Philippa Kaur

Goose capture and trackingWe used rocket netting and leg snares to capture white-fronted geese in three regions in Texas (Rolling Plains, Lower Texas Coast, and South Texas Brushlands) and one region in Louisiana (Chenier Plain) from October to February 2016–2018 (Fig. 1). We determined age and sex of individuals by cloacal inversion, rectrices and other plumage characteristics27,28. We fit a solar powered GPS/ACC/Global System for Mobile communication (GSM) neckband tracking device (Cellular Tracking Technologies Versions BT3.0, BT3.5 and BT3.75; 44–54 g; Rio Grande, New Jersey, USA, and Ornitela OrniTrack-N38; 36 g; Vilnius, Lithuania), and an aluminum U.S. Geological Survey Bird Banding Laboratory metal leg band (Supplementary Fig. S1) on each bird. Geese were captured and tagged under USGS Bird Banding Permits #21314 and #23792, and Texas A&M University-Kingsville Institutional Animal Care and Use Committee #2015-09-01B. Captive geese were permitted under TAMUK IACUC #2018-01-11 and United States Fish and Wildlife Service Waterfowl Sale and Disposal permit #MB03808D-0. All applicable field methods were carried out in accordance with relevant guidelines and regulations. All animal handling protocols were approved by TAMUK IACUC committees and the USGS Bird Banding Laboratory. When multiple white-fronted geese were captured simultaneously, devices were only placed on adult females or adult males to eliminate the potential of placing devices on mated pairs, thus biasing independent data collection due to monogamous, long-term pair bonds in white-fronted geese. Location duty cycles were set to collect a GPS location every 30 min (i.e., 48/day) and location accuracy was 7.2 and 6.5 m for CTT and Ornitela devices, respectively. Data were uploaded once daily to respective online user interface websites when within areas of GSM coverage. When not in coverage areas, data were stored onboard the device until birds returned to coverage areas. All devices were equipped with a tri-axial ACC sensor which measured G-force (g; CTT devices) or millivolts (mV; Ornitela devices) at a fixed sampling scheme; CTT BT3.5 and Ornitela devices collected ACC data for a duration of 3 s every 6 min at 10 Hz, while BT3.0 devices collected data for a duration of 10 s every 6 min at 10 Hz. Generation BT3.0 devices were subsampled to match the sampling scheme of 3 s bursts before analyses. Ornitela units measured in mV were converted to G-force. We applied manufacturer- and tag-specific ACC calibration to all units, respectively, by collecting ACC data on each possible rotation for all axes when the device was stationary and applying the calibration to the raw ACC values (see Ref.29 for full calibration procedure). All devices recorded temperature in °C at each GPS fix. We censored GPS and ACC data from the time of release until individuals appeared to resume normal movement activity (i.e., roosting and foraging), as geese typically flew to the nearest wetland immediately after release where they remained without leaving while acclimating to wearing devices, which ranged from 1 to 7 days30. We defined the start of the winter period following a southward migratory movement from staging areas in Canada, without additional migratory movements southward below 40° 0′ 00″ N, or from the time of device deployment (minus device acclimation period) until geese made large northward migratory movements, or 28 February if geese remained in wintering areas.Figure 1Primary wintering regions of the Midcontinent population of greater white-fronted goose (Anser albifrons frontalis) in North America (excluding regions in Mexico). Transmitters were deployed during winters 2016–2018 in the Chenier Plain (Louisiana), Lower Texas Coast, and Rolling/High Plains regions. Geese that made winter movements outside of these defined regions were classified as ‘Other’ regions. Map created using Esri ArcMap (version 10.3.1; www.esri.com).Full size imageLand cover covariatesWe used publicly available spatial landcover data sets (30-m resolution) in combination with remote sensing to create landscape layers using programs Esri ArcMap (version 10.3.1), Erdas Imagine, and Program R (version 3.5.231). We used 2017 and 2018 National Agricultural Statistics Service Cropland Data Layer (CDL) data sets for agricultural crop types and freshwater wetlands, and the 2010 Coastal Change Analysis Program layer for saltwater and coastal wetland classifications29,32. Additionally, we used multi-spectral Landsat 8 Operational Land Imager satellite imagery, with principal component analysis on eight Landsat bands and a normalized difference vegetation index band, and unsupervised classification33,34 to accurately identify and create a spatial layer for peanut fields. We developed this layer for two regions with annual peanut agriculture (i.e., the Rolling/High Plains and South Texas Brushlands) using ground-truthed peanut fields, because the CDL layer did not identify this crop accurately