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    Flickering flash signals and mate recognition in the Asian firefly, Aquatica lateralis

    Flash recordingAll field recording and experiments were performed at the paddy field in the Northern Chita Peninsula, Aichi Prefecture, central Japan, in June and July between 2003 and 2016. The ambient temperature at the firefly’s active period was measured using a thermometer. The flashes were recorded with a digital video camera (NV-GS-400, Panasonic, Japan) mounted on a tripod at a height of 30–50 cm from ground and a distance of 1.0–1.5 m away from the specimen. Isolated specimens were selected for recording to exclude the background light from other nontarget specimens. When another specimen appeared near the target specimen, the video recording was cancelled. When a female copulated during video recording in the field, her flashes until 1 min before copulation were regarded as those of a ‘receptive female’. To record the flashes of a ‘mated female’, the female specimens already mated were prepared in aquariums (because virgin and mated females cannot be distinguished in the field): the eggs were obtained from wild female specimens collected one year before at the same field and reared to adults; immediately after emergence the virgin female was confined in a small container with two cultured males for two nights to facilitate copulation. As the parents of the reared specimens were collected from the observation field (same genetic background), the rearing temperature was almost the same as that of the natural field, the emergence period of the cultured specimens overlapped with that of the natural population, the adult body sizes of the reared and natural specimens were indistinguishable, and the flash pattern of the cultured mated females was indistinguishable from that of the wild (potentially) mated females. Thus, we believe that there was no influence of different rearing environments, i.e., the flash behavior of the cultured mated female specimens is expected to be substantially the same as that of wild mated female specimens. To distinguish them from wild (potentially) mated females, the elytra of cultured mated females were marked with colored ink before placing them in the field, and after three days, the flashes of ink-marked specimens were recorded. Of note, we never observed male attraction and copulation in any of the mated females used for field observation; thus, the mated females were unreceptive.Waveform analysisSequential still images were captured from video files at 30 frames per second using VirtualDub (GPL), and then the light intensities in the images were qualified (8-bit linear gray scaling from black to white at 0–255) using ImageJ software. In this study, we defined ‘flash’ as a luminescent waveform from baseline to baseline and ‘flickering’ as fluctuation above baseline in a single flash. The waveforms containing a saturated signal (255, white) were omitted. The waveforms of the maximum signal value lower than 50 were also omitted because of the difficulty in separating signal and noise. Approximately 10–90 waveforms per individual were analyzed; thus, the effect of the occasional interruption of the flash recording by the specimen’s movement and/or vegetation swinging between the specimen and the video camera is statistically ignorable. FD is defined as the time interval between the beginning and the end of a flash (Fig. S1). Flicker intensity (FI) was defined as$${text{FI}} = left{ {begin{array}{*{20}l} {mathop {max }limits_{1 le i le n} left( {frac{{{text{min}}left( {p_{i} ,p_{i + 1} } right) – t_{i} }}{{min left( {p_{i} , p_{i + 1} } right) + t_{i} }}} right)} hfill & {{text{if}} , n ge 1} hfill \ 0 hfill & {{text{if}} , n = 0} hfill \ end{array} } right.$$where p, t, and n denote the peak and the trough (local extrema) in the waveform of a flash and the number of toughs in the flash, respectively (Fig. S1). In total, we measured the FD and FI values of 347, 94, and 355 waveforms from 13 sedentary males, 7 receptive females, and 8 mated females, respectively. We did not consider the flash brightness as a factor because the measured value of the light intensity depends largely on the distance between the light source and the detector; thus, the actual brightness of the lantern cannot be practically measured in the field.e-FireflyFor male attraction experiments, we built an electronic LED device, the e-firefly, to generate patterned flashes with various FDs and FIs using a chip LED (green type, λmax = 568 nm, Everlight Electronics, Taiwan; Figs. S2 and S3) with a microcontroller PIC16F628A (Microchip Technology, USA) (see Figs. S4-S5). An example of the program for the microcontroller is shown in Supplementary Data S1. The brightness was constant in all programs. Flickering frequency ranged between 5–12 Hz, which corresponds to that of sedentary male flashes (approximately 10 Hz)15. To prevent direct access of the attracted specimen to the light source, the chip LED was covered by a steel net painted green (see Fig. S2). For flying male attraction experiments, when the male landed within a 100-mm distance from the e-firefly, we judged the attraction to be a success; otherwise, it was a failure. For sedentary male attraction experiments, the e-firefly was placed 200–300 mm away from the sedentary male. When the approaching male touched the steel net covering the e-firefly, to warrant a positive approach, we measured the time the male remained on the net. If the male did not move away from the net for more than 2 min, we judged the attraction to be a success (strict criterion for judgment); otherwise, it was a failure.Spectral measurementThe luminescence spectra of e-firefly and A. lateralis were measured using a Flame-S spectrophotometer (Ocean Insight, USA). The living A. lateralis specimens were anesthetized on ice and frozen at − 20 °C until use. The lantern started luminescence by thawing at room temperature, and the spectrum was measured during luminescence (within 5 min).Statistical analysisFirst, we considered a discriminant analysis using a logistic regression model that discriminates between receptive females and others in the observational data. We fitted several models with combinations of FD and FI, quadratic terms of FD and FI (FD2, FI2), interaction of FD and FI (FD (times) FI), and temperature (T) as explanatory variables. Based on Akaike’s information criteria (AIC) values and model simplicity, we chose the logistic regression model with FD, FI, FD2 and T as explanatory variables. Let (p)(({varvec{x}})) denote the conditional probability that a flash is from a receptive female given ({varvec{x}}=left(mathrm{FD},mathrm{ FI},mathrm{ T}right)) and (widehat{p})(({varvec{x}})) denote its estimate. The coefficients of the logistic regression model are estimated as follows.
    [Model for the observational data with temperature (T)]
    $$begin{gathered} {text{log}}frac{{hat{p}}}{{1 – hat{p}}} = begin{array}{*{20}l} { – 32.26 + 69.69 times FD – 43.47 times FI – 76.63 times FD^{2} + 0.87 times T} hfill \ {~quad left( {6.50} right)quad quad left( {15.37} right)quad quad quad left( {8.56} right)quad quad quad quad left( {17.44} right)quad quad quad left( {0.19} right)~~} hfill \ end{array} hfill \ quad {text{AIC: 84}}{text{.75}} hfill \ end{gathered}$$[Model for the observational data without temperature (T)]$$begin{gathered} {text{log}}frac{{hat{p}}}{{1 – hat{p}}} = begin{array}{*{20}l} { – 7.69~ + 47.57 times FD~ – 38.29 times FI~ – 52.86 times FD^{2} ~} hfill \ {~;left( {1.86} right)quad quad left( {9.68} right)quad quad quad left( {7.08} right)quad quad quad quad left( {11.38} right)~~} hfill \ end{array} hfill \ quad {text{AIC: 114}}{text{.89}} hfill \ end{gathered}$$where values in parentheses indicate standard deviations. The same applies hereafter. Temperature (T) is included in the model not because it affects the occurrence of receptive females but because it affects the FD and/or FI of receptive females. The AIC value increased by 30, which is substantial, when temperature was excluded from the model.Figure 2 shows the FD and FI of each flash from receptive females, mated females and males with the discriminant boundaries of receptive females from others for (p=0.5).We next considered a discriminant analysis for the experimental data. Let ({q}^{f}({varvec{x}})) denote the conditional probability that a flying male is attracted to a flash of ({varvec{x}}=left(mathrm{FD},mathrm{ FI},mathrm{ T}right)) and lands, and ({widehat{q}}^{f}({varvec{x}})) denote its estimate. Among several models we fit, the smallest AIC value is attained by the logistic regression model with FD, FI and T as explanatory variables, but the AIC is not much different from the model with FD and FI only.
    [Model for flying males with temperature (T)]
    $$begin{gathered} {text{log}}frac{{hat{q}^{f} }}{{1 – hat{q}^{f} }} = begin{array}{*{20}l} { – 0.74~~ – 2.42 times FD – 16.82 times FI + 0.31 times T} hfill \ {~;left( {4.01} right)quad quad left( {0.83} right)quad quad quad left( {4.88} right)quad quad quad quad left( {0.20} right)~} hfill \ end{array} hfill \ quad {text{AIC}}:66.96 hfill \ end{gathered}$$

    [Model for flying males without temperature (T)]
    $$begin{gathered} {text{log}}frac{{hat{q}^{f} }}{{1 – hat{q}^{f} }} = begin{array}{*{20}l} { – 5.36~ – 1.72 times FD – 13.69 times FI} hfill \ {~;left( {1.49} right)quad quad left( {0.63} right)~quad quad left( {4.09} right)~~} hfill \ end{array} hfill \ quad {text{AIC}}:67.61 hfill \ end{gathered}$$
    For sedentary males, the model with the smallest AIC value includes all the quadratic terms of FI and FD but not temperature. Let ({q}^{s}({varvec{x}})) denote the conditional probability that a sedentary male is attracted to a flash of ({varvec{x}}=left(mathrm{FD},mathrm{ FI},mathrm{ T}right)) and ({widehat{q}}^{s}left({varvec{x}}right)) denote its estimate. The logistic regression model for ({q}^{s}({varvec{x}})) with the best AIC value is given as follows.
    [Model for sedentary males]
    $${text{log}}frac{{hat{q}~^{s} }}{{1 – hat{q}~^{s} }} = begin{array}{*{20}l} { – 0.68~ + 7.84 times FD~ + 48.17 times FI – 5.35 times FD^{2} – 166.70 times FI^{2} – 65.67 times FD times FI} hfill \ {;left( {0.97} right)quad quad quad left( {2.99} right)quad quad quad left( {17.74} right)quad quad quad left( {1.74} right)quad quad quad quad left( {72.34} right)quad quad quad quad left( {17.67} right)~} hfill \ end{array}$$
    Figure 3 shows successes and failures of attraction of flying males on the left and sedentary males on the right with estimated discriminant boundaries.Let us now estimate probabilities that a flying male is attracted and lands or a sedentary male is attracted to a flash when a flash is from a receptive female or when a flash is either from a sedentary male or mated female. The probability that a flying male is attracted and lands when a flash is from a receptive female is a conditional probability and is expressed as follows.$$begin{aligned} Pleft(left.begin{array}{*{20}c} {text{Flying male}} \ {text{is attracted}} \ end{array} right|begin{array}{*{20}c} {text{Receptive }} \ {{text{female}}} \ end{array} right) & = frac{{Pleft( {begin{array}{*{20}c} {text{Flying male}} \ {text{is attracted}} \ end{array} {text{ and }}begin{array}{*{20}c} {text{Receptive }} \ {{text{female}}} \ end{array} } right) }}{{Pleft( {begin{array}{*{20}c} {{text{Receptive}}} \ {{text{female}}} \ end{array} } right)}}, \ Pleft( {begin{array}{*{20}c} {{text{Receptive}}} \ {{text{female}}} \ end{array} } right) & = mathop int_{Omega } Pleft(left. begin{array}{*{20}c} {{text{Receptive}}} \ {{text{female}}} \ end{array} right|{varvec{x}} right)fleft( {varvec{x}} right)d{varvec{x}} = mathop int_{Omega }pleft( {varvec{x}} right) fleft( {varvec{x}} right)d{varvec{x}} hspace{5mm}{text{and}} \ Pleft( {begin{array}{*{20}c} {text{Flying male}} \ {text{is attracted}} \ end{array} {text{ and }}begin{array}{*{20}c} {text{Receptive }} \ {{text{female}}} \ end{array} } right) & = mathop int_{Omega } Pleft(left. begin{array}{*{20}c} {{text{Receptive}}} \ {{text{female}}} \ end{array} right|varvec{x} right)Pleft(left. begin{array}{*{20}c} {text{Flying male}} \ {text{is attracted}} \ end{array} right|{varvec{x}} right)fleft( {varvec{x}} right)d{varvec{x}} \ & = mathop int_{Omega } pleft( varvec{x} right)q^{f} left( {varvec{x}} right)fleft( {varvec{x}} right)d{varvec{x}}mathbf{.} \ end{aligned}$$Integrals are taken over the domain (Omega) of ({varvec{x}}=(FD, FI, T)) of all females and males, and (f({varvec{x}})) is the joint density function of ({varvec{x}}.) Because (f({varvec{x}})) is unknown, we use the empirical distribution of the observational data, and conditional probabilities given ({varvec{x}}) are replaced with their estimates by logistic regression models. Let ({{varvec{x}}}_{i}=left(F{D}_{i}, F{I}_{i}, {T}_{i}right), i=mathrm{1,2},dots N) denote the (i) th observation in the observational data. The estimates of probabilities are given as follows:$$begin{aligned} hat{P}left( {begin{array}{*{20}c} {{text{Receptive}}} \ {{text{female}}} \ end{array} }right) & = frac{1}{N}mathop sum limits_{i = 1}^{n} hat{p}left( {{varvec{x}}_{i} } right) hspace{15mm} {text{and}} \ hat{P}left( {begin{array}{*{20}c} {text{Flying male}} \ {text{is attracted}} \ end{array} {text{ and }}begin{array}{*{20}c} {text{Receptive }} \ {{text{female}}} \ end{array} } right) & = frac{1}{N}mathop sum limits_{i = 1}^{n} hat{p}left( {{varvec{x}}_{i} } right) hat{q}^{f} left( {{varvec{x}}_{i} } right). \ end{aligned}$$Thus,$$hat{P}left( left. begin{array}{*{20}c} {text{Flying male}} \ {text{is attracted}} \ end{array} right| begin{array}{*{20}c} {text{Receptive }} \ {text{female}} \ end{array} right) = frac{{mathop sum nolimits_{i = 1}^{n} hat{p}left( {{varvec{x}}_{i} } right) hat{q}^{f} left( {{varvec{x}}_{i} } right)}}{{mathop sum nolimits_{i = 1}^{n}hat{p}left(varvec{x}_i right)}}.$$Similarly, we have$$begin{aligned} hat{P}left( left.begin{array}{*{20}c} {text{Flying male}} \ {text{is attracted}} \ end{array}right| {text{Others}} right) & = frac{{mathop sum nolimits_{i = 1}^{n} (1 – hat{p}left( {{varvec{x}}_{i} } right)) hat{q}^{f} left( {{varvec{x}}_{i} } right)}}{{mathop sum nolimits_{i = 1}^{n} (1 – hat{p}left( {{varvec{x}}_{i} } right))}} \ hat{P}left( left. begin{array}{*{20}c} {text{Sedentary male}} \ {text{is attracted}} \ end{array} right| begin{array}{*{20}c} {text{Receptive }} \ {text{female}} \ end{array} right)& = frac{{mathop sum nolimits_{i = 1}^{n} hat{p}left( {{varvec{x}}_{i} } right) hat{q}^{s} left( {{varvec{x}}_{i} } right)}}{{mathop sum nolimits_{i = 1}^{n} hat{p}left( varvec{x}_{i} right)}}hspace{15mm} {text{ and}} \hat{P}left(left. begin{array}{*{20}c} {text{Sedentary male}} \ {text{is attracted}} \ end{array}right| {text{Others}} right) & = frac{{mathop sum nolimits_{i = 1}^{n} left( {1 – hat{p}left( varvec{x}_{i} right)} right) hat{q}^{s} left( {varvec{x}_{i} } right)}}{mathop sum nolimits_{i = 1}^{n} left( {1 – hat{p}left( varvec{x}_{i} right)} right)} . \ end{aligned}$$The estimated probabilities are shown in Table 1.Table 1 Estimated probabilities of a flying male and a sedentary male being attracted to flashes from a receptive female and from others.Full size table More

