: NanoSIMS applications for microbial biology. Methods Mol Biol. 2012;881:375–408.CAS 
PubMed 
Article 

Google Scholar 
Nuñez J, Renslow R, Cliff JB, Anderton CR. NanoSIMS for biological applications: current practices and analyses. Biointerphases. 2018;13:03B301.Article 

Google Scholar 
Dawson KS, Scheller S, Dillon JG, Orphan VJ. Stable isotope phenotyping via cluster analysis of NanoSIMS data as a method for characterizing distinct microbial ecophysiologies and sulfur-cycling in the environment. Front Microbiol. 2016;7:774.PubMed 
PubMed Central 
Article 

Google Scholar 
Arandia-Gorostidi N, Weber PK, Alonso-Sáez L, Morán XAG, Mayali X. Elevated temperature increases carbon and nitrogen fluxes between phytoplankton and heterotrophic bacteria through physical attachment. ISME J. 2017;11:641–50.CAS 
PubMed 
Article 

Google Scholar 
Kopf SH, McGlynn SE, Green-Saxena A, Guan Y, Newman DK, Orphan VJ. Heavy water and (15) N labelling with NanoSIMS analysis reveals growth rate-dependent metabolic heterogeneity in chemostats. Environ Microbiol. 2015;17:2542–56.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Schreiber F, Littmann S, Lavik G, Escrig S, Meibom A, Kuypers MMM, et al. Phenotypic heterogeneity driven by nutrient limitation promotes growth in fluctuating environments. Nat Microbiol. 2016;1:16055.CAS 
PubMed 
Article 

Google Scholar 
Berthelot H, Duhamel S, L’Helguen S, Maguer JF, Wang S, Cetinic I, et al. NanoSIMS single cell analyses reveal the contrasting nitrogen sources for small phytoplankton. ISME J. 2019;13:651–62.CAS 
PubMed 
Article 

Google Scholar 
Calabrese F, Voloshynovska I, Musat F, Thullner M, Schlömann M, Richnow HH, et al. Quantitation and comparison of phenotypic heterogeneity among single cells of monoclonal microbial populations. Front Microbiol. 2019;10:1–23.Article 

Google Scholar 
Calabrese F, Stryhanyuk H, Moraru C, Schlömann M, Wick LY, Richnow HH, et al. Metabolic history and metabolic fitness as drivers of anabolic heterogeneity in isogenic microbial populations. Environ Microbiol. 2021;23:6764–76.CAS 
PubMed 
Article 

Google Scholar 
Gini C. Variabilità e Mutuabilità. Contributo allo Studio delle Distribuzioni e delle Relazioni Statistiche. C. Cuppini, Bologna; 1912.Fernández-Tschieder E, Binkley D. Linking competition with growth dominance and production ecology. Ecol Manag. 2018;414:99–107.Article 

Google Scholar 
Cordonnier T, Kunstler G. The Gini index brings asymmetric competition to light. Perspect Plant Ecol Evol Syst. 2015;17:107–15.Article 

Google Scholar 
Harch BD, Correll RL, Meech W, Kirkby CA, Pankhurst CE. Using the Gini coefficient with BIOLOG substrate utilisation data to provide an alternative quantitative measure for comparing bacterial soil communities. J Microbiol Methods. 1997;30:91–101.CAS 
Article 

Google Scholar 
Li J, Ma YB, Hu HW, Wang JT, Liu YR, He JZ. Field-based evidence for consistent responses of bacterial communities to copper contamination in two contrasting agricultural soils. Front Microbiol. 2015;6:31.PubMed 
PubMed Central 

Google Scholar 
Parada AE, Needham DM, Fuhrman JA. Every base matters: assessing small subunit rRNA primers for marine microbiomes with mock communities, time series and global field samples. Environ Microbiol. 2016;18:1403–14.CAS 
PubMed 
Article 

Google Scholar 
Polerecky L, Adam B, Milucka J, Musat N, Vagner T, Kuypers MM. Look@NanoSIMS-a tool for the analysis of nanoSIMS data in environmental microbiology. Environ Microbiol. 2012;14:1009–23.CAS 
PubMed 
Article 

Google Scholar 
Stryhanyuk H, Calabrese F, Kümmel S, Musat F, Richnow HH, Musat N. Calculation of single cell assimilation rates from SIP-nanoSIMS-derived isotope ratios: a comprehensive approach. Front Microbiol. 2018;9:2342.PubMed 
PubMed Central 
Article 

Google Scholar 
Arandia‐Gorostidi N, Alonso‐Sáez L, Stryhanyuk H, Richnow HH, Morán XAG, Musat N. Warming the phycosphere: Differential effect of temperature on the use of diatom‐derived carbon by two copiotrophic bacterial taxa. Environ Microbiol. 2020;22:1381–96.PubMed 
Article 

Google Scholar 
Mayali X, Weber PK, Pett-Ridge J. Taxon-specific C/N relative use efficiency for amino acids in an estuarine community. FEMS Microbiol Ecol. 2013;83:402–12.CAS 
PubMed 
Article 

Google Scholar 
Meyer NR, Fortney JL, Dekas AE. NanoSIMS sample preparation decreases isotope enrichment: magnitude, variability and implications for single-cell rates of microbial activity. Environ Microbiol. 2021;23:81–98.CAS 
PubMed 
Article 

Google Scholar 
Kemp PF, Lee S, Laroche J. Estimating the growth rate of slowly growing marine bacteria from RNA content. Appl Environ Microbiol. 1993;59:2594–601.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Baltar F, Arístegui J, Gasol J, Sintes E, van Aken H, Herndl G. High dissolved extracellular enzymatic activity in the deep central Atlantic Ocean. Aquat Micro Ecol. 2010;58:287–302.Article 

Google Scholar 
Lønborg C, Nieto-Cid M, Hernando-Morales V, Hernández-Ruiz M, Teira E, Álvarez-Salgado XA. Photochemical alteration of dissolved organic matter and the subsequent effects on bacterial carbon cycling and diversity. FEMS Microbiol Ecol. 2016;92:fiw048.PubMed 
Article 

Google Scholar 
Nagata T, Fukuda H, Fukuda R, Koike I. Bacter-ioplankton distribution and production in deep Pacific waters: large-scale geographic variations and possible coupling with sinking particle fluxes. Limnol Oceanogr. 2000;45:426–35.CAS 
Article 

Google Scholar 
Teira E, Reinthaler T, Pernthaler A, Pernthaler J, Herndl GJ. Combining catalyzed reporter deposition-fluorescence in situ hybridization and microautoradiography to detect substrate utilization by bacteria and Archaea in the deep ocean. Appl Environ Microbiol. 2004;70:4411–4.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Mestre M, Hofer J. The microbial conveyor belt: connecting the globe through dispersion and dormancy. Trends Microbiol. 2020;29:482–92.PubMed 
Article 

Google Scholar 
Giering SLC, Evans C. Overestimation of prokaryotic production by leucine incorporation—and how to avoid it. Limnol Oceanogr. 2022;67:726–38.Article 

Google Scholar 
Amos CM, Castelao RM, Medeiros PM. Offshore transport of particulate organic carbon in the California Current System by mesoscale eddies. Nat Commun. 2019;10:4940.PubMed 
PubMed Central 
Article 

Google Scholar 
Bauer JE, Druffel ERM. Ocean margins as a significant source of organic matter to the deep open ocean. Nature. 1998;392:482–5.CAS 
Article 

Google Scholar 
Tamburini C, Boutrif M, Garel M, Colwell RR, Deming JW. Prokaryotic responses to hydrostatic pressure in the ocean—a review. Environ Microbiol. 2013;15:1262–74.CAS 
PubMed 
Article 