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    Ecological niche model transferability of the white star apple (Chrysophyllum albidum G. Don) in the context of climate and global changes

    IPBES (2019): Summary for policymakers of the global assessment report on biodiversity and ecosystem services of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services. S. Díaz, J. Settele, E. S. Brondízio E.S., H. T. Ngo, M. Guèze, J. Agard, A. Arneth, P. Balvanera, K. A. Brauman, S. H. M. Butchart, K. M. A. Chan, L. A. Garibaldi, K. Ichii, J. Liu, S. M. Subramanian, G. F. Midgley, P. Miloslavich, Z. Molnár, D. Obura, A. Pfaff, S. Polasky, A. Purvis, J. Razzaque, B. Reyers, R. Roy Chowdhury, Y. J. Shin, I. J. Visseren-Hamakers, K. J. Willis, and C. N. Zayas (eds.). IPBES secretariat, Bonn, Germany. 56 p.FAO. in Global Forest Resources Assessment 2020: Main report. Rome. https://doi.org/10.4060/ca9825en (2020).Millennium Ecosystem Assessment (MA). Ecosystems and Human Well-Being: Synthesis. Island Press, Washington (2005)CBD. Considerations for Implementing International Standards and Codes of Conduct in National Invasive Species. Strategies and Plans. CBD (2011).Semper-Pascual, A. et al. Using occupancy models to assess the direct and indirect impacts of agricultural expansion on species’ populations. Biodivers. Conserv. 29, 3669–3688 (2020).
    Google Scholar 
    IPCC. Provisional State of the Global Climate. 2022. https://storymaps.arcgis.com/stories/5417cd9148c248c0985a5b6d028b0277, Accessed 23rd December 2022.Nunez, S. & Alkemade, R. Exploring interaction effects from mechanisms between climate and land-use changes and the projected consequences on biodiversity. Biodivers. Conserv. 30, 3685–3696 (2021).
    Google Scholar 
    Liu, C., White, M. & Newell, G. Measuring and comparing the accuracy of species distribution models with presence absence data. Ecography 34, 232–243. https://doi.org/10.1111/j.1600-0587.2010.06354.x (2011).Article 
    CAS 

    Google Scholar 
    Hao, T., Elith, J., Lahoz-Monfort, J. J. & Guillera-Arroita, G. Testing whether ensemble modelling is advantageous for maximising predictive performance of species distribution models. Ecography 43, 549–558. https://doi.org/10.1111/ecog.04890 (2020).Article 

    Google Scholar 
    Pearson, G. R., Raxworthy, J. C., Nakamura, M. & Peterson, A. T. Predicting species distributions from small numbers of occurrence records: A test case using cryptic geckos in Madagascar. J. Biogeogr 34, 102–117 (2007).
    Google Scholar 
    Thuiller, W. et al. Niche-based modelling as a tool for predicting the risk of alien plant invasions at a global scale. Glob. Chang. Biol. 11, 2234–2250 (2005).ADS 

    Google Scholar 
    He, Y. et al. Predicting potential global distribution and risk regions for potato cyst nematodes (Globodera rostochiensis and Globodera pallida). Sci. Rep. 12(1), 1–10 (2022).ADS 
    CAS 

    Google Scholar 
    Elith, J., Kearney, M. & Phillips, S. The art of modelling range-shifting species. Methods Ecol. Evol. 1, 330–342 (2010).
    Google Scholar 
    Ashraf, U., Chaudhry, M. N. & Peterson, A. T. Ecological niche models of biotic interactions predict increasing pest risk to olive cultivars with changing climate. Ecosphere 12, e03714. https://doi.org/10.1002/ecs2.3714 (2021).Article 

    Google Scholar 
    Elith, J. et al. A statistical explanation of MaxEnt for ecologists. Divers. Distrib. 17, 43–57 (2011).
    Google Scholar 
    Ganglo, J. C. et al. Ecological niche modeling and strategies for the conservation of Dialium guineense Willd. (Black velvet) in West Africa. Int. J. Biodivers. Conserv. 9, 373–388 (2017).
    Google Scholar 
    Djotan, A. K. G. et al. How far can climate changes help to conserve and restore Garcinia kola Heckel, an extinct species in the wild in Benin (West Africa). Int. J. Biodivers. Conserv. 10, 203–213 (2018).
    Google Scholar 
    Kakpo, S. B. et al. Spatial distribution and impacts of climate change on Milicia excelsa in Benin, West Africa. J. For. Res. 32, 143–150. https://doi.org/10.1007/s11676-019-01069-7 (2021).Article 

    Google Scholar 
    Jung, M. et al. A global map of terrestrial habitat types. Sci. Data 7(1), 1–8 (2020).MathSciNet 

    Google Scholar 
    Poor, E. E., Scheick, B. K. & Mullinax, J. M. Multiscale consensus habitat modeling for landscape level conservation prioritization. Sci. Rep. 10(1), 1–13 (2020).
    Google Scholar 
    Schüßler, D., Mantilla-Contreras, J., Stadtmann, R., Ratsimbazafy, J. H. & Radespiel, U. Identification of crucial stepping stone habitats for biodiversity conservation in northeastern Madagascar using remote sensing and comparative predictive modeling. Biodivers. Conserv. 29, 2161–2184 (2020).
    Google Scholar 
    Campos-Cerqueira, M. et al. Climate change is creating a mismatch between protected areas and suitable habitats for frogs and birds in Puerto Rico. Biodivers. Conserv. 30, 3509–3528 (2021).
    Google Scholar 
    Biddle, R. et al. The value of local community knowledge in species distribution modelling for a threatened Neotropical parrot. Biodivers. Conserv. 30, 1803–1823 (2021).
    Google Scholar 
    Costa, A. et al. Modelling the amphibian chytrid fungus spread by connectivity analysis: Towards a national monitoring network in Italy. Biodivers. Conserv. 30(10), 2807–2825 (2021).
    Google Scholar 
    Konowalik, K. & Nosol, A. Evaluation metrics and validation of presence-only species distribution models based on distributional maps with varying coverage. Sci. Rep. 11(1), 1–15 (2021).
    Google Scholar 
    Borgelt, J., Sicacha-Parada, J., Skarpaas, O. & Verones, F. Native range estimates for red-listed vascular plants. Sci. Data 9(1), 1–12 (2022).
    Google Scholar 
    Brychkova, G. et al. Climate change and land-use change impacts on future availability of forage grass species for Ethiopian dairy systems. Sci. Rep. 12(1), 1–16 (2022).
    Google Scholar 
    Carrara, R. & Roig-Juñent, S. A. Maps of potential biodiversity: when the tools for regional conservation planning clash with species ecological niches. Biodivers. Conserv. 31(2), 651–665 (2022).
    Google Scholar 
    Critchlow, R. et al. Multi-taxa spatial conservation planning reveals similar priorities between taxa and improved protected area representation with climate change. Biodivers. Conserv. 31(2), 683–702 (2022).
    Google Scholar 
    González-Orozco, C. E., Porcel, M., Rodriguez-Medina, C. & Yockteng, R. Extreme climate refugia: A case study of wild relatives of cacao (Theobroma cacao) in Colombia. Biodivers. Conserv. 31(1), 161–182 (2022).
    Google Scholar 
    Karami, S., Ejtehadi, H., Moazzeni, H., Vaezi, J. & Behroozian, M. Minimal climate change impacts on the geographic distribution of Nepeta glomerulosa, medicinal species endemic to southwestern and central Asia. Sci. Rep. 12(1), 1–10 (2022).ADS 
    CAS 

    Google Scholar 
    Montemayor, S. I., Besteiro, S. I. & del Río, M. G. Integrating ecological and biogeographical tools for the identification of conservation areas in two Neotropical biogeographic provinces in Argentina based on phytophagous insects. Biodivers. Conserv. 31(7), 1969–1986 (2022).
    Google Scholar 
    da Silva, L. B. et al. How future climate change and deforestation can drastically affect the species of monkeys endemic to the eastern Amazon, and priorities for conservation. Biodivers. Conserv. 31(3), 971–988 (2022).
    Google Scholar 
    Yousefi, M. & Naderloo, R. Global habitat suitability modeling reveals insufficient habitat protection for mangrove crabs. Sci. Rep. 12(1), 1–9 (2022).
    Google Scholar 
    Yudaputra, A. et al. Habitat preferences, spatial distribution and current population status of endangered giant flower Amorphophallus titanum. Biodivers. Conserv. 31(3), 831–854 (2022).
    Google Scholar 
    Gomes, V. H. et al. Species distribution modelling: Contrasting presence-only models with plot abundance data. Sci. Rep. 8(1), 1–12 (2018).
    Google Scholar 
    Hoveka, L. N., van der Bank, M., Bezeng, B. S. & Davies, T. J. Identifying biodiversity knowledge gaps for conserving South Africa’s endemic flora. Biodivers. Conserv. 29, 2803–2819 (2020).
    Google Scholar 
    Macdonald, D. W. et al. Predicting biodiversity richness in rapidly changing landscapes: Climate, low human pressure or protection as salvation?. Biodivers. Conserv. 29, 4035–4057 (2020).
    Google Scholar 
    Peng, Y., Feng, J., Sang, W. & Axmacher, J. C. Geographical divergence of species richness and local homogenization of plant assemblages due to climate change in grasslands. Biodivers. Conserv. 31(3), 797–810 (2022).
    Google Scholar 
    Rincón, V. et al. Connectivity of Natura 2000 potential natural riparian habitats under climate change in the Northwest Iberian Peninsula: Implications for their conservation. Biodivers. Conserv. 31(2), 585–612 (2022).MathSciNet 

    Google Scholar 
    Leta, S. et al. Modeling the global distribution of Culicoides imicola: An Ensemble approach. Sci. Rep. 9(1), 1–9 (2019).ADS 
    CAS 

    Google Scholar 
    Messina, J. P. et al. The current and future global distribution and population at risk of dengue. Nat. Microbiol. 4(9), 1508–1515 (2019).CAS 

    Google Scholar 
    Redding, D. W. et al. Impacts of environmental and socio-economic factors on emergence and epidemic potential of Ebola in Africa. Nat. Commun. 10, 4531 (2019).ADS 

    Google Scholar 
    Klitting, R. et al. Predicting the evolution of the Lassa virus endemic area and population at risk over the next decades. Nat. Commun. 13(1), 1–15 (2022).
    Google Scholar 
    Li, Y. P., Gao, X., An, Q., Sun, Z. & Wang, H. B. Ecological niche modeling based on ensemble algorithms to predicting current and future potential distribution of African swine fever virus in China. Sci. Rep. 12(1), 1–11 (2022).ADS 

    Google Scholar 
    Oppel, S., Schaefer, H. M., Schmidt, V. & Schröder, B. How much suitable habitat is left for the last known population of the Pale-headed Brush-Finch?. The Condor 106, 429–434 (2004).
    Google Scholar 
    Heikkinen, R. K., Marmion, M. & Luoto, M. Does the interpolation accuracy of species distribution models come at the expense of transferability?. Ecography 35, 276–288 (2012).
    Google Scholar 
    Manzoor, S. A., Griffiths, G. & Lukac, M. Species distribution model transferability and model grain size–finer may not always be better. Sci. Rep. 8(1), 1–9 (2018).
    Google Scholar 
    Yates, K. L. et al. Outstanding challenges in the transferability of ecological models. Trends Ecol. Evol. 33, 790–802. https://doi.org/10.1016/j.tree.2018.08.001 (2018).Article 

    Google Scholar 
    Gantchoff, M. G. et al. Distribution model transferability for a wide-ranging species, the Gray Wolf. Sci. Rep. 12(1), 1–11 (2022).
    Google Scholar 
    Lyam, P. T., Adeyemi, T. O. & Ogundipe, O. T. Distribution modelling of Chrysophyllum albidum G. Don. in South-West Nigeria. J. Nat. Environ. Sci. 3, 7–14 (2012).
    Google Scholar 
    Orwa, C., Mutua, A., Kindt, R., Jamnadass, R., & Simons, A. Agroforestree Database: a tree reference and selection guide version 4.0. World Agroforestry Centre, Kenya. http://www.worldagroforestry.org/af/treedb/ (2009).Bolanle-Ojo, O. T. & Onyekwelu, J. C. Socio-economic importance of Chrysophyllum albidum G. Don. Rainforest and derived savanna ecosystems of Ondo state, Nigeria. Eur. J. Agric. For. Res. 2, 43–51 (2014).
    Google Scholar 
    Ugwu, J. A. & Umeh, V. C. Assessment of African star apple (Chrysophyllum albidum) fruit damage due to insect pests in Ibadan Southwest Nigeria. Res. J. For. 9, 87–92 (2015).
    Google Scholar 
    Akoegninou, A., Van der Burg, W. J. & Van der Maesen, L. J. G. in Flore Analytique du Bénin (No. 06.2). Backhuys Publishers. (2006).Houessou, L. G., Lougbegnon, T. O., Gbesso, F. G., Anagonou, L. E. & Sinsin, B. Ethno-botanical study of the African star apple (Chrysophyllum albidum G. Don) in the Southern Benin (West Africa). J. Ethnobiol. Ethnomed. 8, 1–10 (2012).
    Google Scholar 
    Lougbégnon, O. T., Nassi, K. M. & Gbesso, G. H. F. Ethnobotanique quantitative de l’usage de Chrysophyllum albidum G. Don par les populations locales au Bénin. J. Appl. Biosci. 95, 9028–9038 (2015).
    Google Scholar 
    Nartey, D., Gyesi, J. N., & Borquaye, L. S. Chemical composition and biological activities of the essential oils of Chrysophyllum albidum G. Don (African star apple). Biochem. Res. Int. 2021 (2021).Olajide, O., Udo, E. S., & Out, D. O. Diversity and population of timber tree species producing valuable non-timber products in two tropical rainforests in cross river state, Nigeria. J. Agric. Soc. Sci. ISSN Print 1813–2235 (2008)Platts, P. J., Omeny, P. & Marchant, R. AFRICLIM: High-resolution climate projections for ecological applications in Africa. Afr. J. Ecol. 53, 103–108 (2015).
    Google Scholar 
    Hajima, T. et al. Development of the MIROC-ES2L Earth system model and the evaluation of biogeochemical processes and feedbacks. Geosci. Model Dev. 13, 2197–2244 (2020).ADS 

    Google Scholar 
    Aiello-Lammens, M. E., Boria, R. A., Radosavljevic, A., Vilela, B. & Anderson, R. P. An R package for spatial thinning of species occurrence records for use in ecological niche models. Ecography 38, 541–545 (2015).
    Google Scholar 
    Lannuzel, G., Balmot, J., Dubos, N., Thibault, M. & Fogliani, B. High-resolution topographic variables accurately predict the distribution of rare plant species for conservation area selection in a narrow-endemism hotspot in New Caledonia. Biodivers. Conserv. 30, 963–990 (2021).
    Google Scholar 
    Scales, K. L. et al. Scale of inference: On the sensitivity of habitat models for wide-ranging marine predators to the resolution of environmental data. Ecography 40, 210–220 (2017).
    Google Scholar 
    Fick, S. E. & Hijmans, R. J. (2017) WorldClim 2: New 1km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37(12), 4302–4315 (2017).
    Google Scholar 
    Center for International Earth Science Information Network: CIESIN—Columbia University. 2021. Gridded Population of the World, Version 4 (GPWv4): Administrative Unit Center Points with Population Estimates, Revision 11. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). Last Accessed 7th December, 2021. https://doi.org/10.7927/H4BC3WMT (2018)Eyring, V. et al. Overview of the coupled model intercomparison project phase 6 (CMIP6) experimental design and organization. Geosci. Model Dev. 9, 1937–1958. https://doi.org/10.5194/gmd-9-1937-2016 (2016).Article 
    ADS 