Google Scholar 
Arrieta JM, Mayol E, Hansman RL, Herndl GJ, Dittmar T, Duarte CM. Dilution limits dissolved organic carbon utilization in the deep ocean. Science. 2015;348:331–3.CAS 
PubMed 
Article 

Google Scholar 
Alonso C, Musat N, Adam B, Kuypers M, Amann R. HISH-SIMS analysis of bacterial uptake of algal-derived carbon in the Río de la Plata estuary. Syst Appl Microbiol. 2012;35:541–8.CAS 
PubMed 
Article 

Google Scholar 
Klawonn I, Bonaglia S, Whitehouse MJ, Littmann S, Tienken D, Kuypers MMM, et al. Untangling hidden nutrient dynamics: rapid ammonium cycling and single-cell ammonium assimilation in marine plankton communities. ISME J. 2019;13:1960–74.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Campbell BJ, Yu L, Heidelberg JF, Kirchman DL. Activity of abundant and rare bacteria in a coastal ocean. Proc Natl Acad Sci USA. 2011;108:12776–81.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Kirchman DL. Growth rates of microbes in the oceans. Annu Rev Mar Sci. 2016;8:150720190448005.Article 

Google Scholar 
Blazewicz SJ, Barnard RL, Daly RA, Firestone MK. Evaluating rRNA as an indicator of microbial activity in environmental communities: limitations and uses. ISME J. 2013;7:2061–8.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Licht TR, Tolker-Nielsen T, Holmstrøm K, Krogfelt KA, Molin S. Inhibition of Escherichia coli precursor-16S rRNA processing by mouse intestinal contents. Environ Microbiol. 1999;1:23–32.CAS 
PubMed 
Article 

Google Scholar 
Sukenik A, Kaplan-Levy RN, Welch JM, Post AF. Massive multiplication of genome and ribosomes in dormant cells (akinetes) of Aphanizomenon ovalisporum (Cyanobacteria). ISME J. 2012;6:670–9.CAS 
PubMed 
Article 

Google Scholar 
Dekas AE, Connon SA, Chadwick GL, Trembath-Reichert E, Orphan VJ. Activity and interactions of methane seep microorganisms assessed by parallel transcription and FISH-NanoSIMS analyses. ISME J. 2016;10:678–92.CAS 
PubMed 
Article 

Google Scholar 
Wu J, Gao W, Johnson R, Zhang W, Meldrum D. Integrated metagenomic and metatranscriptomic analyses of microbial communities in the meso- and bathypelagic realm of North Pacific Ocean. Mar Drugs. 2013;11:3777–801.PubMed 
PubMed Central 
Article 

Google Scholar 
Ackermann M. A functional perspective on phenotypic heterogeneity in microorganisms. Nat Rev Microbiol. 2015;13:497–508.CAS 
PubMed 
Article 

Google Scholar  More

Thompson, R. L. et al. Acceleration of global N2O emissions seen from two decades of atmospheric inversion. Nat. Clim. Change 9, 993–998 (2019).CAS 
Article 

Google Scholar 
Tian, H. et al. A comprehensive quantification of global nitrous oxide sources and sinks. Nature 586, 248–256 (2020).CAS 
PubMed 
Article 

Google Scholar 
Zhuang, Q., Lu, Y. & Chen, M. An inventory of global N2O emissions from the soils of natural terrestrial ecosystems. Atm. Environ. 47, 66–75 (2012).CAS 
Article 

Google Scholar 
Huang, J. et al. Estimation of regional emissions of nitrous oxide from 1997 to 2005 using multinetwork measurements, a chemical transport model, and an inverse method. J. Geophys. Res. 113, D17313 (2008).Article 

Google Scholar 
D’Amelio, M. T. S., Gatti, L. V., Miller, J. B. & Tans, P. Regional N2O fluxes in Amazonia derived from aircraft vertical profiles. Atmos. Chem. Phys. 9, 8785–8797 (2009).Article 

Google Scholar 
Teh, Y. A., Murphy, W. A., Berrio, J.-C., Boom, A. & Page, S. E. Seasonal variability in methane and nitrous oxide fluxes from tropical peatlands in the western Amazon basin. Biogeosciences 14, 3669–3683 (2017).CAS 
Article 

Google Scholar 
Finn, D. R. et al. Methanogens and methanotrophs show nutrient-dependent community assemblage patterns across tropical peatlands of the Pastaza-Marañón Basin, Peruvian Amazonia. Front. Microbiol. 11, 746 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Buessecker, S. et al. Effects of sterilization techniques on chemodenitrification and N2O production in tropical peat soil microcosms. Biogeosciences 16, 4601–4612 (2019).CAS 
Article 

Google Scholar 
Heil, J., Liu, S., Vereecken, H. & Brüggemann, N. Abiotic nitrous oxide production from hydroxylamine in soils and their dependence on soil properties. Soil Biol. Biochem. 84, 107–115 (2015).CAS 
Article 

Google Scholar 
Samarkin, V. A. et al. Abiotic nitrous oxide emission from the hypersaline Don Juan Pond in Antarctica. Nat. Geosci. 3, 341–344 (2010).CAS 
Article 

Google Scholar 
Otte, J. M. et al. N2O formation by nitrite-induced (chemo)denitrification in coastal marine sediment. Sci. Rep. 9, 10691 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Jones, L. C., Peters, B., Pacheco, J. S. L., Casciotti, K. L. & Fendorf, S. Stable isotopes and iron oxide mineral products as markers of chemodenitrification. Environ. Sci. Technol. 49, 3444–3452 (2015).CAS 
PubMed 
Article 

Google Scholar 
Tolman, W. B. Binding and activation of N2O at transition-metal centers: recent mechanistic insights. Angew. Chem. Int. Ed. 49, 1018–1024 (2010).CAS 
Article 

Google Scholar 
Holtan-Hartwig, L., Dörsch, P. & Bakken, L. R. Low temperature control of soil denitrifying communities: kinetics of N2O production and reduction. Soil Biol. Biochem. 34, 1797–1806 (2002).CAS 
Article 

Google Scholar 
Gorelsky, S. I., Ghosh, S. & Solomon, E. I. Mechanism of N2O reduction by the μ4-S tetranuclear CuZ cluster of nitrous oxide reductase. J. Am. Chem. Soc. https://doi.org/10.1021/ja055856o (2005).Tsai, M.-L. et al. [Cu2O]2+ active site formation in Cu–ZSM-5: geometric and electronic structure requirements for N2O activation. J. Am. Chem. Soc. https://doi.org/10.1021/ja4113808 (2014).Sanford, R. A. et al. Unexpected nondenitrifier nitrous oxide reductase gene diversity and abundance in soils. Proc. Natl Acad. Sci. USA 109, 19709–19714 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Jones, C. M. et al. Recently identified microbial guild mediates soil N2O sink capacity. Nat. Clim. Change 4, 801–805 (2014).CAS 
Article 

Google Scholar 
Hallin, S., Philippot, L., Löffler, F. E., Sanford, R. A. & Jones, C. M. Genomics and ecology of novel N2O-reducing microorganisms. Trends Microbiol. 26, 43–55 (2018).CAS 
PubMed 
Article 

Google Scholar 
Lycus, P. et al. A bet-hedging strategy for denitrifying bacteria curtails their release of N2O. Proc. Natl Acad. Sci. USA 115, 11820–11825 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Burns, L. C., Stevens, R. J. & Laughlin, R. J. Determination of the simultaneous production and consumption of soil nitrite using 15N. Soil Biol. Biochem. 27, 839–844 (1995).CAS 
Article 

Google Scholar 
Burns, L. C., Stevens, R. J. & Laughlin, R. J. Production of nitrite in soil by simultaneous nitrification and denitrification. Soil Biol. Biochem. 28, 609–616 (1996).CAS 
Article 

Google Scholar 
Wullstein, L. H. & Gilmour, C. M. Non-enzymatic formation of nitrogen gas. Nature 210, 1150–1151 (1966).CAS 
Article 

Google Scholar 
Liu, S., Schloter, M., Hu, R., Vereecken, H. & Brüggemann, N. Hydroxylamine contributes more to abiotic N2O production in soils than nitrite. Front. Environ. Sci. https://doi.org/10.3389/fenvs.2019.00047 (2019).Thorn, K. A. & Mikita, M. A. Nitrite fixation by humic substances: nitrogen-15 nuclear magnetic resonance evidence for potential intermediates in chemodenitrification. Soil Sci. Soc. Am. J. 64, 568–582 (2000).CAS 
Article 

Google Scholar 
Thorn, K. A., Younger, S. J. & Cox, L. G. Order of functionality loss during photodegradation of aquatic humic substances. J. Environ. Qual. 39, 1416–1428 (2010).CAS 
PubMed 
Article 

Google Scholar 
Klüpfel, L., Piepenbrock, A., Kappler, A. & Sander, M. Humic substances as fully regenerable electron acceptors in recurrently anoxic environments. Nat. Geosci. 7, 195–200 (2014).Article 

Google Scholar 
Lovley, D. R. & Blunt-Harris, E. L. Role of humic-bound iron as an electron transfer agent in dissimilatory Fe(III) reduction. Appl. Environ. Microbiol. 65, 4252–4254 (1999).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Kappler, A., Benz, M., Schink, B. & Brune, A. Electron shuttling via humic acids in microbial iron(III) reduction in a freshwater sediment. FEMS Microbiol. Ecol. 47, 85–92 (2004).CAS 
PubMed 
Article 

Google Scholar 
Van Cleemput, O., Patrick, W. H. & McIlhenny, R. C. Nitrite decomposition in flooded soil under different pH and redox potential conditions. Soil Sci. Soc. Am. J. 40, 55–60 (1976).Article 

Google Scholar 
Van Cleemput, O. & Baert, L. Nitrite: a key compound in N loss processes under acid conditions? Plant Soil 76, 233–241 (1984).Article 

Google Scholar 
Porter, L. K. Gaseous products produced by anaerobic reaction of sodium nitrite with oxime compounds and oximes synthesized from organic matter. Soil Sci. Soc. Am. J. 33, 696–702 (1969).CAS 
Article 

Google Scholar 
Liu, B., Mørkved, P. T., Frostegård, Å. & Bakken, L. R. Denitrification gene pools, transcription and kinetics of NO, N2O and N2 production as affected by soil pH. FEMS Microbiol. Ecol. 72, 407–417 (2010).CAS 
PubMed 
Article 

Google Scholar 
Palmer, K., Biasi, C. & Horn, M. A. Contrasting denitrifier communities relate to contrasting N2O emission patterns from acidic peat soils in arctic tundra. ISME J. 6, 1058–1077 (2012).CAS 
PubMed 
Article 

Google Scholar 
Domeignoz-Horta, L. et al. The diversity of the N2O reducers matters for the N2O:N2 denitrification end-product ratio across an annual and a perennial cropping system. Front. Microbiol. https://doi.org/10.3389/fmicb.2015.00971 (2015).Domeignoz-Horta, L. A. et al. Peaks of in situ N2O emissions are influenced by N2O-producing and reducing microbial communities across arable soils. Glob. Change Biol. 24, 360–370 (2018).Article 

Google Scholar 
Onley, J. R., Ahsan, S., Sanford, R. A. & Löffler, F. E. Denitrification by Anaeromyxobacter dehalogenans, a common soil bacterium lacking the nitrite reductase genes nirS and nirK. Appl. Environ. Microbiol. 84, 4 (2018).Article 

Google Scholar 
Sanford, R. A., Cole, J. R. & Tiedje, J. M. Characterization and description of Anaeromyxobacter dehalogenans gen. nov., sp. nov., an aryl-halorespiring facultative anaerobic myxobacterium. Appl. Environ. Microbiol. 68, 893–900 (2002).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Mohr, K. I., Zindler, T., Wink, J., Wilharm, E. & Stadler, M. Myxobacteria in high moor and fen: an astonishing diversity in a neglected extreme habitat. MicrobiologyOpen 6, e00464 (2017).PubMed Central 
Article 

Google Scholar 
Hori, T., Müller, A., Igarashi, Y., Conrad, R. & Friedrich, M. W. Identification of iron-reducing microorganisms in anoxic rice paddy soil by ¹³C-acetate probing. ISME J. 4, 267–278 (2010).CAS 
PubMed 
Article 

Google Scholar 
Kawaichi, S. et al. Ardenticatena maritima gen. nov., sp. nov., a ferric iron- and nitrate-reducing bacterium of the phylum ‘Chloroflexi’ isolated from an iron-rich coastal hydrothermal field, and description of Ardenticatenia classis nov. Int. J. Sys. Evol. Microbiol. 63, 2992–3002 (2013).CAS 
Article 

Google Scholar 
Podosokorskaya, O. A. et al. Characterization of Melioribacter roseus gen. nov., sp. nov., a novel facultatively anaerobic thermophilic cellulolytic bacterium from the class Ignavibacteria, and a proposal of a novel bacterial phylum Ignavibacteriae. Environ. Microbiol. 15, 1759–1771 (2013).CAS 
PubMed 
Article 

Google Scholar 
Yoon, S. et al. Nitrous oxide reduction kinetics distinguish bacteria harboring clade I nosz from those harboring clade II NosZ. Appl. Environ. Microbiol. 82, 3793–3800 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Maher, B. A. & Taylor, R. M. Formation of ultrafine-grained magnetite in soils. Nature 336, 368–370 (1988).CAS 
Article 

Google Scholar 
Sanchez, P. A. Properties and Management of Soils in the Tropics (Wiley, 1976).White, A. F. et al. Chemical weathering in a tropical watershed, Luquillo Mountains, Puerto Rico: I. Long-term versus short-term weathering fluxes. Geochim. Cosmochim. Acta 62, 209–226 (1998).CAS 
Article 

Google Scholar 
Hall, S. J., Liptzin, D., Buss, H. L., DeAngelis, K. & Silver, W. L. Drivers and patterns of iron redox cycling from surface to bedrock in a deep tropical forest soil: a new conceptual model. Biogeochemistry 130, 177–190 (2016).CAS 
Article 

Google Scholar 
Buchwald, C., Grabb, K., Hansel, C. M. & Wankel, S. D. Constraining the role of iron in environmental nitrogen transformations: dual stable isotope systematics of abiotic NO2− reduction by Fe(II) and its production of N2O. Geochim. Cosmochim. Acta 186, 1–12 (2016).CAS 
Article 

Google Scholar 
Grabb, K. C., Buchwald, C., Hansel, C. M. & Wankel, S. D. A dual nitrite isotopic investigation of chemodenitrification by mineral-associated Fe(II) and its production of nitrous oxide. Geochim. Cosmochim. Acta 196, 388–402 (2017).CAS 
Article 

Google Scholar 
Drewer, J. et al. Linking nitrous oxide and nitric oxide fluxes to microbial communities in tropical forest soils and oil palm plantations in Malaysia in laboratory incubations. Front. For. Glob. Change 3, 4 (2020).Article 