    Google Scholar 
    Dormann, C. F. et al. Collinearity: A review of methods to deal with it and a simulation study evaluating their performance. Ecography 36, 27–46 (2013).
    Google Scholar 
    Naimi, B. & Araújo, M. B. sdm: A reproducible and extensible R platform for species distribution modelling. Ecography 39, 368–375. https://doi.org/10.1111/ecog.01881 (2016).Article 

    Google Scholar 
    R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/ (2020)Phillips, S. J., Anderson, R. P. & Schapire, R. E. Maximum entropy modeling of species geographic distributions. Ecol. Model. 190, 231–259 (2006).
    Google Scholar 
    Elith, J., Leathwick, J. R. & Hastie, T. A working guide to boosted regression trees. J. Anim. Ecol. 77, 802–813 (2008).CAS 

    Google Scholar 
    Breiman, L. Random forests. Mach. Learn. 45, 5–32. https://doi.org/10.1023/A:1010933404324 (2001).Article 
    MATH 

    Google Scholar 
    Zheng, B. & Agresti, A. Summarizing the predictive power of a generalized linear model. Stat. Med. 19, 1771–1781 (2000).CAS 

    Google Scholar 
    Hastie, T. J. in Generalized Additive Models, Statistical models, 249–307 (Routledge, 2017).Friedman, J. H. Multivariate adaptive regression splines. Ann. Stat. 19, 1–67 (1991).MathSciNet 
    MATH 

    Google Scholar 
    Allouche, O., Tsoar, A. & Kadmon, R. Assessing the accuracy of species distribution models: Prevalence, kappa and the true skill statistic (TSS). J. Appl. Ecol. 43, 1223–1232 (2006).
    Google Scholar 
    Barbet-Massin, M., Jiguet, F., Albert, C. H. & Thuiller, W. Selecting pseudo-absences for species distribution models: How, where and how many?. Methods Ecol. Evol. 3, 327–338 (2012).
    Google Scholar 
    QGIS Development Team. QGIS geographic information system. Open Source Geospatial Foundation Project. http://qgis.osgeo.org (2021)Fandohan, B. et al. Women’s traditional knowledge, use value, and the contribution of tamarind (Tamarindus indica L.) to rural households’ cash income in Benin. Econ. Bot. 64, 248–259 (2010).
    Google Scholar 
    Gouwakinnou, G. N., Lykke, A. M., Assogbadjo, A. E. & Sinsin, B. Local knowledge, pattern and diversity of use of Sclerocarya birrea. J. Ethnobiol. Ethnomed. 7, 1–9 (2011).
    Google Scholar 
    O’Donnell, M. S. & Ignizio, D. A. Bioclimatic predictors for supporting ecological applications in the conterminous United States. US Geological Surv. Data Ser. 691, 4–9 (2012).
    Google Scholar 
    United Nations. 2022. World population projected to reach 9.8 billion in 2050, and 11.2 billion in 2100. https://www.un.org/en/desa/world-population-projected-reach-98-billion-2050-and-112-billion-2100, Accessed 25th December 2022 .Gbesso, F. H. G., Tente, B. H. A., Gouwakinnou, G. N. & Sinsin, B. A. Influence des changements climatiques sur la distribution géographique de Chrysophyllum albidum G. Don (Sapotaceae) au Benin. Int. J. Biol. Chem. Sci. 7, 2007–2018 (2013).
    Google Scholar 
    Elith, J. et al. Novel methods improve prediction of species’ distributions from occurrence data. Ecography 29, 129–151 (2006).
    Google Scholar 
    Mi, C., Huettmann, F., Guo, Y., Han, X. & Wen, L. Why choose random forest to predict rare species distribution with few samples in large undersampled areas? Three Asian crane species models provide supporting evidence. Peer J. 5, e2849. https://doi.org/10.7717/peerj.2849 (2017).Article 

    Google Scholar 
    Segurado, P. & Araujo, M. B. An evaluation of methods for modelling species distributions. J. Biogeogr. 31, 1555–1568 (2004).
    Google Scholar 
    Pearson, R. G. et al. Model-based uncertainty in species range prediction. J. Biogeogr. 33, 1704–1711 (2006).
    Google Scholar 
    Dambros, C. et al. The role of environmental filtering, geographic distance and dispersal barriers in shaping the turnover of plant and animal species in Amazonia. Biodivers. Conserv. 29, 3609–3634 (2020).
    Google Scholar  More

  • in

    Genomic architecture of migration timing in a long-distance migratory songbird

    Davidson, S. C. et al. Ecological insights from three decades of animal movement tracking across a changing arctic. Science 370, 712–715 (2020).ADS 
    CAS 

    Google Scholar 
    Cohen, J. M., Lajeunesse, M. J. & Rohr, J. R. A global synthesis of animal phenological responses to climate change. Nat. Clim. Chang. 8, 224–228 (2018).ADS 

    Google Scholar 
    Both, C., Bouwhuis, S., Lessells, C. M. & Visser, M. E. Climate change and population declines in a long-distance migratory bird. Nature 441, 81–83 (2006).ADS 
    CAS 

    Google Scholar 
    Studds, C. E. & Marra, P. P. Rainfall-induced changes in food availability modify the spring departure programme of a migratory bird. Proc. R. Sci. B. 278, 3437–3443 (2011).
    Google Scholar 
    González, A. M., Bayly, N. J. & Hobson, K. A. Earlier and slower or later and faster: spring migration pace linked to departure time in a Neotropical migrant songbird. J. Anim. Ecol. 89, 2840–2851 (2020).
    Google Scholar 
    Liedvogel, M., Åkesson, S. & Bensch, S. The genetics of migration on the move. Trends Ecol. Evol. 26, 561–569 (2011).
    Google Scholar 
    Caprioli, M. et al. Clock gene variation is associated with breeding phenology and maybe under directional selection in the migratory barn swallow. PLoS ONE 7, e35140 (2012).ADS 
    CAS 

    Google Scholar 
    Mettler, R., Segelbacher, G. & Schaefer, M. H. Interactions between a candidate gene for migration (ADCYAP1), morphology and sex predict spring arrival in blackcap populations. PLoS ONE 10, e0144587 (2015).
    Google Scholar 
    Bazzi, G. et al. Clock gene polymorphism and scheduling of migration: a geolocator study of the barn swallow Hirundo rustica. Sci. Rep. 5, 12443 (2015).ADS 

    Google Scholar 
    Saino, N. et al. Polymorphism at the Clock gene predicts phenology of long-distance migratoin in birds. Mol. Ecol. 24, 1758–1773 (2015).CAS 

    Google Scholar 
    Bossu, C. M. et al. Clock-linked genes underlie seasonal migratory timing in a diurnal raptor. Proc. R. Soc. B. 289, 20212507 (2022).CAS 

    Google Scholar 
    O’Malley, K. G., Ford, M. J. & Hard, J. J. Clock polymorphism in Pacific salmon: evidence for variable selection along a latitudinal gradient. Proc. R. Soc. B. 277, 3703–3714 (2010).
    Google Scholar 
    Peterson, M. P. et al. Variation in candidate genes CLOCK and ADCYAP1 does not consistently predict differences in migratory behavior in the songbird genus Junco. F1000Research 2 (2013).McKinnon, E. A. & Ten Love, O. P. years tracking the migrations of small landbirds: Lessons learned in the golden age of bio-logging. Auk 135, 834–856 (2018).
    Google Scholar 
    Fraser, K. C. et al. Continent-wide tracking to determine migratory connectivity and tropical habitat associations of a declining aerial insectivore. Proc. R. Soc. B. 279, 4901–4906 (2012).
    Google Scholar 
    Neufeld, L. R. et al. Breeding latitude is associated with the timing of nesting and migration around the annual calendar among purple martin Progne subis populations. J. Ornithol. 162, 1009–1024 (2021).
    Google Scholar 
    Peona, V. et al. Identifying the causes and consequences of assembly gaps using a multiplatform genome assembly of a bird-of-paradise. Mol. Ecol. 21(1), 263–286 (2020).
    Google Scholar 
    Coelho, L. A., Musher, L. J. & Cracraft, J. A multireference-based whole genome assembly for the obligate ant-following antbird, Rhegmatorhina melanosticta (Thamnophilidae). Diversity 11(19), 144 (2019).CAS 

    Google Scholar 
    Zhou, X., Carbonetto, P. & Stephens, M. Polygenic modeling with Bayesian sparse linear mixed models. PLoS Genet. 9, e1003264 (2013).CAS 

    Google Scholar 
    Fuller, Z. L. et al. Population genetics of the coral Acropora millepora: Towards a genomic predictor of bleaching. Science 369(6501) (2019).Jones, S., Pfister-Genskow, M., Benca, R. M. & Cirelli, C. Molecular correlates of sleep and wakefulness in the brain of the white-crowned sparrow. J. Neurochem. 105, 46–62 (2008).CAS 

    Google Scholar 
    Ma, C. et al. Sleep regulation by neurotensinergic neurons in a thalamo-amygdala circuit. Neuron 103 (2019).Wong, J. M. & Eirin-Lopez, J. M. Evolution of methyltransferase-like (METTL) proteins in metazoan: a complex gene family involved in epitranscriptomic regulation and other epigenetic processes. Mol. Biol. Evol. 38, 5309–5327 (2021).CAS 

    Google Scholar 
    Jia, Z. et al. ACSS3 in brown fast drives propionate catabolism and its deficiency leads to autophagy and systemic metabolic dysfunction. Clin. Transl. Med. 12, e665 (2022).CAS 

    Google Scholar 
    Muller, F. et al. Towards a conceptual framework for explaining variation in nocturnal departure time of songbird migrants. Mov. Ecol. 4, 24 (2016).
    Google Scholar 
    Fraser, K. C. et al. Individual variability in migration timing can explain long-term population-level advances in a songbird. Front. Ecol. Evol. 7, 324 (2019).ADS 

    Google Scholar 
    Barret, R. D. H. & Schluter, D. Adaptation from standing genetic variation. Trends Ecol. Evol. 23(1), 38–44 (2008).
    Google Scholar 
    Colodro-Conde, L. et al. A direct test of the diathesis-stress model for depression. Mol. Psychiatry 23, 1590–1596 (2017).
    Google Scholar 
    Dudbridge, F. Power and predictive accuracy of polygenic risk scores. PLOS Genetics 9(4) (2013).Lavallée, C. D. et al. The use of nocturnal flights for barrier crossing in a diurnally migrating songbird. Mov. Ecol. 9, 21 (2021).
    Google Scholar 
    Saino, N. et al. Migration phenology and breeding success are predicted by methylation of a photoperiodic gene in the barn swallow. Sci. Rep. 7, 45412 (2017).ADS 
    CAS 

    Google Scholar 
    Henry, R. A. et al. Changing the selectivity of p300 by acetyl-CoA modulation of histone acetylation. ACS Chem. Biol 10, 146–156 (2015).CAS 

    Google Scholar 
    Sun, H., Skorgerbø, G., Wang, Z., Liu, W. & Li, Y. Structural relationships between highly conserved elements and genes in vertebrate genomes. PLoS ONE 3, e3727 (2008).ADS 

    Google Scholar 
    Chin, C. S. et al. Phased diploid genome assembly with single-molecule real-time sequencing. Nat. Methods 13, 1050–1054 (2016).CAS 

    Google Scholar 
    Chin, C. S. et al. Nonhybrid, finished microbial genome assemblies from long-read SMRT sequencing data. Nat. Methods 10, 563–569 (2013).CAS 

    Google Scholar 
    Koren, S. et al. Canu: scalable and accurate long-read assembly via adaptive k-mer weighting and repeat separation. Genome Res. 27, 722–736 (2017).CAS 

    Google Scholar 
    Walker, B. J. et al. Pilon: an integrated tool for comprehensive microbial variant detection and genome assembly improvement. PLoS ONE 9, e112963 (2014).ADS 

    Google Scholar 
    Coombe, L. et al. ARKS: Chromosome-scale scaffolding of human genome drafts with linked read kmers. BMC Bioinform. 19, 1–10 (2018).
    Google Scholar 
    Campbell, M. S., Holt, C., Moore, B. & Yandell, M. Genome annotation and curation using MAKER and MAKER‐P. Curr. Protocols Bioinform. 48, 4.11.1–4.11.39 (2014).Malmberg, M. M. et al. Evaluation and recommendations for routine genotyping using skim whole genome re-sequencing in canola. Front. Plant. Sci. 9 (2018).Browning, B. L. & Browning, S. R. Genotype imputation with millions of reference samples. Am. J. Hum. Genet. 98, 116–126 (2016).CAS 

    Google Scholar 
    Golicz, A. A., Bayer, P. E. & Edwards, D. Skim-based genotyping by sequencing. Methods Mol. Biol. 1245, 257–270 (2015).CAS 

    Google Scholar 
    Hill, R. D. Theory of geolocation by light levels. In B. J. L. Boeuf, & R. M. Laws (Ed.), Elephant seals: Population ecology, behaviour and physiology, pp. 227–236. Berkeley, CA: University of California Press (1994).Wotherspoon, S., Summer, M. & Lisovski, S. BAStag: basic data processing for light based geolocation archival tags. Version 0.1.3. (2016).Lisovski, S. & Hahn, S. GeoLight-processing and anslysing light-based geolocator data in R. Methods Ecol. Evol. 3, 1055–1059 (2012).
    Google Scholar 
    Gompert, Z., Lucas, L. K., Nice, C. C. & Buerkle, C. A. Genome divergence and the genetic architecture of barriers to gene flow between Lycaeides idas and L. melissa. Evolution 67, 2498–2514 (2013).
    Google Scholar 
    Pfeifer, S. P. et al. The evolutionary history of Nebraska deer mice: local adaptation in the face of strong gene flow. Mol. Biol. Evol. 35, 792–806 (2018).CAS 

    Google Scholar 
    Purcell, S. et al. PLINK: a toolset for whole-genome association and population-based linkage analysis. Am. J. Hum. Genet. 81, 559–575 (2007).CAS 

    Google Scholar 
    Choi, S. W., Mak, T. S. & O’Reilly, P. F. Tutorial: a guide to performing polygenic risk score analysis. Nat Protoc 15, 2759–2772 (2020).CAS 

    Google Scholar 
    Danecek, P. et al. The variant call format and VCFtools. Bioinformatics 27, 2156–2158 (2011).CAS 

    Google Scholar 
    Cruickshank, T. E. & Hahn, M. W. Reanalysis suggests that genomic islands of speciation are due to reduced diversity, not reduced gene flow. Mol. Ecol. 23, 3133–3157 (2014).
    Google Scholar 
    Vijay, N. et al. Evolution of heterogeneous genome differentiation across multiple contact zones in a crow species complex. Nat. Commun. 7, 13195 (2016).ADS 
    CAS 

    Google Scholar 
    Delmore, K. et al. The evolutionary history and genomics of European blackcap migration. eLife 9, e54462 (2020). More

  • in

    A report card approach to describe temporal and spatial trends in parameters for coastal seagrass habitats

    Costanza, R. et al. Twenty years of ecosystem services: How far have we come and how far do we still need to go?. Ecosyst. Serv. 28, 1–16. https://doi.org/10.1016/j.ecoser.2017.09.008 (2017).Article 