Google Scholar 
Yvon-Durocher, G., Jones, J. I., Trimmer, M., Woodward, G. & Montoya, J. M. Warming alters the metabolic balance of ecosystems. Phil. Trans. R. Soc. B 365, 2117–2126 (2010).PubMed 
PubMed Central 
Article 

Google Scholar 
Yvon-Durocher, G. et al. Reconciling the temperature dependence of respiration across timescales and ecosystem types. Nature 487, 472–476 (2012).CAS 
PubMed 
Article 

Google Scholar 
Jauhiainen, J., Kerojoki, O., Silvennoinen, H., Limin, S. & Vasander, H. Heterotrophic respiration in drained tropical peat is greatly affected by temperature – a passive ecosystem cooling experiment. Environ. Res. Lett. 9, 105013 (2014).Article 

Google Scholar 
Wang, S., Zhuang, Q., Lähteenoja, O., Draper, F. C. & Cadillo-Quiroz, H. Potential shift from a carbon sink to a source in Amazonian peatlands under a changing climate. Proc. Natl Acad. Sci. USA 115, 12407–12412 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Stumm, W. & Lee, G. F. Oxygenation of ferrous iron. Ind. Eng. Chem. 53, 143–146 (1961).CAS 
Article 

Google Scholar 
Theis, T. L. & Singer, P. C. Complexation of iron(II) by organic matter and its effect on iron(II) oxygenation. Environ. Sci. Technol. 8, 569–573 (1974).CAS 
Article 

Google Scholar 
Wan, X. et al. Complexation and reduction of iron by phenolic substances: implications for transport of dissolved Fe from peatlands to aquatic ecosystems and global iron cycling. Chem. Geol. 498, 128–138 (2018).CAS 
Article 

Google Scholar 
Daugherty, E. E., Gilbert, B., Nico, P. S. & Borch, T. Complexation and redox buffering of iron(II) by dissolved organic matter. Environ. Sci. Technol. 51, 11096–11104 (2017).CAS 
PubMed 
Article 

Google Scholar 
Prananto, J. A., Minasny, B., Comeau, L.-P., Rudiyanto, R. & Grace, P. Drainage increases CO2 and N2O emissions from tropical peat soils. Glob. Change Biol. 26, 4583–4600 (2020).Article 

Google Scholar 
Stirling, E., Fitzpatrick, R. W. & Mosley, L. Drought effects on wet soils in inland wetlands and peatlands. Earth Sci. Rev. 210, 103387 (2020).CAS 
Article 

Google Scholar 
Hodgkins, S. B. et al. Tropical peatland carbon storage linked to global latitudinal trends in peat recalcitrance. Nat. Commun. 9, 3640 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Gumbricht, T. et al. An expert system model for mapping tropical wetlands and peatlands reveals South America as the largest contributor. Glob. Change Biol. 23, 3581–3599 (2017).Article 

Google Scholar 
IPCC Climate Change 2021: The Physical Science Basis (eds Masson-Delmotte, V. et al.) (Cambridge Univ. Press, 2021).Babbin, A. R., Bianchi, D., Jayakumar, A. & Ward, B. B. Rapid nitrous oxide cycling in the suboxic ocean. Science 348, 1127–1129 (2015).CAS 
PubMed 
Article 

Google Scholar 
Hamilton, S. K. & Ostrom, N. E. Measurement of the stable isotope ratio of dissolved N2 in 15N tracer experiments. Limnol. Oceanogr. Methods 5, 233–240 (2007).CAS 
Article 

Google Scholar 
Ostrom, N. E., Gandhi, H., Trubl, G. & Murray, A. E. Chemodenitrification in the cryoecosystem of Lake Vida, Victoria Valley, Antarctica. Geobiology 14, 575–587 (2016).CAS 
PubMed 
Article 

Google Scholar 
Stumm, W. & Morgan, J. J. Aquatic Chemistry 3rd edn (John Wiley & Sons, 1996).Homyak, P. M., Kamiyama, M., Sickman, J. O. & Schimel, J. P. Acidity and organic matter promote abiotic nitric oxide production in drying soils. Glob. Change Biol. 23, 1735–1747 (2017).Article 

Google Scholar 
Henry, S., Bru, D., Stres, B., Hallet, S. & Philippot, L. Quantitative detection of the nosZ gene, encoding nitrous oxide reductase, and comparison of the abundances of 16S rRNA, narG, nirK, and nosZ genes in soils. Appl. Environ. Microbiol. 72, 5181–5189 (2006).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Jones, C. M., Graf, D. R., Bru, D., Philippot, L. & Hallin, S. The unaccounted yet abundant nitrous oxide-reducing microbial community: a potential nitrous oxide sink. ISME J. 7, 417–426 (2013).CAS 
PubMed 
Article 

Google Scholar 
Zhang, B. et al. A new primer set for clade I nosZ that recovers genes from a broader range of taxa. Biol. Fertil. Soils 57, 523–531 (2021).CAS 
Article 

Google Scholar 
Herbold, C. W. et al. A flexible and economical barcoding approach for highly multiplexed amplicon sequencing of diverse target genes. Front. Microbiol. 6, 8966 (2015).Article 

Google Scholar 
Edgar, R. C. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460–2461 (2010).CAS 
PubMed 
Article 

Google Scholar 
Edgar, R. C. UNOISE2: improved error-correction for Illumina 16S and ITS amplicon sequencing. Preprint at https://www.biorxiv.org/content/early/2016/10/15/081257 (2016).Wang, Q. et al. Ecological patterns of nifH genes in four terrestrial climatic zones explored with targeted metagenomics using Framebot, a new informatics tool. mBio 4, e00592-13 (2013).PubMed 
PubMed Central 
Article 

Google Scholar 
Buchfink, B., Xie, C. & Huson, D. H. Fast and sensitive protein alignment using DIAMOND. Nat. Methods 12, 59–60 (2015).CAS 
PubMed 
Article 

Google Scholar 
Fish, J. A. et al. FunGene: the functional gene pipeline and repository. Front. Microbiol. 4, 291 (2013).PubMed 
PubMed Central 
Article 

Google Scholar 
Huson, D. H. et al. MEGAN Community Edition – interactive exploration and analysis of large-scale microbiome sequencing data. PLoS Comput. Biol. 12, e1004957 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
Huson, D. H. et al. MEGAN-LR: new algorithms allow accurate binning and easy interactive exploration of metagenomic long reads and contigs. Biol. Direct 13, 6 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Ondov, B. D., Bergman, N. H. & Phillippy, A. M. Interactive metagenomic visualization in a Web browser. BMC Bioinformatics 12, 385 (2011).PubMed 
PubMed Central 
Article 

Google Scholar 
Kumar, S., Stecher, G., Li, M., Knyaz, C. & Tamura, K. MEGA X: molecular evolutionary genetics analysis across computing platforms. Mol. Biol. Evol. 35, 1547–1549 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar  More

Farmery, A. K., Hendrie, G. A., O’Kane, G., McManus, A. & Green, B. S. Sociodemographic variation in consumption patterns of sustainable and nutritious seafood in Australia. Front. Nutr. 5, 118. https://doi.org/10.3389/fnut.2018.00118 (2018).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Guillen, J. et al. Global seafood consumption footprint. Ambio 48, 111–122. https://doi.org/10.1007/s13280-018-1060-9 (2019).Article 
PubMed 

Google Scholar 
Norse, E. A. et al. Sustainability of deep-sea fisheries. Mar. Policy 36, 307–320. https://doi.org/10.1016/j.marpol.2011.06.008 (2012).Article 

Google Scholar 
Baco, A. R. et al. A synthesis of genetic connectivity in deep-sea fauna and implications for marine reserve design. Mol. Ecol. 25, 3276–3298. https://doi.org/10.1111/mec.13689 (2016).Article 
PubMed 

Google Scholar 
Victorero, L., Watling, L., Deng Palomares, M. L. & Nouvian, C. Out of sight, but within reach: A global history of bottom-trawled deep-sea fisheries from > 400 m depth. Front. Mar. Sci. 5, 98. https://doi.org/10.3389/fmars.2018.00098 (2018).Article 

Google Scholar 
Cowen, R. K. & Sponaugle, S. Larval dispersal and marine population connectivity. Ann. Rev. Mar. Sci. 1, 443–466. https://doi.org/10.1146/annurev.marine.010908.163757 (2009).Article 
PubMed 

Google Scholar 
Taylor, M. L. & Roterman, C. N. Invertebrate population genetics across Earth’s largest habitat: The deep-sea floor. Mol. Ecol. 26, 4872–4896. https://doi.org/10.1111/mec.14237 (2017).CAS 
Article 
PubMed 

Google Scholar 
Allendorf, F. W., England, P. R., Luikart, G., Ritchie, P. A. & Ryman, N. Genetic effects of harvest on wild animal populations. Trends Ecol. Evol. 23, 327–337. https://doi.org/10.1016/j.tree.2008.02.008 (2008).Article 
PubMed 

Google Scholar 
Carreras, C. et al. Population genomics of an endemic Mediterranean fish: Differentiation by fine scale dispersal and adaptation. Sci. Rep. 7, 43417. https://doi.org/10.1038/srep43417 (2017).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Coleman, F. C. & Williams, S. L. Overexploiting marine ecosystem engineers: Potential consequences for biodiversity. Trends Ecol. Evol. 17, 40–44. https://doi.org/10.1016/S0169-5347(01)02330-8 (2002).Article 

Google Scholar 
Neubauer, P., Jensen, O. P., Hutchings, J. A. & Baum, J. K. Resilience and recovery of overexploited marine populations. Science 340, 347–349. https://doi.org/10.1126/science.1230441 (2013).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Ovenden, J. R., Berry, O., Welch, D. J., Buckworth, R. C. & Dichmont, C. M. Ocean’s eleven: A critical evaluation of the role of population, evolutionary and molecular genetics in the management of wild fisheries. Fish Fish. 16, 125–159. https://doi.org/10.1111/faf.12052 (2015).Article 

Google Scholar 
Pinsky, M. L. & Palumbi, S. R. Meta-analysis reveals lower genetic diversity in overfished populations. Mol. Ecol. 23, 29–39. https://doi.org/10.1111/mec.12509 (2014).Article 
PubMed 

Google Scholar 
Sundqvist, L., Keenan, K., Zackrisson, M., Prodöhl, P. & Kleinhans, D. Directional genetic differentiation and relative migration. Ecol. Evol. 6, 3461–3475. https://doi.org/10.1002/ece3.2096 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Waples, R. S. et al. Guidelines for genetic data analysis. J. Cetac. Res. Manag. 18, 33–80 (2018).ADS 

Google Scholar 
Hauser, L., Adcock, G. J., Smith, P. J., Bernal Ramírez, J. H. & Carvalho, G. R. Loss of microsatellite diversity and low effective population size in an overexploited population of New Zealand snapper (Pagrus auratus). Proc. Natl. Acad. Sci. 99, 11742–11747. https://doi.org/10.1073/pnas.172242899 (2002).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Laikre, L., Palm, S. & Ryman, N. Genetic population structure of fishes: Implications for coastal zone management. AMBIO A J. Hum. Environ. 34, 111–119. https://doi.org/10.1579/0044-7447-34.2.111 (2005).Article 

Google Scholar 
Gaggiotti, O. E. Population genetic models of source–sink metapopulations. Theor. Popul. Biol. 50, 178–208. https://doi.org/10.1006/tpbi.1996.0028 (1996).CAS 
Article 
PubMed 
MATH 

Google Scholar 
Hughes, A. R., Inouye, B. D., Johnson, M. T. J., Underwood, N. & Vellend, M. Ecological consequences of genetic diversity. Ecol. Lett. 11, 609–623. https://doi.org/10.1111/j.1461-0248.2008.01179.x (2008).Article 
PubMed 

Google Scholar 
Bracco, A., Liu, G., Galaska, M. P., Quattrini, A. M. & Herrera, S. Integrating physical circulation models and genetic approaches to investigate population connectivity in deep-sea corals. J. Mar. Syst. 198, 103189. https://doi.org/10.1016/j.jmarsys.2019.103189 (2019).Article 

Google Scholar 
Liu, S.-Y.V., Hsin, Y.-C. & Cheng, Y.-R. Using particle tracking and genetic approaches to infer population connectivity in the deep-sea scleractinian coral Deltocyathus magnificus in the South China sea. Deep Sea Res. Part I 161, 103297. https://doi.org/10.1016/j.dsr.2020.103297 (2020).Article 

Google Scholar 
Dambach, J., Raupach, M. J., Leese, F., Schwarzer, J. & Engler, J. O. Ocean currents determine functional connectivity in an Antarctic deep-sea shrimp. Mar. Ecol. 37, 1336–1344. https://doi.org/10.1111/maec.12343 (2016).ADS 
CAS 
Article 

Google Scholar 
Selkoe, K. A., Henzler, C. M. & Gaines, S. D. Seascape genetics and the spatial ecology of marine populations. Fish Fish. 9, 363–377. https://doi.org/10.1111/j.1467-2979.2008.00300.x (2008).Article 

Google Scholar 
Yan, R.-J., Schnabel, K. E., Rowden, A. A., Guo, X.-Z. & Gardner, J. P. A. Population structure and genetic connectivity of squat lobsters (Munida Leach, 1820) associated with vulnerable marine ecosystems in the southwest Pacific Ocean. Front. Mar. Sci. https://doi.org/10.3389/fmars.2019.00791 (2020).Article 

Google Scholar 
Breusing, C. et al. Biophysical and population genetic models predict the presence of “phantom” stepping stones connecting Mid-Atlantic Ridge vent ecosystems. Curr. Biol. 26, 2257–2267. https://doi.org/10.1016/j.cub.2016.06.062 (2016).CAS 
Article 
PubMed 

Google Scholar 
Fisheries New Zealand. Fisheries Assessment: Scampi (SCI). https://fs.fish.govt.nz/Page.aspx?pk=113&dk=24443 (2017).Botsford, L. W. et al. Connectivity, sustainability, and yield: Bridging the gap between conventional fisheries management and marine protected areas. Rev. Fish Biol. Fish. 19, 69–95. https://doi.org/10.1007/s11160-008-9092-z (2009).Article 

Google Scholar 
NIWA. Annual Distribution of Scampi. Ministry for Primary Industries, New Zealand. https://mpi.maps.arcgis.com/home/item.html?id=97da6c1a912b45a8855bf741211f5911 (2016).Heasman, K. G. & Jeffs, A. G. Fecundity and potential juvenile production for aquaculture of the New Zealand scampi, Metanephrops challengeri (Balss, 1914) (Decapoda: Nephropidae). Aquaculture 511, 634184. https://doi.org/10.1016/j.aquaculture.2019.05.069 (2019).Article 

Google Scholar 
Smith, P. J. Allozyme variation in scampi (Metanephrops challengeri) fisheries around New Zealand. NZ J. Mar. Freshw. Res. 33, 491–497. https://doi.org/10.1080/00288330.1999.9516894 (1999).Article 