    Google Scholar 
    Harwell, M. A. et al. Conceptual framework for assessing ecosystem health. Integr. Environ. Assess. Manag. 15, 544–564. https://doi.org/10.1002/ieam.4152 (2019).Article 

    Google Scholar 
    Halpern, B. S. et al. A global map of human impact on marine ecosystems. Science 319, 948–952. https://doi.org/10.1126/science.1149345 (2008).Article 
    ADS 
    CAS 

    Google Scholar 
    Roca, G. et al. Response of seagrass indicators to shifts in environmental stressors: A global review and management synthesis. Ecol. Ind. 63, 310–323. https://doi.org/10.1016/j.ecolind.2015.12.007 (2016).Article 

    Google Scholar 
    Westgate, M. J., Likens, G. E. & Lindenmayer, D. B. Adaptive management of biological systems: A review. Biol. Cons. 158, 128–139. https://doi.org/10.1016/j.biocon.2012.08.016 (2013).Article 

    Google Scholar 
    Logan, M. et al. Ecosystem health report cards: An overview of frameworks and analytical methodologies. Ecol. Indic. 113, 105834. https://doi.org/10.1016/j.ecolind.2019.105834 (2020).Article 

    Google Scholar 
    Dennison, W. C., Lookingbill, T. R., Carruthers, T. J., Hawkey, J. M. & Carter, S. L. An eye-opening approach to developing and communicating integrated environmental assessments. Front. Ecol. Environ. 5, 307–314. https://doi.org/10.1890/1540-9295(2007)5[307:AEATDA]2.0.CO;2 (2007).Article 

    Google Scholar 
    Harwell, M. A. et al. A framework for an ecosystem integrity report card: examples from south Florida show how an ecosystem report card links societal values and scientific information. Bioscience 49, 543–556. https://doi.org/10.2307/1313475 (1999).Article 

    Google Scholar 
    Collier, C. J. et al. An evidence-based approach for setting desired state in a complex Great Barrier Reef seagrass ecosystem: A case study from Cleveland Bay. Environ. Sustain. Indic. 7, 100042. https://doi.org/10.1016/j.indic.2020.100042 (2020).Article 

    Google Scholar 
    Coles, R. G. et al. Seagrass: Ecology, Uses and Threats (Nova Science Publishers, Inc., 2011).
    Google Scholar 
    Grech, A. et al. A comparison of threats, vulnerabilities and management approaches in global seagrass bioregions. Environ. Res. Lett. 7, 024006. https://doi.org/10.1088/1748-9326/7/2/024006 (2012).Article 
    ADS 

    Google Scholar 
    Lambert, V. M. et al. Connecting targets for catchment sediment loads to ecological outcomes for seagrass using multiple lines of evidence. Mar. Pollut. Bull. https://doi.org/10.1016/j.marpolbul.2021.112494 (2021).Article 

    Google Scholar 
    Adams, M. P. et al. Predicting seagrass decline due to cumulative stressors. Environ. Model. Softw. 130, 104717. https://doi.org/10.1016/j.envsoft.2020.104717 (2020).Article 

    Google Scholar 
    Chartrand, K. M., Szabó, M., Sinutok, S., Rasheed, M. A. & Ralph, P. J. Living at the margins: The response of deep-water seagrasses to light and temperature renders them susceptible to acute impacts. Mar. Environ. Res. 136, 126–138. https://doi.org/10.1016/j.marenvres.2018.02.006 (2018).Article 
    CAS 

    Google Scholar 
    Chartrand, K., Bryant, C., Carter, A., Ralph, P. & Rasheed, M. Light thresholds to prevent dredging impacts on the Great Barrier Reef seagrass, Zostera muelleri spp. capricorni. Front. Mar. Sci. 3, 17. https://doi.org/10.3389/fmars.2016.00106 (2016).Article 

    Google Scholar 
    Abal, E. & Dennison, W. Seagrass depth range and water quality in southern Moreton Bay, Queensland, Australia. Mar. Freshwater Res. 47, 763–771. https://doi.org/10.1071/MF9960763 (1996).Article 
    CAS 

    Google Scholar 
    Dennison, W. et al. Assessing water quality with submersed aquatic vegetation: Habitat requirements as barometers of Chesapeake Bay health. Bioscience 43, 86–94. https://doi.org/10.2307/1311969 (1993).Article 

    Google Scholar 
    Carter, A. B., Collier, C., Coles, R., Lawrence, E. & Rasheed, M. A. Community-specific, “desired” states for seagrasses through cycles of loss and recovery. J. Environ. Manag. 314, 115059. https://doi.org/10.1016/j.jenvman.2022.115059 (2022).Article 

    Google Scholar 
    Kaldy, J. E., Brown, C. A. & Pacella, S. R. Carbon limitation in response to nutrient loading in an eelgrass mesocosm: Influence of water residence time. Mar. Ecol. Prog. Ser. 689, 1–17. https://doi.org/10.3354/meps14061 (2022).Article 
    CAS 

    Google Scholar 
    Carter, A. B. et al. A spatial analysis of seagrass habitat and community diversity in the Great Barrier Reef World Heritage Area. Sci. Rep. https://doi.org/10.1038/s41598-021-01471-4 (2021).Article 

    Google Scholar 
    Kenworthy, W. J., Wyllie-Echeverria, S., Coles, R. G., Pergent, G. & Pergent-Martini, C. Seagrasses: Biology, Ecology and Conservation 595–623 (Springer, 2006).
    Google Scholar 
    Hayes, M. A. et al. The differential importance of deep and shallow seagrass to nekton assemblages of the great barrier reef. Diversity 12, 292. https://doi.org/10.3390/d12080292 (2020).Article 

    Google Scholar 
    Marsh, H., O’Shea, T. J. & Reynolds, J. E. III. Ecology and Conservation of the Sirenia: Dugongs and Manatees Vol. 18 (Cambridge University Press, 2011).Book 

    Google Scholar 
    Scott, A. L. et al. The role of herbivory in structuring tropical seagrass ecosystem service delivery. Front. Plant Sci. 9, 1–10. https://doi.org/10.3389/fpls.2018.00127 (2018).Article 

    Google Scholar 
    York, P. H., Macreadie, P. I. & Rasheed, M. A. Blue carbon stocks of Great Barrier Reef deep-water seagrasses. Biol. Lett. 14, 20180529. https://doi.org/10.1098/rsbl.2018.0529 (2018).Article 
    CAS 

    Google Scholar 
    Unsworth, R. K., Collier, C. J., Waycott, M., Mckenzie, L. J. & Cullen-Unsworth, L. C. A framework for the resilience of seagrass ecosystems. Mar. Pollut. Bull. 100, 34–46. https://doi.org/10.1016/j.marpolbul.2015.08.016 (2015).Article 
    CAS 

    Google Scholar 
    Madden, C. J., Rudnick, D. T., McDonald, A. A., Cunniff, K. M. & Fourqurean, J. W. Ecological indicators for assessing and communicating seagrass status and trends in Florida Bay. Ecol. Ind. 9, S68–S82. https://doi.org/10.1016/j.ecolind.2009.02.004 (2009).Article 
    CAS 

    Google Scholar 
    York, P. et al. Dynamics of a deep-water seagrass population on the Great Barrier Reef: Annual occurrence and response to a major dredging program. Sci. Rep. 5, 13167. https://doi.org/10.1038/srep13167 (2015).Article 
    ADS 
    CAS 

    Google Scholar 
    Rasheed, M. A., McKenna, S. A., Carter, A. B. & Coles, R. G. Contrasting recovery of shallow and deep water seagrass communities following climate associated losses in tropical north Queensland, Australia. Mar. Pollut. Bull. 83, 491–499. https://doi.org/10.1016/j.marpolbul.2014.02.013 (2014).Article 
    CAS 

    Google Scholar 
    Smith, T., Chartrand, K., Wells, J., Carter, A. & Rasheed, M. Seagrasses in Port Curtis and Rodds Bay 2019 Annual long-term monitoring and whole port survey. 71, https://www.tropwater.com/wp-content/uploads/2022/10/20-64-Annual-Seagrass-monitoring-in-Port-Curtis-and-Rodds-Bay-2019.pdf (Centre for Tropical Water & Aquatic Ecosystem Research (TropWATER) Publication 20/64, James Cook University, Cairns, 2020).Ruaro, R., Gubiani, E. A., Hughes, R. M. & Mormul, R. P. Global trends and challenges in multimetric indices of biological condition. Ecol. Indic. 110, 105862. https://doi.org/10.1016/j.ecolind.2019.105862 (2020).Article 

    Google Scholar 
    Kilminster, K. et al. Unravelling complexity in seagrass systems for management: Australia as a microcosm. Sci. Total Environ. 534, 97–109. https://doi.org/10.1016/j.scitotenv.2015.04.061 (2015).Article 
    ADS 
    CAS 

    Google Scholar 
    Collier, C. J., Chartrand, K., Honchin, C., Fletcher, A. & Rasheed, M. Light thresholds for seagrasses of the GBR: a synthesis and guiding document. Including knowledge gaps and future priorities. 41, http://nesptropical.edu.au/wp-content/uploads/2016/05/NESP-TWQ-3.3-FINAL-REPORTa.pdf (Report to the National Environmental Science Programme, Cairns, 2016).Bryant, C., Jarvis, J. C., York, P. & Rasheed, M. Gladstone Healthy Harbour Partnership Pilot Report Card; ISP011: Seagrass., 74, https://researchonline.jcu.edu.au/44549/ (Centre for Tropical Water & Aquatic Ecosystem Research (TropWATER) Publication 14/53, James Cook University, Cairns, 2014).McIntosh, E. J. et al. Designing report cards for aquatic health with a whole-of-system approach: Gladstone Harbour in the Great Barrier Reef. Ecol. Ind. 102, 623–632. https://doi.org/10.1016/j.ecolind.2019.03.012 (2019).Article 

    Google Scholar 
    Birch, W. & Birch, M. Succession and pattern of tropical intertidal seagrasses in Cockle Bay, Queensland, Australia: A decade of observations. Aquat. Bot. 19, 343–367. https://doi.org/10.1016/0304-3770(84)90048-2 (1984).Article 

    Google Scholar 
    Rasheed, M. A. Recovery and succession in a multi-species tropical seagrass meadow following experimental disturbance: The role of sexual and asexual reproduction. J. Exp. Mar. Biol. Ecol. 310, 13–45. https://doi.org/10.1016/j.jembe.2004.03.022 (2004).Article 

    Google Scholar 
    Christiaen, B., Lehrter, J., Goff, J. & Cebrian, J. Functional implications of changes in seagrass species composition in two shallow coastal lagoons. Mar. Ecol. Prog. Ser. 557, 11. https://doi.org/10.3354/meps11847 (2016).Article 

    Google Scholar 
    Hyndes, G. A., Kendrick, A. J., MacArthur, L. D. & Stewart, E. Differences in the species- and size-composition of fish assemblages in three distinct seagrass habitats with differing plant and meadow structure. Mar. Biol. 142, 1195–1206. https://doi.org/10.1007/s00227-003-1010-2 (2003).Article 

    Google Scholar 
    Ray, B. R., Johnson, M. W., Cammarata, K. & Smee, D. L. Changes in seagrass species composition in Northwestern Gulf of Mexico Estuaries: Effects on associated seagrass Fauna. PLoS ONE 9, e107751. https://doi.org/10.1371/journal.pone.0107751 (2014).Article 
    ADS 
    CAS 

    Google Scholar 
    Ondiviela, B. et al. The role of seagrasses in coastal protection in a changing climate. Coast. Eng. 87, 11. https://doi.org/10.1016/j.coastaleng.2013.11.005 (2014).Article 

    Google Scholar 
    Lavery, P. S., Mateo, M. -Á., Serrano, O. & Rozaimi, M. Variability in the carbon storage of seagrass habitats and its implications for global estimates of blue carbon ecosystem service. PLoS ONE 8, e73748. https://doi.org/10.1371/journal.pone.0073748 (2013).Article 
    ADS 
    CAS 

    Google Scholar 
    Coles, R. G. et al. The Great Barrier Reef World Heritage Area seagrasses: Managing this iconic Australian ecosystem resource for the future. Estuar. Coast. Shelf Sci. 153, A1–A12. https://doi.org/10.1016/j.ecss.2014.07.020 (2015).Article 
    ADS 

    Google Scholar 
    Smith, T. M., Reason, C., McKenna, S. & Rasheed, M. A. Seagrasses in Port Curtis and Rodds Bay 2020. Annual long-term monitoring. 54, https://www.dropbox.com/s/f5yb6bjjpbvc1f2/21%2016%20Smith%20et%20al%202021%20Annual%20Seagrass%20monitoring%20in%20Port%20Curtis%20and%20Rodds%20Bay%202020_Final%20version.pdf?dl=0 (Centre for Tropical Water & Aquatic Ecosystem Research (TropWATER) Publication 21/16, James Cook University, Cairns, 2021).Windle, J., Rolfe, J. & Pascoe, S. Assessing recreational benefits as an economic indicator for an industrial harbour report card. Ecol. Ind. 80, 224–231. https://doi.org/10.1016/j.ecolind.2017.05.036 (2017).Article 

    Google Scholar 
    Scott, A. & Rasheed, M. A. Port of Karumba long-term annual seagrass monitoring 2020. 28, https://www.dropbox.com/s/fwtys67ljssbp9t/21%2005%20Scott%20%26%20Rasheed%202021%20FINAL%202020%20Karumba%20Long-term%20seagrass%20monitoring%20report%20low%20res.pdf?dl=0 (Centre for Tropical Water & Aquatic Ecosystem Research (TropWATER) Publication 21/05, James Cook University, Cairns, 2021).
    Google Scholar 
    Smith, T., Reason, C., McKenna, S. & Rasheed, M. Port of Weipa long‐term seagrass monitoring program, 2000 ‐ 2020. 49, https://www.dropbox.com/s/ghqy3bmn9p8jbsi/20%2058%20Smith%20et%20al%202020%20Port%20of%20Weipa%20Annual%20Long%20Term%20Seagrass%20Monitoring%20Report%202020.pdf?dl=0 (Centre for Tropical Water & Aquatic Ecosystem Research (TropWATER) Publication 20/58, James Cook University, Cairns, 2020).Reason, C. L., Smith, T. M. & Rasheed, M. A. Seagrass habitat of Cairns Harbour and Trinity Inlet: Cairns Shipping Development Program and Annual Monitoring Report 2020. 54, https://www.dropbox.com/s/m7xtrytjjip3a42/21%2009%20Final_Cairns%20Harbour%20Seagrass%20Monitoring%20Report%202020.pdf?dl=0 (Centre for Tropical Water & Aquatic Ecosystem Research (TropWATER) Publication 21/09, James Cook University, Cairns, 2021).Reason, C. L., York, P. H. & Rasheed, M. A. Seagrass habitat of Mourilyan Harbour: Annual monitoring report – 2020. 36, https://www.dropbox.com/s/kg3toxmlifh62tg/21%2010%20Mourilyan%20Harbour%20seagrass%20monitoring%20report%202020.pdf?dl=0 (Centre for Tropical Water & Aquatic Ecosystem Research (TropWATER) Publication 21/10, James Cook University, Cairns, 2021).McKenna, S., Wilkinson, J., Chartrand, K. & Van De Wetering, C. Port of Townsville Seagrass Monitoring Program: 2020. 62, https://www.dropbox.com/s/n8nsx8ts93fgr36/21%2014%20Final%20POTL%20Annual%20Seagrass%20Report%202020.pdf?dl=0 (Centre for Tropical Water & Aquatic Ecosystem Research (TropWATER) Publication 21/14, James Cook University, Cairns, 2021).McKenna, S. A., van de Wetering, C., Wilkinson, J. & Rasheed, M. A. Port of Abbot Point long-term seagrass monitoring program: 2020. 35, https://www.dropbox.com/s/l5a5l7pkikcjrfb/21%2025%20McKenna%20et%20al%20Port%20of%20Abbot%20Point%20Long-term%20seagrass%20Monitoring%20report%202020.pdf?dl=0 (Centre for Tropical Water & Aquatic Ecosystem Research (TropWATER) Publication 21/25, James Cook University, Cairns, 2021).York, P. H. & Rasheed, M. A. Annual Seagrass Monitoring in the Mackay-Hay Point Region – 2020. 42, https://www.dropbox.com/s/u45yezm3984lw1a/21%2020%20Hay%20Point%20and%20Mackay%20Seagrass%20Final%20Report%202020.pdf?dl=0 (Centre for Tropical Water & Aquatic Ecosystem Research (TropWATER) Publication 21/20, James Cook University, Cairns, 2021).van de Wetering, C., Carter, A. B. & Rasheed, M. A. Mackay-Whitsunday-Isaac Seagrass Monitoring 2017–2020: Marine Inshore South Zone. 30, https://researchonline.jcu.edu.au/70923/ (Centre for Tropical Water & Aquatic Ecosystem Research (TropWATER) Publication 21/06, James Cook University, Cairns, 2021).Carter, A. B. et al. Torres Strait Seagrass 2021 Report Card. 76, https://researchonline.jcu.edu.au/70797/ (Centre for Tropical Water & Aquatic Ecosystem Research (TropWATER) Publication 21/13, James Cook University, Cairns, 2021).Gladstone Ports Corporation. Port of Gladstone. https://www.gpcl.com.au/port-of-gladstone (2022).Sawynok, B., Venables, B. & Pinto, U. Incorporating a fish recruitment indicator into a health report card: A case study from Gladstone Harbour, Australia. Ecol. Indic. 115, 106329. https://doi.org/10.1016/j.ecolind.2020.106329 (2020).Article 