Google Scholar 
Berry, P. The biology of Nephrops andamanicus Wood-Mason (Decapoda, Reptantia). Report No. 22, 1–55 (South African Association for Marine Biological Research, Oceanographic Research Institute, Durban, South Africa, 1969).Major, R. N. & Jeffs, A. G. Orientation and food search behaviour of a deep sea lobster in turbulent versus laminar odour plumes. Helgol. Mar. Res. 71, 9. https://doi.org/10.1186/s10152-017-0489-8 (2017).Article 

Google Scholar 
Tuck, I. D., Parsons, D. M., Hartill, B. W. & Chiswell, S. M. Scampi (Metanephrops challengeri) emergence patterns and catchability. ICES J. Mar. Sci. 72, i199–i210. https://doi.org/10.1093/icesjms/fsu244 (2015).Article 

Google Scholar 
Chiswell, S. M. & Booth, J. D. Sources and sinks of larval settlement in Jasus edwardsii around New Zealand: Where do larvae come from and where do they go?. Mar. Ecol. Prog. Ser. 354, 201–217. https://doi.org/10.3354/meps07217 (2008).ADS 
Article 

Google Scholar 
Silva, C. N. S., Macdonald, H. S., Hadfield, M. G., Cryer, M. & Gardner, J. P. A. Ocean currents predict fine-scale genetic structure and source-sink dynamics in a marine invertebrate coastal fishery. ICES J. Mar. Sci. 76, 1007–1018. https://doi.org/10.1093/icesjms/fsy201 (2019).Article 

Google Scholar 
Singh, S. P., Groeneveld, J. C., Hart-Davis, M. G., Backeberg, B. C. & Willows-Munro, S. Seascape genetics of the spiny lobster Panulirus homarus in the Western Indian Ocean: Understanding how oceanographic features shape the genetic structure of species with high larval dispersal potential. Ecol. Evol. 8, 12221–12237. https://doi.org/10.1002/ece3.4684 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Singh, S. P., Groeneveld, J. C. & Willows-Munro, S. Between the current and the coast: Genetic connectivity in the spiny lobster Panulirus homarus rubellus, despite potential barriers to gene flow. Mar. Biol. 166, 36. https://doi.org/10.1007/s00227-019-3486-4 (2019).Article 

Google Scholar 
Thomas, L. & Bell, J. J. Testing the consistency of connectivity patterns for a widely dispersing marine species. Heredity 111, 345–354. https://doi.org/10.1038/hdy.2013.58 (2013).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Baeza, J. A., Holstein, D., Umaña-Castro, R. & Mejía-Ortíz, L. M. Population genetics and biophysical modeling inform metapopulation connectivity of the Caribbean king crab Maguimithrax spinosissimus. Mar. Ecol. Prog. Ser. 610, 83–97 (2019).ADS 
CAS 
Article 

Google Scholar 
Hedgecock, D., Barber, P. H. & Edmands, S. Genetic approaches to measuring connectivity. Oceanography 20, 70–79 (2007).Article 

Google Scholar 
Jahnke, M. & Jonsson, P. R. Biophysical models of dispersal contribute to seascape genetic analyses. Philos. Trans. R. Soc. B Biol. Sci. 377, 20210024. https://doi.org/10.1098/rstb.2021.0024 (2022).Article 

Google Scholar 
Sebastian, W. et al. Genomic investigations provide insights into the mechanisms of resilience to heterogeneous habitats of the Indian Ocean in a pelagic fish. Sci. Rep. 11, 20690. https://doi.org/10.1038/s41598-021-00129-5 (2021).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Lal, M. M., Southgate, P. C., Jerry, D. R., Bosserelle, C. & Zenger, K. R. A parallel population genomic and hydrodynamic approach to fishery management of highly-dispersive marine invertebrates: The case of the Fijian black-lip pearl oyster Pinctada margaritifera. PLoS ONE 11, e0161390. https://doi.org/10.1371/journal.pone.0161390 (2016).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Xu, T. et al. Hidden historical habitat-linked population divergence and contemporary gene flow of a deep-sea patellogastropod limpet. Mol. Biol. Evol. 38, 5640–5654. https://doi.org/10.1093/molbev/msab278 (2021).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
de Souza, J. M. A. C. et al. Moana Ocean Hindcast—A 25+ years simulation for New Zealand Waters using the ROMS v3.9 model. EGUsphere https://doi.org/10.5194/egusphere-2022-41 (2022).Norrie, C., Dunphy, B., Roughan, M., Weppe, S. & Lundquist, C. Spill-over from aquaculture may provide a larval subsidy for the restoration of mussel reefs. Aquac. Environ. Interact. 12, 231–249 (2020).Article 

Google Scholar 
Larsson, J. et al. Regional genetic differentiation in the blue mussel from the Baltic Sea area. Estuar. Coast. Shelf Sci. 195, 98–109. https://doi.org/10.1016/j.ecss.2016.06.016 (2017).ADS 
Article 

Google Scholar 
Nicolle, A. et al. Modelling larval dispersal of Pecten maximus in the English Channel: A tool for the spatial management of the stocks. ICES J. Mar. Sci. 74, 1812–1825. https://doi.org/10.1093/icesjms/fsw207 (2017).Article 

Google Scholar 
Hold, N. et al. Using biophysical modelling and population genetics for conservation and management of an exploited species, Pecten maximus L. Fish. Oceanogr. 30, 740–756. https://doi.org/10.1111/fog.12556 (2021).Article 

Google Scholar 
Truelove, N. K. et al. Biophysical connectivity explains population genetic structure in a highly dispersive marine species. Coral Reefs 36, 233–244. https://doi.org/10.1007/s00338-016-1516-y (2017).ADS 
Article 

Google Scholar 
Busch, K. et al. Population connectivity of fan-shaped sponge holobionts in the deep Cantabrian Sea. Deep Sea Res. Part I 167, 103427. https://doi.org/10.1016/j.dsr.2020.103427 (2021).Article 

Google Scholar 
Ross, P. M., Hogg, I. D., Pilditch, C. A. & Lundquist, C. J. Phylogeography of New Zealand’s coastal benthos. NZ J. Mar. Freshw. Res. 43, 1009–1027. https://doi.org/10.1080/00288330.2009.9626525 (2009).Article 

Google Scholar 
Tuck, I. D. Characterisation and a length-based assessment model for scampi (Metanephrops challengeri) at the Auckland Islands (SCI 6A). Report No. 2015/21, 160 (Ministry for Primary Industries, Wellington, 2015).Verry, A. J. F., Walton, K., Tuck, I. D. & Ritchie, P. A. Genetic structure and recent population expansion in the commercially harvested deepsea decapod, Metanephrops challengeri (Crustacea: Decapoda). NZ J. Mar. Freshw. Res. 54, 251–270. https://doi.org/10.1080/00288330.2019.1707696 (2020).CAS 
Article 

Google Scholar 
Selkoe, K. A. et al. A decade of seascape genetics: Contributions to basic and applied marine connectivity. Mar. Ecol. Prog. Ser. 554, 1–19. https://doi.org/10.3354/meps11792 (2016).ADS 
Article 

Google Scholar 
Hare, M. P. et al. Understanding and estimating effective population size for practical application in marine species management. Conserv. Biol. 25, 438–449. https://doi.org/10.1111/j.1523-1739.2010.01637.x (2011).Article 
PubMed 

Google Scholar 
Ashry, N. A. Plant biodiversity and biotechnology. In From Plant Genomics to Plant Biotechnology (eds Poltronieri, P. et al.) 205–222 (Woodhead Publishing, 2013).Chapter 

Google Scholar 
Sgrò, C. M., Lowe, A. J. & Hoffmann, A. A. Building evolutionary resilience for conserving biodiversity under climate change. Evol. Appl. 4, 326–337. https://doi.org/10.1111/j.1752-4571.2010.00157.x (2011).Article 
PubMed 

Google Scholar 
Kerr, L. A., Cadrin, S. X. & Secor, D. H. Simulation modelling as a tool for examining the consequences of spatial structure and connectivity on local and regional population dynamics. ICES J. Mar. Sci. 67, 1631–1639. https://doi.org/10.1093/icesjms/fsq053 (2010).Article 

Google Scholar 
Carroll, E. L. et al. Perturbation drives changing metapopulation dynamics in a top marine predator. Proc. R. Soc. B Biol. Sci. 287, 20200318. https://doi.org/10.1098/rspb.2020.0318 (2020).Article 

Google Scholar 
Chiswell, S. M., Bostock, H. C., Sutton, P. J. H. & Williams, M. J. M. Physical oceanography of the deep seas around New Zealand: A review. NZ J. Mar. Freshw. Res. 49, 286–317. https://doi.org/10.1080/00288330.2014.992918 (2015).Article 

Google Scholar 
Chiswell, S. M. & Roemmich, D. The East Cape Current and two eddies: A mechanism for larval retention?. NZ J. Mar. Freshw. Res. 32, 385–397. https://doi.org/10.1080/00288330.1998.9516833 (1998).Article 

Google Scholar 
Condie, S. & Condie, R. Retention of plankton within ocean eddies. Glob. Ecol. Biogeogr. 25, 1264–1277. https://doi.org/10.1111/geb.12485 (2016).Article 

Google Scholar 
Lesser, J. H. R. Phyllosoma larvae of Jasus edwardsii (Hutton) (Crustacea: Decapoda: Palinuridae) and their distribution off the east coast of the North Island, New Zealand. NZ J. Mar. Freshw. Res. 12, 357–370. https://doi.org/10.1080/00288330.1978.9515763 (1978).Article 

Google Scholar 
Kawecki, T. J. Ecological and evolutionary consequences of source-sink population dynamics. In Ecology, Genetics and Evolution of Metapopulations (eds Hanski, I. & Gaggiotti, O. E.) 387–414 (Academic Press, 2004).Chapter 

Google Scholar 
Figueira, W. F. & Crowder, L. B. Defining patch contribution in source-sink metapopulations: the importance of including dispersal and its relevance to marine systems. Popul. Ecol. 48, 215–224. https://doi.org/10.1007/s10144-006-0265-0 (2006).Article 

Google Scholar 
Heinrichs, J. A. et al. Recent advances and current challenges in applying source-sink theory to species conservation. Curr. Landsc. Ecol. Rep. 4, 51–60. https://doi.org/10.1007/s40823-019-00039-3 (2019).Article 

Google Scholar 
Hastings, A. & Botsford, L. W. Persistence of spatial populations depends on returning home. Proc. Natl. Acad. Sci. 103, 6067–6072. https://doi.org/10.1073/pnas.0506651103 (2006).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Heinrichs, J. A., Lawler, J. J. & Schumaker, N. H. Intrinsic and extrinsic drivers of source-sink dynamics. Ecol. Evol. 6, 892–904. https://doi.org/10.1002/ece3.2029 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Gilroy, J. J. & Edwards, D. P. Source-sink dynamics: A neglected problem for landscape-scale biodiversity conservation in the Tropics. Curr. Landsc. Ecol. Rep. 2, 51–60. https://doi.org/10.1007/s40823-017-0023-3 (2017).Article 

Google Scholar 
Lal, M. M., Bosserelle, C., Kishore, P. & Southgate, P. C. Understanding marine larval dispersal in a broadcast-spawning invertebrate: A dispersal modelling approach for optimising spat collection of the Fijian black-lip pearl oyster Pinctada margaritifera. PLoS ONE 15, e0234605. https://doi.org/10.1371/journal.pone.0234605 (2020).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Chassé, J. & Miller, R. J. Lobster larval transport in the southern Gulf of St. Lawrence. Fish. Oceanogr. 19, 319–338. https://doi.org/10.1111/j.1365-2419.2010.00548.x (2010).Article 

Google Scholar 
Lindegren, M., Andersen, K. H., Casini, M. & Neuenfeldt, S. A metacommunity perspective on source–sink dynamics and management: the Baltic Sea as a case study. Ecol. Appl. 24, 1820–1832. https://doi.org/10.1890/13-0566.1 (2014).Article 
PubMed 

Google Scholar 
Tuck, I. D. et al. Estimating the abundance of scampi in SCI 6A (Auckland Islands) in 2013. Report No. 2015/10, 48 (Ministry for Primary Industries, 2015).Brierley, A. S. & Kingsford, M. J. Impacts of climate change on marine organisms and ecosystems. Curr. Biol. 19, R602–R614. https://doi.org/10.1016/j.cub.2009.05.046 (2009).CAS 
Article 
PubMed 

Google Scholar 
Caesar, L., Rahmstorf, S., Robinson, A., Feulner, G. & Saba, V. Observed fingerprint of a weakening Atlantic Ocean overturning circulation. Nature 556, 191–196. https://doi.org/10.1038/s41586-018-0006-5 (2018).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Thornalley, D. J. R. et al. Anomalously weak Labrador Sea convection and Atlantic overturning during the past 150 years. Nature 556, 227–230. https://doi.org/10.1038/s41586-018-0007-4 (2018).ADS 
CAS 
Article 
PubMed 

Google Scholar 
van Gennip, S. J. et al. Going with the flow: The role of ocean circulation in global marine ecosystems under a changing climate. Glob. Change Biol. 23, 2602–2617. https://doi.org/10.1111/gcb.13586 (2017).ADS 
Article 

Google Scholar 
Bashevkin, S. M. et al. Larval dispersal in a changing ocean with an emphasis on upwelling regions. Ecosphere 11, e03015. https://doi.org/10.1002/ecs2.3015 (2020).Article 

Google Scholar 
Gerber, L. R., Mancha-Cisneros, M. D. M., O’Connor, M. I. & Selig, E. R. Climate change impacts on connectivity in the ocean: Implications for conservation. Ecosphere 5, 1–18. https://doi.org/10.1890/es13-00336.1 (2014).Article 

Google Scholar 
Hoegh-Gulderg, O. & Pearse, J. Temperature, food availability, and the development of marine invertebrate larvae. Am. Zool. 35, 415–425. https://doi.org/10.1093/icb/35.4.415 (1995).Article 

Google Scholar 
O’Connor, M. I. et al. Temperature control of larval dispersal and the implications for marine ecology, evolution, and conservation. Proc. Natl. Acad. Sci. USA 104, 1266–1271. https://doi.org/10.1073/pnas.0603422104 (2007).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Cetina-Heredia, P., Roughan, M., van Sebille, E., Feng, M. & Coleman, M. A. Strengthened currents override the effect of warming on lobster larval dispersal and survival. Glob. Change Biol. 21, 4377–4386. https://doi.org/10.1111/gcb.13063 (2015).ADS 
Article 

Google Scholar 
Borja, A. et al. Past and future grand challenges in marine ecosystem ecology. Front. Mar. Sci. https://doi.org/10.3389/fmars.2020.00362 (2020).Article 

Google Scholar 
Ogilvie, S. et al. Mātauranga Māori driving innovation in the New Zealand scampi fishery. NZ J. Mar. Freshw. Res. 52, 590–602. https://doi.org/10.1080/00288330.2018.1532441 (2018).Article 

Google Scholar 
Andrews, S. FastQC: A quality control tool for high throughput sequence data v. 0.11.7 (Babraham Bioinformatics, 2010). http://www.bioinformatics.babraham.ac.uk/projects/fastqc.Rochette, N. C., Rivera-Colón, A. G. & Catchen, J. M. Stacks 2: Analytical methods for paired-end sequencing improve RADseq-based population genomics. Mol. Ecol. 28, 4737–4754. https://doi.org/10.1111/mec.15253 (2019).CAS 
Article 
PubMed 