    Google Scholar 
    Pascoe, S. et al. Developing a social, cultural and economic report card for a regional industrial harbour. PLoS ONE 11, e0148271. https://doi.org/10.1371/journal.pone.0148271 (2016).Article 
    CAS 

    Google Scholar 
    Chartrand, K. M., Bryant, C. V., Sozou, A., Ralph, P. J. & Rasheed, M. A. Final Report: Deep‐water seagrass dynamics ‐ Light requirements, seasonal change and mechanisms of recruitment. 67, https://www.dropbox.com/sh/mo8dcq1322qv5c3/AAAgu3lEnJsLgxdawXaOltu-a/2017?dl=0&preview=17+16+Final+Report+Deep-water+seagrass+dynamics.pdf&subfolder_nav_tracking=1 (Centre for Tropical Water & Aquatic Ecosystem Research (TropWATER) Publication 17/16, James Cook University, Cairns, 2017).Kirkman, H. Decline of seagrass in northern areas of Moreton Bay, Queensland. Aquat. Bot. 5, 63–76. https://doi.org/10.1016/0304-3770(78)90047-5 (1978).Article 

    Google Scholar 
    Mellors, J. E. An evaluation of a rapid visual technique for estimating seagrass biomass. Aquat. Bot. 42, 67–73. https://doi.org/10.1016/0304-3770(91)90106-F (1991).Article 

    Google Scholar 
    Emmer, I. et al. Methodology for tidal wetland and seagrass restoration VM0033, version 2.0. https://verra.org/wp-content/uploads/2018/03/VM0033-Methodology-for-Tidal-Wetland-and-Seagrass-Restoration-v2.0-30Sep21-1.pdf (2021). More

  • in

    Agricultural spider decline: long-term trends under constant management conditions

    Waters, C. N. et al. The Anthropocene is functionally and stratigraphically distinct from the Holocene. Science 351, 137. https://doi.org/10.1126/science.aad2622 (2016).Article 
    CAS 

    Google Scholar 
    Thomas, J. A. & Morris, M. G. Patterns, mechanisms and rates of extinction among invertebrates in the United Kingdom. Phil. Trans. R. Soc. Lond. B 344, 47–54 (1994).Article 
    ADS 

    Google Scholar 
    Thomas, J. A. et al. Comparative losses of british butterflies, birds, and plants and the global extinction crisis. Science 303, 1879–1881. https://doi.org/10.1126/science.1095046 (2004).Article 
    ADS 
    CAS 

    Google Scholar 
    van Klink, R. et al. Meta-analysis reveals declines in terrestrial but increases in freshwater insect abundances. Science 368, 417–420. https://doi.org/10.1126/science.aax9931 (2020).Article 
    ADS 
    CAS 

    Google Scholar 
    Hallmann, C. A. et al. More than 75 percent decline over 27 years in total flying insect biomass in protected areas. PLoS ONE 12, 21. https://doi.org/10.1371/journal.pone.0185809 (2017).Article 
    CAS 

    Google Scholar 
    Barmentlo, S. H. et al. Experimental evidence for neonicotinoid driven decline in aquatic emerging insects. Proc. Natl. Acad. Sci. USA 118, 8. https://doi.org/10.1073/pnas.2105692118j1of8 (2021).Article 

    Google Scholar 
    Ehlers, B. K., Bataillon, T. & Damgaard, C. F. Ongoing decline in insect-pollinated plants across Danish grasslands. Biol. Lett. 17, 20210493. https://doi.org/10.1098/rsbl.2021.0493 (2021).Article 

    Google Scholar 
    Seibold, S. et al. Arthropod decline in grasslands and forests is associated with landscape-level drivers. Nature 574, 671–674. https://doi.org/10.1038/s41586-019-1684-3 (2019).Article 
    ADS 
    CAS 

    Google Scholar 
    Cardoso, P. et al. Scientists’ warning to humanity on insect extinctions. Biol. Conserv. 242, 108426. https://doi.org/10.1016/j.biocon.2020.108426 (2020).Article 

    Google Scholar 
    Montgomery, G. A. et al. Is the insect apocalypse upon us? How to find out. Biol. Conserv. 241, 6. https://doi.org/10.1016/j.biocon.2019.108327 (2020).Article 

    Google Scholar 
    Jactel, H. et al. Insect decline: immediate action is needed. C. R. Biol. 343, 267–293. https://doi.org/10.5802/crbiol.37 (2020).Article 

    Google Scholar 
    Owens, A. C. S. et al. Light pollution is a driver of insect declines. Biol. Conserv. 241, 9. https://doi.org/10.1016/j.biocon.2019.108259 (2020).Article 

    Google Scholar 
    Sanchez-Bayo, F. & Wyckhuys, K. A. G. Worldwide decline of the entomofauna: A review of its drivers. Biol. Conserv. 232, 8–27. https://doi.org/10.1016/j.biocon.2019.01.020 (2019).Article 

    Google Scholar 
    Michalko, R., Pekar, S. & Entling, M. H. An updated perspective on spiders as generalist predators in biological control. Oecologia https://doi.org/10.1007/s00442-018-4313-1 (2018).Article 

    Google Scholar 
    Nyffeler, M., Sterling, W. & Dean, D. How spiders make a living. Environ. Entomol. 23, 1357–1367 (1994).Article 

    Google Scholar 
    Branco, V. V. & Cardoso, P. An expert-based assessment of global threats and conservation measures for spiders. Glob. Ecol. Conserv. 24, 15. https://doi.org/10.1016/j.gecco.2020.e01290 (2020).Article 

    Google Scholar 
    Gobbi, M., Fontaneto, D. & De Bernardi, F. Influence of climate changes on animal communities in space and time: The case of spider assemblages along an alpine glacier foreland. Glob. Change Biol. 12, 1985–1992. https://doi.org/10.1111/j.1365-2486.2006.01236.x (2006).Article 
    ADS 

    Google Scholar 
    Mammola, S., Goodacre, S. L. & Isaia, M. Climate change may drive cave spiders to extinction. Ecography 41, 233–243. https://doi.org/10.1111/ecog.02902 (2018).Article 

    Google Scholar 
    Potapov, A. M. et al. Functional losses in ground spider communities due to habitat structure degradation under tropical land-use change. Ecology 101, e02957. https://doi.org/10.1002/ecy.2957 (2020).Article 

    Google Scholar 
    Kormann, U. et al. Local and landscape management drive trait-mediated biodiversity of nine taxa on small grassland fragments. Divers. Distrib. 21, 1204–1217. https://doi.org/10.1111/ddi.12324 (2015).Article 

    Google Scholar 
    Hogg, B. N. & Daane, K. M. Ecosystem services in the face of invasion: the persistence of native and nonnative spiders in an agricultural landscape. Ecol. Appl. 21, 565–576. https://doi.org/10.1890/10-0496.1 (2011).Article 

    Google Scholar 
    Galle, R., Happe, A. K., Baillod, A. B., Tscharntke, T. & Batary, P. Landscape configuration, organic management, and within-field position drive functional diversity of spiders and carabids. J. Appl. Ecol. 56, 63–72. https://doi.org/10.1111/1365-2664.13257 (2019).Article 

    Google Scholar 
    Pekár, S. Spiders (Araneae) in the pesticide world: An ecotoxicological review. Pest. Manage. Sci. 68, 1438–1446. https://doi.org/10.1002/ps.3397 (2012).Article 
    CAS 

    Google Scholar 
    Bommarco, R., Miranda, F., Bylund, H. & Bjorkman, C. Insecticides suppress natural enemies and increase pest damage in cabbage. J. Econ. Entomol. 104, 782–791. https://doi.org/10.1603/ec10444 (2011).Article 
    CAS 

    Google Scholar 
    Outhwaite, C. L., Gregory, R. D., Chandler, R. E., Collen, B. & Isaac, N. J. B. Complex long-term biodiversity change among invertebrates, bryophytes and lichens. Nature Ecol. Evol. 4, 384–392. https://doi.org/10.1038/s41559-020-1111-z (2020).Article 

    Google Scholar 
    Rix, M. G. et al. Where have all the spiders gone? The decline of a poorly known invertebrate fauna in the agricultural and arid zones of southern Australia. Austral Entomol. 56, 14–22. https://doi.org/10.1111/aen.12258 (2017).Article 

    Google Scholar 
    Nyffeler, M. & Bonte, D. Where have all the spiders gone? Observations of a dramatic population density decline in the once very abundant garden spider, Araneus diadematus (Araneae: Araneidae), in the Swiss Midland. Insects 11, 12. https://doi.org/10.3390/insects11040248 (2020).Article 

    Google Scholar 
    Bowden, J. J., Hansen, O. L. P., Olsen, K., Schmidt, N. M. & Høye, T. T. Drivers of inter-annual variation and long-term change in High-Arctic spider species abundances. Polar Biol. 41, 1635–1649. https://doi.org/10.1007/s00300-018-2351-0 (2018).Article 

    Google Scholar 
    Samu, F., Németh, J. & Kiss, B. Assessment of the efficiency of a hand-held suction device for sampling spiders: Improved density estimation or oversampling?. Ann. Appl. Biol. 130, 371–378. https://doi.org/10.1111/j.1744-7348.1997.tb06840.x (1997).Article 

    Google Scholar 
    Nentwig, W. et al. Spiders of Europe. Version 07.2022. https://www.araneae.nmbe.ch (2022).Heimer, S. & Nentwig, W. Spinnen Mitteleuropas (Paul Parey, 1991).
    Google Scholar 
    Samu, F. & Szinetár, C. On the nature of agrobiont spiders. J. Arachnol. 30, 389–402. https://doi.org/10.1636/0161-8202(2002)030[0389:Otnoas]2.0.Co;2 (2002).Article 

    Google Scholar 
    Buchar, J. & Růžička, V. Catalogue of Spiders of the Czech Republic (Peres, 2002).
    Google Scholar 
    Samu, F. A general data model for databases in experimental animal ecology. Acta Zool. Acad. Sci. Hung. 45, 273–290 (1999).
    Google Scholar 
    Laliberté, E., Legendre, P. & Shipley, B. FD: Measuring Functional Diversity from Multiple Traits, and Other Tools for Functional Ecology. R package version 1.0–12. (2014).Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48. https://doi.org/10.18637/jss.v067.i01 (2015).Article 

    Google Scholar 
    Zuur, A., Ieno, E., Walker, N., Saveliev, A. & Smith, G. Mixed Effects Models and Extensions in Ecology with R (Springer, 2009).Book 
    MATH 

    Google Scholar 
    Vegan. Community Ecology Package. R package Version 2.5–6. The Comprehensive R Archive Network (2019).ter Braak, C. J. F. & Smilauer, P. Canoco Reference Manual and User’s Guide: Software for Ordination, Version 5.1x. (Microcomputer Power, 2018).McRae, L., Deinet, S. & Freeman, R. The diversity-weighted living planet index: Controlling for taxonomic bias in a global biodiversity indicator. PLoS ONE 12, e0169156. https://doi.org/10.1371/journal.pone.0169156 (2017).Article 
    CAS 

    Google Scholar 
    Toju, H. & Baba, Y. G. DNA metabarcoding of spiders, insects, and springtails for exploring potential linkage between above- and below-ground food webs. Zool. Lett. 4, 12. https://doi.org/10.1186/s40851-018-0088-9 (2018).Article 

    Google Scholar 
    Dirzo, R. et al. Defaunation in the anthropocene. Science 345, 401–406. https://doi.org/10.1126/science.1251817 (2014).Article 
    ADS 
    CAS 

    Google Scholar 
    Lister, B. C. & Garcia, A. Climate-driven declines in arthropod abundance restructure a rainforest food web. Proc. Natl. Acad. Sci. USA 115, E10397–E10406. https://doi.org/10.1073/pnas.1722477115 (2018).Article 
    ADS 
    CAS 

    Google Scholar 
    Harwood, J. D., Sunderland, K. D. & Symondson, W. O. C. Monoclonal antibodies reveal the potential of the tetragnathid spider Pachygnatha degeeri (Araneae: Tetragnathidae) as an aphid predator. Bull. Entomol. Res. 95, 161–167. https://doi.org/10.1079/BER2004346 (2005).Article 
    CAS 

    Google Scholar 
    Samu, F., Beleznai, O. & Tholt, G. A potential spider natural enemy against virus vector leafhoppers in agricultural mosaic landscapes: Corroborating ecological and behavioral evidence. Biol. Control. 67, 390–396. https://doi.org/10.1016/j.biocontrol.2013.08.016 (2013).Article 

    Google Scholar 
    Biteniekyté, M. & Relys, V. Epigeic spider communities of a peat bog and adjacent habitats. Rev. Iber. Aracnol. 15, 81–87 (2008).
    Google Scholar 
    Michalko, R., Kosulic, O., Hula, V. & Surovcova, K. Niche differentiation of two sibling wolf spider species, Pardosa lugubris and Pardosa alacris, along a canopy openness gradient. J. Arachnol. 44, 46–51 (2016).Article 

    Google Scholar 
    Nyffeler, M. & Birkhofer, K. An estimated 400–800 million tons of prey are annually killed by the global spider community. Naturwissenschaften 104, 30. https://doi.org/10.1007/s00114-017-1440-1 (2017).Article 
    CAS 

    Google Scholar 
    Sohlström, E. H. et al. Future climate and land-use intensification modify arthropod community structure. Agric. Ecosyst. Environ. 327, 107830. https://doi.org/10.1016/j.agee.2021.107830 (2022).Article 
    CAS 