Google Scholar 
Danecek, P. et al. The variant call format and VCFtools. Bioinformatics 27, 2156–2158. https://doi.org/10.1093/bioinformatics/btr330 (2011).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing v. 4.1.0 (R Studio v1.4.1106) (R Foundation for Statistical Computing, Vienna, Austria, 2021). https://www.R-project.org/.Díaz-Arce, N. & Rodríguez-Ezpeleta, N. Selecting RAD-seq data analysis parameters for population genetics: The more the better?. Front. Genet. 10, 533. https://doi.org/10.3389/fgene.2019.00533 (2019).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Potapov, V. & Ong, J. L. Examining sources of error in PCR by single-molecule sequencing. PLoS ONE 12, e0169774. https://doi.org/10.1371/journal.pone.0169774 (2017).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Goudet, J. & Jombart, T. hierfstat: Estimation and Tests of Hierarchical F-Statistics v. 0.04-22 (Comprehensive R Archive Network (CRAN), 2015). https://CRAN.R-project.org/package=hierfstat.Nei, M. Molecular Evolutionary Genetics (Columbia University Press, 1987).Book 

Google Scholar 
Nei, M. & Chesser, R. K. Estimation of fixation indices and gene diversities. Ann. Hum. Genet. 47, 253–259. https://doi.org/10.1111/j.1469-1809.1983.tb00993.x (1983).CAS 
Article 
PubMed 
MATH 

Google Scholar 
Archer, F. I., Adams, P. E. & Schneiders, B. B. stratag: An R package for manipulating, summarizing and analysing population genetic data. Mol. Ecol. Resour. 17, 5–11. https://doi.org/10.1111/1755-0998.12559 (2017).CAS 
Article 
PubMed 

Google Scholar 
Kamvar, Z. N., Tabima, J. F. & Grünwald, N. J. Poppr: An R package for genetic analysis of populations with clonal, partially clonal, and/or sexual reproduction. PeerJ 2, e281. https://doi.org/10.7717/peerj.281 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
Kamvar, Z. N., Brooks, J. C. & Grünwald, N. J. Novel R tools for analysis of genome-wide population genetic data with emphasis on clonality. Front. Genet. 6, 208. https://doi.org/10.3389/fgene.2015.00208 (2015).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Jombart, T. adegenet: A R package for the multivariate analysis of genetic markers. Bioinformatics 24, 1403–1405. https://doi.org/10.1093/bioinformatics/btn129 (2008).CAS 
Article 
PubMed 

Google Scholar 
Jombart, T. & Ahmed, I. adegenet 1.3–1: New tools for the analysis of genome-wide SNP data. Bioinformatics 27, 3070–3071. https://doi.org/10.1093/bioinformatics/btr521 (2011).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Miller, J. M., Cullingham, C. I. & Peery, R. M. The influence of a priori grouping on inference of genetic clusters: Simulation study and literature review of the DAPC method. Heredity 125, 269–280. https://doi.org/10.1038/s41437-020-0348-2 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Keenan, K., McGinnity, P., Cross, T. F., Crozier, W. W. & Prodöhl, P. A. diveRsity: An R package for the estimation and exploration of population genetics parameters and their associated errors. Methods Ecol. Evol. 4, 782–788. https://doi.org/10.1111/2041-210x.12067 (2013).Article 

Google Scholar 
Nei, M. Analysis of gene diversity in subdivided populations. Proc. Natl. Acad. Sci. 70, 3321–3323. https://doi.org/10.1073/pnas.70.12.3321 (1973).ADS 
CAS 
Article 
PubMed 
PubMed Central 
MATH 

Google Scholar 
Dagestad, K. F., Röhrs, J., Breivik, Ø. & Ådlandsvik, B. OpenDrift v1.0: A generic framework for trajectory modelling. Geosci. Model Dev. 11, 1405–1420. https://doi.org/10.5194/gmd-11-1405-2018 (2018).ADS 
Article 

Google Scholar 
Jeffs, A., Daniels, C. & Heasman, K. In Fisheries and Aquaculture: Natural History of Crustacea, Vol. 9 (eds Lovrich, G. & Thiel, M.) 285–311 (Oxford University Press, 2020).Lundquist, C. J., Oldman, J. W. & Lewis, M. J. Predicting suitability of cockle Austrovenus stutchburyi restoration sites using hydrodynamic models of larval dispersal. NZ J. Mar. Freshw. Res. 43, 735–748. https://doi.org/10.1080/00288330909510038 (2009).Article 

Google Scholar 
Lundquist, C. J., Thrush, S. F., Oldman, J. W. & Senior, A. K. Limited transport and recolonization potential in shallow tidal estuaries. Limnol. Oceanogr. 49, 386–395. https://doi.org/10.4319/lo.2004.49.2.0386 (2004).ADS 
Article 

Google Scholar 
Okubo, A. & Ebbesmeyer, C. C. Determination of vorticity, divergence, and deformation rates from analysis of drogue observations. Deep-Sea Res. Oceanogr. Abstr. 23, 349–352. https://doi.org/10.1016/0011-7471(76)90875-5 (1976).ADS 
Article 

Google Scholar 
Pierce, D. ncdf4: Interface to Unidata netCDF (Version 4 or Earlier) Format Data Files v. 1.17 (Comprehensive R Archive Network (CRAN), 2019). https://CRAN.R-project.org/package=ncdf4.Coelho, S. C. C., Gherardi, D. F. M., Gouveia, M. B. & Kitahara, M. V. Western boundary currents drive sun-coral (Tubastraea spp.) coastal invasion from oil platforms. Sci. Rep. 12, 5286. https://doi.org/10.1038/s41598-022-09269-8 (2022).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Demmer, J. et al. The role of wind in controlling the connectivity of blue mussels (Mytilus edulis L.) populations. Mov. Ecol. 10, 3. https://doi.org/10.1186/s40462-022-00301-0 (2022).Article 
PubMed 
PubMed Central 

Google Scholar 
Atalah, J., South, P. M., Briscoe, D. K. & Vennell, R. Inferring parental areas of juvenile mussels using hydrodynamic modelling. Aquaculture 555, 738227. https://doi.org/10.1016/j.aquaculture.2022.738227 (2022).Article 

Google Scholar 
McGeady, R., Lordan, C. & Power, A. M. Long-term interannual variability in larval dispersal and connectivity of the Norway lobster (Nephrops norvegicus) around Ireland: When supply-side matters. Fish. Oceanogr. 31, 255–270. https://doi.org/10.1111/fog.12576 (2022).Article 

Google Scholar 
Pante, E. & Simon-Bouhet, B. marmap: A package for importing, plotting and analyzing bathymetric and topographic data in R. PLoS ONE 8, e73051. https://doi.org/10.1371/journal.pone.0073051 (2013).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2016).MATH 

Google Scholar 
Becker, R. A., Wilks, A. R. & Brownrigg, R. mapdata: Extra Map Databases v. 2.3.0 (Comprehensive R Archive Network (CRAN), 2018). https://CRAN.R-project.org/package=mapdata.McIlroy, D., Brownrigg, R., Minka, T. P. & Bivan, R. mapproj: Map Projections v. 1.2.7 (Comprehensive R Archive Network (CRAN), 2020). https://CRAN.R-project.org/package=mapproj.South, A. rnaturalearth: World Map Data from Natural Earth v. 0.1.0 (Comprehensive R Archive Network (CRAN), 2017). https://CRAN.R-project.org/package=rnaturalearth. More