    Google Scholar 
    Sallé, A. et al. Climate change alters temperate forest canopies and indirectly reshapes arthropod communities. Front. For. Glob. Change 4, 710854 (2021).Article 

    Google Scholar 
    Høye, T. T. et al. Nonlinear trends in abundance and diversity and complex responses to climate change in Arctic arthropods. Proc. Natl. Acas. Sci. USA 118, e2002557117 (2021).Article 

    Google Scholar 
    Tscharntke, T., Klein, A. M., Kruess, A., Steffan-Dewenter, I. & Thies, C. Landscape perspectives on agricultural intensification and biodiversity: Ecosystem service management. Ecol. Lett. 8, 857–874. https://doi.org/10.1111/j.1461-0248.2005.00782.x (2005).Article 

    Google Scholar 
    Kleijn, D., Rundlöf, M., Scheper, J., Smith, H. G. & Tscharntke, T. Does conservation on farmland contribute to halting the biodiversity decline?. Trends Ecol. Evol. 26, 474–481. https://doi.org/10.1016/j.tree.2011.05.009 (2011).Article 

    Google Scholar 
    Swinbank, A. The European Union’s Common Agricultural Policy (CAP) The New Palgrave Dictionary of Economics 1–9 (Palgrave Macmillan, 2016).
    Google Scholar 
    Wissinger, S. Cyclic colonization in predictably ephemeral habitats: A template for biological control in annual crop systems. Biol. Control 10, 4–15 (1997).Article 

    Google Scholar 
    Samu, F., Szita, É. & Botos, E. Short- and longer-term colonization of alfalfa by spiders: A case study into the succession of perennial fields. In European Arachnology 2008 (eds Nentwig, W. et al.) 153–163 (Natural History Museum, 2010).
    Google Scholar 
    Samu, F., Horváth, A., Neidert, D., Botos, E. & Szita, É. Metacommunities of spiders in grassland habitat fragments of an agricultural landscape. Basic Appl. Ecol. 31, 92–103. https://doi.org/10.1016/j.baae.2018.07.009 (2018).Article 

    Google Scholar  More

  • in

    Asynchrony in coral community structure contributes to reef-scale community stability

    Parmesan, C. Ecological and evolutionary responses to recent climate change. Annu. Rev. Ecol. Evol. Syst. 37, 637–669 (2006).
    Google Scholar 
    Elahi, R. et al. Recent trends in local-scale marine biodiversity reflect community structure and human impacts. Curr. Biol. 25, 1938–1943 (2015).CAS 

    Google Scholar 
    Harley, C. D. G. Climate change, keystone predation, and biodiversity loss. Science 334, 1124–1127 (2011).ADS 
    CAS 

    Google Scholar 
    Poloczanska, E. S. et al. Global imprint of climate change on marine life. Nat. Clim. Change 3, 919–925 (2013).ADS 

    Google Scholar 
    Bellwood, D. R., Hughes, T. P., Folke, C. & Nyström, M. Confronting the coral reef crisis. Nature 429, 827–833 (2004).ADS 
    CAS 

    Google Scholar 
    Moreno-Mateos, D. et al. Anthropogenic ecosystem disturbance and the recovery debt. Nat. Commun. 8, 14163 (2017).ADS 
    CAS 

    Google Scholar 
    Newman, E. A. Disturbance ecology in the Anthropocene. Front. Ecol. Evol. 7, 147 (2019).
    Google Scholar 
    Mittelbach, G. G. et al. What is the observed relationship between species richness and productivity?. Ecology 82, 2381–2396 (2001).
    Google Scholar 
    van Nes, E. H. & Scheffer, M. Implications of spatial heterogeneity for catastrophic regime shifts in ecosystems. Ecology 86, 1797–1807 (2005).
    Google Scholar 
    Tylianakis, J. M. et al. Resource heterogeneity moderates the biodiversity-function relationship in real world ecosystems. Plos Biol. 6, e122 (2008).
    Google Scholar 
    Loreau, M. et al. In Metacommunities: Spatial Dynamics and Ecological Communities (eds Holyoak, M. et al.) (The University of Chicago Press, 2005).
    Google Scholar 
    Loreau, M. From Populations to Ecosystems (Princeton University Press, 2010). https://doi.org/10.1515/9781400834167.vii.Book 

    Google Scholar 
    Moreira, E. F., Boscolo, D. & Viana, B. F. Spatial heterogeneity regulates plant-pollinator networks across multiple landscape scales. PLoS ONE 10, e0123628 (2015).
    Google Scholar 
    Costanza, J. K., Moody, A. & Peet, R. K. Multi-scale environmental heterogeneity as a predictor of plant species richness. Landsc. Ecol. 26, 851–864 (2011).
    Google Scholar 
    Hughes, T. P. et al. Global warming transforms coral reef assemblages. Nature 556, 492–496 (2018).ADS 
    CAS 

    Google Scholar 
    Nyström, M., Graham, N. A. J., Lokrantz, J. & Norström, A. V. Capturing the cornerstones of coral reef resilience: Linking theory to practice. Coral Reefs 27, 795–809 (2008).ADS 

    Google Scholar 
    Virah-Sawmy, M., Gillson, L. & Willis, K. J. How does spatial heterogeneity influence resilience to climatic changes? Ecological dynamics in southeast Madagascar. Ecol. Monogr. 79, 557–574 (2009).
    Google Scholar 
    Wilson, D. S. Complex interactions in metacommunities, with implications for biodiversity and higher levels of selection. Ecology 73, 1984–2000 (1992).
    Google Scholar 
    Leibold, M. A. et al. The metacommunity concept: A framework for multi-scale community ecology. Ecol. Lett. 7, 601–613 (2004).
    Google Scholar 
    Briggs, C. J. & Hoopes, M. F. Stabilizing effects in spatial parasitoid–host and predator–prey models: A review. Theor. Popul. Biol. 65, 299–315 (2004).MATH 

    Google Scholar 
    Wang, S., Haegeman, B. & Loreau, M. Dispersal and metapopulation stability. PeerJ 3, e1295 (2015).
    Google Scholar 
    Tilman, D. The ecological consequences of changes in biodiversity: A search for general principles. Ecology 80, 1455–1474 (1999).
    Google Scholar 
    Loreau, M., Mouquet, N. & Gonzalez, A. Biodiversity as spatial insurance in heterogeneous landscapes. Proc. Natl. Acad. Sci. 100, 12765–12770 (2003).ADS 
    CAS 

    Google Scholar 
    Yachi, S. & Loreau, M. Biodiversity and ecosystem productivity in a fluctuating environment: The insurance hypothesis. Proc. Natl. Acad. Sci. 96, 1463–1468 (1999).ADS 
    CAS 

    Google Scholar 
    Bouvier, T. et al. Contrasted effects of diversity and immigration on ecological insurance in marine bacterioplankton communities. PLoS ONE 7, e37620 (2012).ADS 
    CAS 

    Google Scholar 
    Hammond, M., Loreau, M., Mazancourt, C. & Kolasa, J. Disentangling local, metapopulation, and cross-community sources of stabilization and asynchrony in metacommunities. Ecosphere 11, e03078 (2020).
    Google Scholar 
    Lamy, T., Legendre, P., Chancerelle, Y., Siu, G. & Claudet, J. Understanding the spatio-temporal response of coral reef fish communities to natural disturbances: Insights from beta-diversity decomposition. PLoS ONE 10, e0138696 (2015).
    Google Scholar 
    Lamy, T. et al. Species insurance trumps spatial insurance in stabilizing biomass of a marine macroalgal metacommunity. Ecology 100, e02719 (2019).
    Google Scholar 
    Stier, A. C., Shelton, A. O., Samhouri, J. F., Feist, B. E. & Levin, P. S. Fishing, environment, and the erosion of a population portfolio. Ecosphere https://doi.org/10.1002/ecs2.3283 (2020).Article 

    Google Scholar 
    Burgess, S. C. et al. Beyond connectivity: How empirical methods can quantify population persistence to improve marine protected-area design. Ecol. Appl. 24, 257–270 (2014).
    Google Scholar 
    Saenz-Agudelo, P., Jones, G. P., Thorrold, S. R. & Planes, S. Connectivity dominates larval replenishment in a coastal reef fish metapopulation. Proc. R. Soc. B Biol. Sci. 278, 2954–2961 (2011).
    Google Scholar 
    Wood, S., Paris, C. B., Ridgwell, A. & Hendy, E. J. Modelling dispersal and connectivity of broadcast spawning corals at the global scale. Glob. Ecol. Biogeogr. 23, 1–11 (2014).
    Google Scholar 
    Loreau, M. et al. Biodiversity as insurance: From concept to measurement and application. Biol. Rev. https://doi.org/10.1111/brv.12756 (2021).Article 

    Google Scholar 
    Thibaut, L. M. & Connolly, S. R. Understanding diversity–stability relationships: Towards a unified model of portfolio effects. Ecol. Lett. 16, 140–150 (2013).
    Google Scholar 
    Wilcox, K. R. et al. Asynchrony among local communities stabilises ecosystem function of metacommunities. Ecol. Lett. 20, 1534–1545 (2017).
    Google Scholar 
    Loreau, M. & de Mazancourt, C. Species synchrony and its drivers: Neutral and nonneutral community dynamics in fluctuating environments. Am. Nat. 172, E48–E66 (2008).
    Google Scholar 
    Loreau, M. & Mazancourt, C. Biodiversity and ecosystem stability: A synthesis of underlying mechanisms. Ecol. Lett. 16, 106–115 (2013).
    Google Scholar 
    Gross, K. et al. Species richness and the temporal stability of biomass production: A new analysis of recent biodiversity experiments. Am. Nat. 183, 1–12 (2014).
    Google Scholar 
    Sullaway, G. H., Shelton, A. O. & Samhouri, J. F. Synchrony erodes spatial portfolios of an anadromous fish and alters availability for resource users. J. Anim. Ecol. https://doi.org/10.1111/1365-2656.13575 (2021).Article 

    Google Scholar 
    Adjeroud, M., Augustin, D., Galzin, R. & Salvat, B. Natural disturbances and interannual variability of coral reef communities on the outer slope of Tiahura (Moorea, French Polynesia): 1991 to 1997. Mar. Ecol. Prog. Ser. 237, 121–131 (2002).ADS 

    Google Scholar 
    Adjeroud, M. et al. Recurrent disturbances, recovery trajectories, and resilience of coral assemblages on a South Central Pacific reef. Coral Reefs 28, 775–780 (2009).ADS 

    Google Scholar 
    Pratchett, M. S., Trapon, M., Berumen, M. L. & Chong-Seng, K. Recent Disturbances Augment Community Shifts in Coral Assemblages in Moorea, French Polynesia (SpringerLink, 2011). https://doi.org/10.1007/s00338-010-0678-2.Book 

    Google Scholar 
    Kayal, M. et al. Predator crown-of-thorns starfish (Acanthaster planci) outbreak, mass mortality of corals, and cascading effects on reef fish and benthic communities. PLoS ONE 7, e47363 (2012).ADS 
    CAS 

    Google Scholar 
    McWilliam, M., Pratchett, M. S., Hoogenboom, M. O. & Hughes, T. P. Deficits in functional trait diversity following recovery on coral reefs. Proc. R. Soc. B 287, 20192628 (2020).
    Google Scholar 
    Hoegh-Guldberg, O. et al. Coral reefs under rapid climate change and ocean acidification. Science 318, 1737–1742 (2007).ADS 
    CAS 

    Google Scholar 
    Penin, L., Adjeroud, M., Schrimm, M. & Lenihan, H. S. High spatial variability in coral bleaching around Moorea (French Polynesia): Patterns across locations and water depths. C. R. Biol. 330, 171–181 (2007).
    Google Scholar 
    Adam, T. C. et al. Herbivory, connectivity, and ecosystem resilience: Response of a coral reef to a large-scale perturbation. PLoS ONE 6, e23717 (2011).ADS 
    CAS 

    Google Scholar 
    Edmunds, P. et al. Why more comparative approaches are required in time-series analyses of coral reef ecosystems. Mar. Ecol. Prog. Ser. 608, 297–306 (2019).ADS 

    Google Scholar 
    Pérez-Rosales, G. et al. Documenting decadal disturbance dynamics reveals archipelago-specific recovery and compositional change on Polynesian reefs. Mar. Pollut. Bull. 170, 112659 (2021).
    Google Scholar 
    Bruno, J. F. & Selig, E. R. Regional decline of coral cover in the Indo-Pacific: Timing, extent, and subregional comparisons. PLoS ONE 2, e711 (2007).ADS 

    Google Scholar 
    Jackson, J. B. C. et al. Status and trends of Caribbean coral reefs. Global Coral Reef Monitoring Network, IUCN, Gland, Switzerland (2014)Edmunds, P. J. Implications of high rates of sexual recruitment in driving rapid reef recovery in Mo’orea, French Polynesia. Sci. Rep. 8, 16615 (2018).ADS 

    Google Scholar 
    Burgess, S. C., Johnston, E. C., Wyatt, A. S. J., Leichter, J. J. & Edmunds, P. J. Response diversity in corals: Hidden differences in bleaching mortality among cryptic Pocillopora species. Ecology https://doi.org/10.1002/ecy.3324 (2021).Article 

    Google Scholar 
    Holbrook, S. J. et al. Recruitment drives spatial variation in recovery rates of resilient coral reefs. Sci. Rep. 8, 7338 (2018).ADS 

    Google Scholar 
    Guest, J. R. et al. A framework for identifying and characterising coral reef “oases” against a backdrop of degradation. J. Appl. Ecol. 55, 2865–2875 (2018).
    Google Scholar 
    Hench, J. L., Leichter, J. J. & Monismith, S. G. Episodic circulation and exchange in a wave-driven coral reef and lagoon system. Limnol. Oceanogr. 53, 2681–2694 (2008).ADS 

    Google Scholar 
    Barry, J. P. & Dayton, P. K. Ecological heterogeneity. Ecol. Stud. https://doi.org/10.1007/978-1-4612-3062-5_14 (1991).Article 

    Google Scholar 
    Edmunds, P. & Bruno, J. The importance of sampling scale in ecology: Kilometer-wide variation in coral reef communities. Mar. Ecol. Prog. Ser. 143, 165–171 (1996).ADS 

    Google Scholar 
    Lough, J. M., Anderson, K. D. & Hughes, T. P. Increasing thermal stress for tropical coral reefs: 1871–2017. Sci. Rep. 8, 6079 (2018).ADS 
    CAS 

    Google Scholar 
    van Oppen, M. J. H. & Lough, J. M. Coral bleaching, patterns, processes, causes and consequences. Ecol. Stud. https://doi.org/10.1007/978-3-319-75393-5_14 (2018).Article 

    Google Scholar 
    Monismith, S. G. Hydrodynamics of coral reefs. Annu. Rev. Fluid Mech. 39, 37–55 (2007).ADS 
    MATH 

    Google Scholar 
    Edmunds P. Of Moorea Coral Reef LTER. MCR LTER: Coral Reef: Long-term Population and Community Dynamics: Corals, ongoing since 2005. knb-lter-mcr.4.33 https://doi.org/10.6073/pasta/1f05f1f52a2759dc096da9c24e88b1e8 (2020).Cowles, J. et al. Resilience: insights from the U.S. Long-term ecological research network. Ecosphere 12, e03434 (2021).
    Google Scholar 
    Beijbom, O. et al. Towards automated annotation of benthic survey images: Variability of human experts and operational modes of automation. PLoS ONE 10, e0130312 (2015).
    Google Scholar 
    Veron, J. E. N. Corals of the world, v. 1–3. Australian Institute of Marine Science (2000)Washburn, L of Moorea Coral Reef LTER. MCR LTER: Coral Reef: Ocean Currents and Biogeochemistry: salinity, temperature and current at CTD and ADCP mooring FOR01 from 2004 ongoing. knb-lter-mcr.30.36doi:10.6073/pasta/124d19950c5234bf1937661989dcced7 (2021).Safaie, A. et al. High frequency temperature variability reduces the risk of coral bleaching. Nat. Commun. 9, 1671 (2018).ADS 

    Google Scholar 
    Dean, R. G. & Dalrymple, R. A. Water Wave Mechanics for Engineers and Scientists. Advanced Series on Ocean Engineering Vol. 2 (World Scientific, 1991).
    Google Scholar 
    Carroll, A., Harrison, P. & Adjeroud, M. Sexual reproduction of Acropora reef corals at Moorea, French Polynesia. Coral Reefs 25, 93–97 (2006).ADS 

    Google Scholar 
    Han, X., Adam, T. C., Schmitt, R. J., Brooks, A. J. & Holbrook, S. J. Response of herbivore functional groups to sequential perturbations in Moorea, French Polynesia. Coral Reefs 35, 999–1009 (2016).ADS 

    Google Scholar 
    Clarke, K. R. Non-parametric multivariate analyses of changes in community structure. Austral Ecol. 18, 117–143 (1993).
    Google Scholar 
    Clarke, K. R., Somerfield, P. J. & Chapman, M. G. On resemblance measures for ecological studies, including taxonomic dissimilarities and a zero-adjusted Bray–Curtis coefficient for denuded assemblages. J. Exp. Mar. Biol. Ecol. 330, 55–80 (2006).
    Google Scholar 
    RStudio Team. RStudio: Integrated development for R. RStudio, PBC, Boston, MA URL http://www.rstudio.com/ (2021).Oksanen J. et al. vegan: Community ecology package. R package version 2.5–7. https://CRAN.R-project.org/package=vegan (2020).Wickham, et al. Welcome to the Tidyverse. J. Open Source Softw. 4(43), 1686. https://doi.org/10.21105/joss.01686 (2019).Article 
    ADS 

    Google Scholar 
    Corlett, R. T. The Anthropocene concept in ecology and conservation. Trends Ecol. Evol. 30, 36–41 (2015).
    Google Scholar 
    Williams, G. J. et al. Coral reef ecology in the Anthropocene. Funct. Ecol. 33, 1014–1022 (2019).
    Google Scholar 
    Walther, G.-R. et al. Ecological responses to recent climate change. Nature 416, 389–395 (2002).ADS 
    CAS 

    Google Scholar 
    Walther, G.-R. Community and ecosystem responses to recent climate change. Philos. Trans. R. Soc. B Biol. Sci. 365, 2019–2024 (2010).
    Google Scholar 
    Cinner, J. E. et al. Bright spots among the world’s coral reefs. Nature 535, 416–419 (2016).ADS 
    CAS 

    Google Scholar 
    Grman, E., Lau, J. A., Schoolmaster, D. R. & Gross, K. L. Mechanisms contributing to stability in ecosystem function depend on the environmental context. Ecol. Lett. 13, 1400–1410 (2010).
    Google Scholar 
    Schindler, D. E. et al. Population diversity and the portfolio effect in an exploited species. Nature 465, 609–612 (2010).ADS 
    CAS 

    Google Scholar 
    Doak, D. F. et al. The statistical inevitability of stability-diversity relationships in community ecology. Am. Nat. 151, 264–276 (1998).CAS 

    Google Scholar 
    Isbell, F. I., Polley, H. W. & Wilsey, B. J. Biodiversity, productivity and the temporal stability of productivity: Patterns and processes. Ecol. Lett. 12, 443–451 (2009).
    Google Scholar 
    Connell, J. H. Diversity in tropical rain forests and coral reefs author. Science 199, 1302–1310 (1978).ADS 
    CAS 

    Google Scholar 
    Plaisance, L., Caley, M. J., Brainard, R. E. & Knowlton, N. The diversity of coral reefs: What are we missing?. PLoS ONE 6, e25026 (2011).ADS 
    CAS 

    Google Scholar 
    Williams, G. J. et al. Biophysical drivers of coral trophic depth zonation. Mar. Biol. 165, 60 (2018).
    Google Scholar 
    Moritz, C. et al. Long-term monitoring of benthic communities reveals spatial determinants of disturbance and recovery dynamics on coral reefs. Mar. Ecol. Prog. Ser. 672, 141–152 (2021).ADS 

    Google Scholar 
    Dietzel, A. et al. The spatial footprint and patchiness of large scale disturbances on coral reefs. Global Change Biol. 27, 4825–4838 (2021).CAS 

    Google Scholar 
    Leichter, J. et al. Biological and physical interactions on a tropical island coral reef: Transport and retention processes on Moorea, French Polynesia. Oceanography 26, 52–63 (2011).
    Google Scholar 
    Porter, J. W. et al. Population trends among Jamaican reef corals. Nature 294, 249–250 (1981).ADS 

    Google Scholar 
    Graham, N. A. J., Jennings, S., MacNeil, M. A., Mouillot, D. & Wilson, S. K. Predicting climate-driven regime shifts versus rebound potential in coral reefs. Nature 518, 94–97 (2015).ADS 
    CAS 

    Google Scholar 
    Whittaker, R. H. & Levin, S. A. The role of mosaic phenomena in natural communities. Theor. Popul. Biol. 12, 117–139 (1977).CAS 

    Google Scholar 
    Karlson, R. H. & Hurd, L. E. Disturbance, coral reef communities, and changing ecological paradigms. Coral Reefs 12, 117–125 (1993).ADS 

    Google Scholar 
    Stoddart, D. R. Effects of Hurricane Hattie on the British Honduras reefs and cays, October 30–31, 1961. Atoll Res. Bull. 95, 1–142 (1963).
    Google Scholar 
    Witman, J. D. Physical disturbance and community structure of exposed and protected reefs: A case study from St. John U.S. Virgin Islands. Integr. Comp. Biol. 32, 641–654 (1992).
    Google Scholar 
    Thorson, J. T., Scheuerell, M. D., Olden, J. D. & Schindler, D. E. Spatial heterogeneity contributes more to portfolio effects than species variability in bottom-associated marine fishes. Proc. R. Soc. B 285, 20180915 (2018).
    Google Scholar 
    Mellin, C., MacNeil, M. A., Cheal, A. J., Emslie, M. J. & Caley, M. J. Marine protected areas increase resilience among coral reef communities. Ecol. Lett. 19, 629–637 (2016).
    Google Scholar 
    Beyer, H. L. et al. Risk-sensitive planning for conserving coral reefs under rapid climate change. Conserv. Lett. 11, e12587 (2018).
    Google Scholar 
    Harrison, H. B., Bode, M., Williamson, D. H., Berumen, M. L. & Jones, G. P. A connectivity portfolio effect stabilizes marine reserve performance. Proc. Natl. Acad. Sci. 117, 25595–25600 (2020).ADS 
    CAS 

    Google Scholar 
    Walter, J. A. et al. The spatial synchrony of species richness and its relationship to ecosystem stability. Ecology https://doi.org/10.1002/ecy.3486 (2021).Article 

    Google Scholar 
    Wang, S., Lamy, T., Hallett, L. M. & Loreau, M. Stability and synchrony across ecological hierarchies in heterogeneous metacommunities: Linking theory to data. Ecography 42, 1200–1211 (2019).
    Google Scholar 
    Catano, C. P., Fristoe, T. S., LaManna, J. A. & Myers, J. A. Local species diversity, β-diversity and climate influence the regional stability of bird biomass across North America. Proc. R. Soc. B 287, 20192520 (2020).
    Google Scholar 
    Roscher, C. et al. Identifying population- and community-level mechanisms of diversity–stability relationships in experimental grasslands. J. Ecol. 99, 1460–1469 (2011).
    Google Scholar 
    Downing, A. L., Brown, B. L. & Leibold, M. A. Multiple diversity–stability mechanisms enhance population and community stability in aquatic food webs. Ecology 95, 173–184 (2014).
    Google Scholar 
    Moran, P. The statistical analysis of the Canadian Lynx cycle. Aust. J. Zool. 1, 291–298 (1953).
    Google Scholar 
    Townsend, D. L. & Gouhier, T. C. Spatial and interspecific differences in recruitment decouple synchrony and stability in trophic metacommunities. Theor. Ecol. 12, 319–327 (2019).
    Google Scholar 
    Yeager, M. E., Gouhier, T. C. & Hughes, A. R. Predicting the stability of multitrophic communities in a variable world. Ecology 101, e02992 (2020).
    Google Scholar 
    Hughes, T. P. et al. Emergent properties in the responses of tropical corals to recurrent climate extremes. Curr. Biol. https://doi.org/10.1016/j.cub.2021.10.046 (2021).Article 

    Google Scholar 
    Jackson, J. B. C. Morphological strategies of sessile animals. In Biology and Systematics of Colonial Organisms (eds Larwood, G. & Rosen, B. R.) 499–555 (Academic, 1979).
    Google Scholar 
    Sammarco, P. W. & Andrews, J. C. Localized dispersal and recruitment in Great Barrier Reef Corals: The helix experiment. Science 239, 1422–1424 (1988).ADS 
    CAS 

    Google Scholar 
    Edmunds, P. J. Unusually high coral recruitment during the 2016 El Niño in Mo’orea, French Polynesia. PLoS ONE 12, e0185167 (2017).
    Google Scholar 
    Bull, G. Distribution and abundance of coral plankton. Coral Reefs 4, 197–200 (1986).ADS 

    Google Scholar 
    Hodgson, G. Abundance and distribution of planktonic coral larvae in Kaneohe Bay, Oahu, Hawaii. Mar. Ecol. Prog. Ser. 26, 61–71 (1985).ADS 

    Google Scholar 
    Edmunds, P. J. Vital rates of small reef corals are associated with variation in climate. Limnol. Oceanogr. 66, 901–913 (2021).ADS 

    Google Scholar  More

  • in

    Benthic biota of Chilean fjords and channels in 25 years of cruises of the National Oceanographic Committee

    The data were recorded under the DarwinCore standard55,56 in a matrix named “Benthic biota of CIMAR-Fiordos and Southern Ice Field Cruises”58. The occurrence dataset contains direct basic information (description, scope [temporal, geographic and taxonomic], methodology, bibliography, contacts, data description, GBIF registration and citation), project details, metrics (taxonomy and occurrences classification), activity (citations and download events) and download options. The following data fields were occupied:Column 1: “occurrenceID” (single indicator of the biological record indicating the cruise and correlative record).Column 2: “basisOfRecord” (“PreservedSpecimen” for occurrence records with catalogue number of scientific collection, “MaterialCitation” for any literature record).Column 3: “institutionCode” (The acronym in use by the institution having custody of the sample or information referred to in the record).Column 4: “collectionCode” (The name of the cruise).Column 5: “catalogNumber” (The repository number in museums or correlative number).Column 6: “type” (All records entered as “text”).Column 7: “language” (Spanish, English or both).Column 8: “institutionID” (The identifier for the institution having custody of the sample or information referred to in the record).Column 9: “collectionID” (The identifier for the collection or dataset from which the record was derived).Column 10: “datasetID” (The code “CONA-benthic-biota-database” for entire database).Column 11: “recordedBy” (Author/s who recorded the original occurrence [publication source]).Column 12: “individualCount” (Number of individuals recorded).Column 13: “associatedReferences” (Publication source [report and/or paper/s] for each record).Column 14: “samplingProtocol” (The sampling gear for each record).Column 15: “eventDate” (The date-time or interval during which the record occurred).Column 16: “eventRemarks” (Comments or notes about the event).Column 17: “continent” (Location).Column 18: “country” (Location).Column 19: “countryCode” (The standard code for the country in which the location occurs).Column 20: “stateProvince” (Location, refers to the Administrative Region of Chile).Column 21: “county” (Location, refers to the Administrative Province of Chile).Column 22: “municipality” (Location, refers to the Administrative Commune of Chile).Column 23: “locality” (The specific name of the place).Column 24: “verbatimLocality” (The original textual description of the place).Column 25: “verbatimDepth” (The original description of the depth).Column 26: “minimumDepthInMeters” (The shallowest depth of a range of depths).Column 27: “maximumDepthInMeters” (The deepest depth of a range of depths).Column 28: “locationRemarks” (The name of the sample station of the cruise).Column 29: “verbatimLatitude” (The verbatim original latitude of the location).Column 30: “verbatimLongitude” (The verbatim original longitude of the location).Column 31: “verbatimCoordinateSystem” (The coordinate format for the “verbatimLatitude” and “verbatimLongitude” or the “verbatimCoordinates” of the location).Column 32: “verbatimSRS” (The spatial reference system [SRS] upon which coordinates given in “verbatimLatitude” and “verbatimLongitude” are based)Column 33: “decimalLatitude” (The geographic latitude in decimal degrees).Column 34: “decimalLongitude” (The geographic longitude in decimal degrees).Column 35: “geodeticDatum” (The spatial reference system [SRS] upon which the geographic coordinates given in “decimalLatitude” and “decimalLongitude” was based).Column 36: “coordinateUncertaintyInMeters” (The horizontal distance from the given “decimalLatitude” and “decimalLongitude” describing the smallest circle containing the whole of the location).Column 37: “georeferenceRemarks” (Notes about the spatial description determination).Column 38: “identifiedBy” (Responsible for recording the original occurrence [publication source]).Column 39: “dateIdentified” (The date-time or interval during which the identification occurred.)Column 40: “identificationQualifier” (A taxonomic determination [e.g., “sp.”, “cf.”]).Column 41: “scientificNameID” (An identifier for the nomenclatural details of a scientific name).Column 42: “scientificName” (The name of species or taxon of the occurrence record).Column 43: “kingdom” (The scientific name of the kingdom in which the taxon is classified).Column 44: “phylum” (The scientific name of the phylum or division in which the taxon is classified).Column 45: “class” (The scientific name of the class in which the taxon is classified).Column 46: “order” (The scientific name of the order in which the taxon is classified).Column 47: “family” (The scientific name of the family in which the taxon is classified).Column 48: “genus” (The scientific name of the genus in which the taxon is classified).Column 49: “subgenus” (The scientific name of the subgenus in which the taxon is classified).Column 50: “specificEpithet” (The name of the first or species epithet of the “scientificName”).Column 51: “infraspecificEpithet” (The name of the lowest or terminal infraspecific epithet of the “scientificName”).Column 52: “taxonRank” (The taxonomic rank of the most specific name in the “scientificName”).Column 53: “scientificNameAuthorship” (The authorship information for the “scientificName” formatted according to the conventions of the applicable nomenclatural Code).Column 54: “verbatimIdentification” (A string representing the taxonomic identification as it appeared in the original record).The information sources (see Fig. 2b) provided a total of 107 publications (22 cruise reports and 85 scientific papers; see Fig. 2c). Nineteen of the 22 cruise reports reviewed provided species occurrence records8,28,29,30,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46, one provided qualitative or descriptive data, with no recorded occurrences31, and two did not provide information on benthic biota (CIMAR-9 and −23 cruises). Of all the scientific papers reviewed, 74 provided records of species occurrences (Table 2), while 11 did not provide any record, as they were data without occurrences of geographically referenced species or with descriptive or qualitative information: Foraminifera59,60,61,62, Annelida63,64,65,66, Fishes67, Mollusca68 and Echinodermata69. The phyla with the highest number of publications were the following: Annelida (present in 18 reports and 21 papers), Mollusca (in 14 and 20), Arthropoda (in 10 and 18), Echinodermata (in 10 and 9), Chordata (in 10 and 9) and Foraminifera (in 4 and 10).Table 2 Publications with >100 occurrences, indicating the main recorded taxa.Full size tableThe information registry includes data on occurrences and number of individuals for 8,854 records (files in the database), representing 1,225 species (Fig. 3). The main taxa in terms of occurrence and number of species were Annelida (mainly Polychaeta), Foraminifera, Mollusca and Arthopoda (mainly Crustacea), together accumulating ~70% of total occurrences and ~73% of the total species (Fig. 3). The large number of recorded occurrences of Myzozoa (10%) should be highlighted, which, however, only represent about 32 species. Echinodermata represented ~8% of occurrences and 7% of species.Fig. 3Occurrences and total species by taxon, considering large taxonomic groups of the benthic biota recorded in the CIMAR 1 to 25 and CDHS-1995 cruises. The absolute values of occurrences and species are represented in parentheses.Full size imageThe cruises with the highest number of occurrences were CIMAR-2 (with 1,424), followed by CIMAR-8 (1,040) and CIMAR-16 (813) (Fig. 4). Three dominant taxonomic groups were recorded in most cruises, except for cruises CIMAR-1, CIMAR-4, CIMAR-17, CIMAR-18 and CIMAR-24 (Fig. 4). The cruises with the highest number of species recorded were CIMAR-2 (with 335), CIMAR-3 (328) and CIMAR-8 (323) (Fig. 5). Three or fewer dominant taxonomic groups were recorded only in the CIMAR-1, CIMAR-4, CIMAR-17, CIMAR-18 and CIMAR-24 cruises (Fig. 5).Fig. 4Total occurrences and percentages per dominant taxon recorded in each of the CIMAR 1 to 25 and CDHS-1995 cruises. The absolute values of occurrences per dominant taxon are represented in parentheses.Full size imageFig. 5Total species and percentages per dominant taxon recorded in each of the CIMAR 1 to 25 and CDHS-1995 cruises. The absolute values of species per dominant taxon are represented in parentheses.Full size imageThe latitudinal bands 42°S and 45°S are those with the highest number of occurrences (Fig. 6), while the 56°S and 46°S bands had the fewest. The highest number of species was recorded in the 52°S and 50°S latitudinal bands, while, as with the occurrences, the lowest values corresponded to the 56°S and 46°S latitudinal bands (Fig. 6).Fig. 6Occurrences and number of species recorded by latitudinal band from the CIMAR 1 to 25 and CDHS-1995 cruises. SEP: South-eastern Pacific.Full size image More

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    Genetic population structures of common scavenging species near hydrothermal vents in the Okinawa Trough

    Van Dover, C. L. et al. Environmental management of deep-sea chemosynthetic ecosystems: justification of and considerations for a spatially based approach. ISA Technical Study: No.9. (International Seabed Authority, 2011).Ikehata, K., Suzuki, R., Shimada, K., Ishibashi, J., & Urabe, T. Mineralogical and Geochemical Characteristics of Hydrothermal Minerals Collected from Hydrothermal Vent Fields in the Southern Mariana Spreading Center. In Subseafloor biosphere linked to hydrothermal systems: TAIGA Concept. 275–288 (Springer Tokyo, 2015).Rona, P. A. & Scott, S. D. A special issue on sea-floor hydrothermal mineralization; new perspectives; preface. Econ. Geol. 88, 1935–1976 (1993).
    Google Scholar 
    Glasby, G. P., Iizasa, K., Yuasa, M. & Usui, A. Submarine hydrothermal mineralization on the Izu-Bonin arc, south of Japan: an overview. Mar. Georesources Geotech. 18, 141–176 (2000).
    Google Scholar 
    Van Dover, C. L. Inactive sulfide ecosystems in the deep sea: a review. Front. Mar. Sci. 6, 461. https://doi.org/10.3389/fmars.2019.00461 (2019).Article 

    Google Scholar 
    Boschen, R. E., Rowde, A. A., Clark, M. R. & Gardner, J. P. Mining of deep-sea seafloor massive sulfides: a review of the deposits, their benthic communities, impacts from mining, regulatory frameworks and management strategies. Ocean Coast. Manag. 84, 54–67 (2013).
    Google Scholar 
    Washburn, T. W. et al. Ecological risk assessment for deep-sea mining. Ocean Coast. Manag. 176, 24–39 (2019).
    Google Scholar 
    Matsui, T., Sugishima, H., Okamoto, N., Igarashi, Y. Evaluation of turbidity and resedimentation through seafloor disturbance experiments for assessment of environmental impacts associated with exploitation of seafloor massive sulfides mining. Proceedings of the Twenty-eighth. International Ocean and Polar Engineering Conference. 144–151 (2018).International Seabed Authority. Recommendations for the guidance of contractors for the assessment of the possible environmental impacts arising from exploration for marine minerals in the Area. https://www.isa.org.jm/documents/isba19ltc8 (2013).Suzuki, K., Yoshida, K., Watanabe, H. & Yamamoto, H. Mapping the resilience of chemosynthetic communities in hydrothermal vent fields. Sci. Rep. 8, 9364. https://doi.org/10.1038/s41598-018-27596-7 (2018).Article 
    ADS 
    CAS 

    Google Scholar 
    Yahagi, T., Watanabe, H., Ishibashi, J. I. & Kojima, S. Genetic population structure of four hydrothermal vent shrimp species (Alvinocarididae) in the Okinawa Trough, Northwest Pacific. Mar. Ecol. Prog. Ser. 529, 159–169 (2015).ADS 

    Google Scholar 
    Mullineaux, L. S. Deep-sea hydrothermal vent communities. In Marine community ecology and conservation (eds Bertness, M. D. et al.) 383–400 (Sinauer, 2013).
    Google Scholar 
    Van Dover, C. L., German, C. R., Speer, K. G., Parson, L. M. & Vrijenhoek, R. C. Evolution and biogeography of deep-sea vent and seep invertebrates. Science 295, 1253–1257 (2002).ADS 

    Google Scholar 
    Yahagi, T., Kayama-Watanabe, H., Kojima, S. & Kano, Y. Do larvae from deep-sea hydrothermal vents disperse in surface waters?. Ecology 98, 1524–1534 (2017).
    Google Scholar 
    Hebert, P. D. & Gregory, T. R. The promise of DNA barcoding for taxonomy. Syst. Biol. 54, 852–859 (2005).
    Google Scholar 
    Iguchi, A. et al. Comparative analysis on the genetic population structures of the deep-sea whelks Buccinum tsubai and Neptunea constricta in the Sea of Japan. Mar. Biol. 151, 31–39 (2007).
    Google Scholar 
    Goode, G. B. & Bean, T. H. A catalogue of the fishes of Essex County, Massachusetts, including the fauna of Massachusetts Bay and the contiguous deep waters. Bull. Essex Inst. 11, 1–38 (1879).
    Google Scholar 
    Johnson, J. Y. Descriptions of some new genera and species of fishes obtained at Madeira. Proc. Zool. Soc. Lond. 1862, 167–180 (1862).
    Google Scholar 
    Bate, C. S. Report on the Crustacea Macrura collected by the Challenger during the years 1873–76. Report on the scientific results of the Voyage of H.M.S. Challenger during the years 1873–76. Zoology 24, 1–942 (1888).
    Google Scholar 
    Folmer, O., Black, M., Hoeh, W. R., Lutz, R. & Vrijenhoek, R. C. DNA primers for amplification of mitochondrial cytochrome c oxidase subunit I from diverse metazoan invertebrates. Mol. Mar. Biol Biotech. 3, 294–299 (1994).CAS 

    Google Scholar 
    Pilgrim, E. M., Blum, M. J., Reusser, D. A., Lee, H. & Darling, J. A. Geographic range and structure of cryptic genetic diversity among Pacific North American populations of the non-native amphipod Grandidierella japonica. Biol. Invasions 15, 2415–2428 (2013).
    Google Scholar 
    Suyama, Y. & Matsuki, Y. MIG-seq: an effective PCR-based method for genome-wide single-nucleotide polymorphism genotyping using the next-generation sequencing platform. Sci. Rep. 5, 16963. https://doi.org/10.1038/srep16963 (2015).Article 
    ADS 
    CAS 

    Google Scholar 
    R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org/ (2020).Paradis, E., Claude, J. & Strimmer, K. APE: analyses of phylogenetics and evolution in R language. Bioinformatics 20, 289–290 (2004).CAS 

    Google Scholar 
    Katoh, K. & Standley, D. M. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol. Biol. Evol. 30, 772–780 (2013).CAS 

    Google Scholar 
    Shen, W., Le, S., Li, Y. & Hu, F. SeqKit: a cross-platform and ultrafast toolkit for FASTA/Q file manipulation. PLoS ONE 11, e0163962. https://doi.org/10.1371/journal.pone.0163962 (2016).Article 
    CAS 

    Google Scholar 
    Paradis, E. pegas: an R package for population genetics with an integrated–modular approach. Bioinformatics 26, 419–420 (2010).CAS 

    Google Scholar 
    Kumar, S., Stecher, G. & Tamura, K. MEGA7: molecular evolutionary genetics analysis version 7.0 for bigger datasets. Mol. Biol. Evol. 33, 1870–1874 (2016).CAS 

    Google Scholar 
    Darriba, D. et al. ModelTest-NG: a new and scalable tool for the selection of DNA and protein evolutionary models. Mol. Biol. Evol. 37, 291–294 (2020).CAS 

    Google Scholar 
    Kozlov, A. M., Darriba, D., Flouri, T., Morel, B. & Stamatakis, A. RaxML-NG: a fast, scalable and user-friendly tool for maximum likelihood phylogenetic inference. Bioinformatics 35, 4453–4455 (2019).CAS 

    Google Scholar 
    Ronquist, F. R. & Huelsenbeck, J. P. MRBAYES 3: Bayesian inference of phylogeny. Bioinformatics 19, 1572–1574 (2003).CAS 

    Google Scholar 
    Puillandre, N., Brouillet, S. & Achaz, G. ASAP: assemble species by automatic partitioning. Mol. Ecol. Resour. 21, 609–620 (2021).
    Google Scholar 
    Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.journal 17, http://journal.embnet.org/index.php/embnetjournal/article/view/200/479 (2011).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 (2019).CAS 

    Google Scholar 
    Purcell, S. et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81, 559–575 (2007).CAS 

    Google Scholar 
    Jombart, T. adegenet: a R package for the multivariate analysis of genetic markers. Bioinformatics 24, 1403–1405 (2008).CAS 

    Google Scholar 
    Goudet, J. Hierfstat, a package for R to compute and test hierarchical F-statistics. Mol. Ecol. Notes 5, 184–186 (2013).
    Google Scholar 
    Oksanen, J. et al. vegan: Community Ecology Package. R package version 2.5–6. https://CRAN.R-project.org/package=vegan (2019).Dana, J. D. Synopsis of the genera of Gammaracea. Am. J. Sci. Arts 8, 135–140 (1849).
    Google Scholar 
    Hansen, H. J. Malacostraca marina Groenlandiæ occidentalis Oversigt over det vestlige Grønlands Fauna af malakostrake Havkrebsdyr. Vidensk. Meddel. Natuirist. Foren Kjobenhavn, Aaret 9, 5–226 (1888).
    Google Scholar 
    Van Dover, C. L. The ecology of deep-sea hydrothermal vents (Princeton University Press, 2000).
    Google Scholar 
    Tunnicliffe, V. The biology of hydrothermal vents: ecology and evolution. Oceanogr. Mar. Biol. Annu. Rev. 29, 319–407 (1991).
    Google Scholar 
    Priede, I. G., Bagley, P. M., Smith, A., Creasey, S. & Merrett, N. R. Scavenging deep demersal fishes of the Porcupine Seabight, north-east Atlantic: observations by baited camera, trap and trawl. J. Mar. Biol. Assoc. U. K. 74, 481–498 (1994).
    Google Scholar 
    Causse, R., Biscoito, M. & Briand, P. First record of the deep-sea eel Ilyophis saldanhai (Synaphobranchidae, Anguilliformes) from the Pacific Ocean. Cybium 29, 413–416 (2005).
    Google Scholar 
    King, N. J., Bagley, P. M. & Priede, I. G. Depth zonation and latitudinal distribution of deep-sea scavenging demersal fishes of the Mid-Atlantic Ridge, 42 to 53°N. Mar. Ecol. Prog. Ser. 319, 263–274 (2006).ADS 

    Google Scholar 
    Leitner, A. B., Durden, J. M., Smith, C. R., Klingberg, E. D. & Drazen, J. C. Synaphobranchid eel swarms on abyssal seamounts: largest aggregation of fishes ever observed at abyssal depths. Deep Sea Res. Oceanogr. Res. Part I Pap. 167, 103423. https://doi.org/10.1016/j.dsr.2020.103423 (2021).Article 

    Google Scholar 
    Fishelson, L. Comparative internal morphology of deep-sea eels, with particular emphasis on gonads and gut structure. J. Fish. Biol. 44, 75–101 (1994).
    Google Scholar 
    Bailey, D. M. et al. High swimming and metabolic activity in the deep-sea eel Synaphobranchus kaupii revealed by integrated in situ and in vitro measurements. Physiol. Biochem. Zool. 78, 335–346 (2005).
    Google Scholar 
    Trenkel, V. M. & Lorance, P. Estimating Synaphobranchus kaupii densities: contribution of fish behaviour to differences between bait experiments and visual strip transects. Deep Sea Res. Oceanogr. Res. Part I Pap. 58, 63–71 (2011).ADS 

    Google Scholar 
    Raupach, M. J. et al. Genetic homogeneity and circum-Antarctic distribution of two benthic shrimp species of the Southern Ocean, Chorismus antarcticus and Nematocarcinus lanceopes. Mar. Biol. 157, 1783–1797 (2010).CAS 

    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 (2016).ADS 
    CAS 

    Google Scholar 
    Dambach, J., Raupach, M. J., Mayer, C., Schwarzer, J. & Leese, F. Isolation and characterization of nine polymorphic microsatellite markers for the deep-sea shrimp Nematocarcinus lanceopes (Crustacea: Decapoda: Caridea). BMC Res. Notes 6, 75. https://doi.org/10.1186/1756-0500-6-75 (2013).Article 

    Google Scholar 
    Ritchie, H., Jamieson, A. J. & Piertney, S. B. Phylogenetic relationships among hadal amphipods of the Superfamily Lysianassoidea: Implications for taxonomy and biogeography. Deep Sea Res. Part I 105, 119–131 (2015).CAS 

    Google Scholar 
    Bowen, B. W. et al. Phylogeography unplugged: comparative surveys in the genomic era. Bull. Mar. Sci. 90, 13–46 (2014).
    Google Scholar 
    Ritchie, H., Jamieson, A. J. & Piertney, S. B. Population genetic structure of two congeneric deep-sea amphipod species from geographically isolated hadal trenches in the Pacific Ocean. Deep Sea Res. Part I. 119, 50–57 (2017).
    Google Scholar 
    Iguchi, A. et al. Deep-sea amphipods around cobalt-rich ferromanganese crusts: taxonomic diversity and selection of candidate species for connectivity analysis. PLoS ONE 15, e0228483. https://doi.org/10.1371/journal.pone.0228483 (2020).Article 
    CAS 

    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 (2016).
    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 (2017).CAS 

    Google Scholar  More