based on our field observations during captures. We achieved  > 90% accuracy of peanut identification for each image independently based on annual ground-truthed observations of peanut fields. Finally, we grouped like-habitat categories to reduce the total number of categories to eight: corn, grass/winter wheat, herbaceous wetlands, other grains (i.e., soybeans, sorghum, and peanuts), rice, woody wetlands, open water/unconsolidated shore and other (Supplementary Table S1). White-fronted geese used several ecologically distinct regions in both winters of our study (Fig. 1), where the landscape composition of specific landcover types varied. To account for regional variability, we added region ID as a categorical variable to all GPS locations. Regions included the MAV, Chenier Plain, Texas Mid-coast, Lower Texas Coast, South Texas Brushlands, Texas Rolling/High Plains, and Other (i.e., locations outside of these identified wintering regions; Fig. 1). We used regional shapefiles of Gulf Coast Joint Venture Initiative Areas (Laguna Madre [Lower Texas Coast], Texas Mid-coast, and Chenier Plain35), and Level III Ecoregions (Mississippi Alluvial Valley, Texas Rolling/High Plains, and South Texas Brushlands36) as boundaries to classify data into regions. Due to insufficient and incompatible spatial layers for Mexico, we limited analyses to locations within the US.Location and acceleration data collectionRemotely determining behaviors of individuals using ACC data is most accurately addressed by developing a training dataset of known behaviors linked with ACC measurements of those behaviors18,37. To develop a training dataset, we collected video footage of two domestic white-fronted geese in Texas, US, and 18 tagged wild Greenland white-fronted geese (A. a. flavirostris) fitted with the same device types and the same data collection scheme, in Wexford, Ireland and Hvanneyri, Iceland during winters 2017–2018. We supplemented wild recordings with behavioral recordings of captive white-fronted geese as a proxy for wild individuals due to difficulty filming wild geese in inclement weather and obstructed video footage, which is common in ACC literature19,20,38,39. To replicate devices placed on wild white-fronted geese and account for potential variation in ACC measurements between device brands, among device versions and individual geese, we deployed three versions of devices used in this study on captive white-fronted geese during filming sessions38,40. We attached tracking devices to captive geese one week prior to video collection to allow geese to adjust to wearing devices. We collected ACC measurements for 3 s bursts, at 1 min intervals, at 10 Hz. We constructed a 149 m2 enclosure in an agricultural field to imitate an environment that wild geese may encounter. We created two enclosure settings allowing captive geese to forage on sprouted winter wheat (~ 2–15 cm) or on a randomly dispersed mixture of grain seeds (corn, wheat, sorghum) to account for both ‘grazing’ of vascular vegetation and ‘gleaning’ of agricultural grains to imitate foraging in wild geese. We used Sony Handycam DCR-SR45 video cameras, matched internal camera clocks with a running Universal Coordinated Time clock, and verbally re-calibrated the current time every 2 min during video footage collection. We filmed 119.5 h of video footage, and matched behavior with recorded ACC measurements by pairing video and device timestamps for each device using JWatcher41 and Program R.We characterized goose behaviors into four categories: ‘stationary’, ‘walk’, and ‘foraging’ from ground-truthed video footage, and ‘flight’ from visual inspection of the ACC data and consecutive GPS tracks during migration where device-measured speed remained  > 4.63 km/h (based on a natural break in the speed density distribution of all GPS locations). Each ACC burst was classified as only one behavior (i.e., a goose that was walking as it foraged was classified as ‘foraging’). We combined wild goose behaviors and captive goose behaviors after identifying minimal differences in ACC burst summary statistics29 for ‘stationary’ and ‘walk’ behaviors. We used ‘graze’ behaviors only from wild geese because of low sample size for captive geese and slight differences in ACC summary statistics between captive and wild geese for this behavior. ‘Glean’ foraging behavior was only classified from captive geese. We then combined ‘graze’ and ‘glean’ behaviors into an overall ‘foraging’ behavior to account for variation in foraging behavior of wild geese, and because machine learning models could not accurately distinguish between the two foraging modes40. We randomly subsampled all behaviors to 150 bursts if our dataset contained at least that many bursts to reduce the risk of artificially increasing prediction accuracy20. We determined there were insufficient differences in ACC signatures between CTT BT3.0 and BT3.5 versions by visual comparison of signatures and summary statistics, and merged all bursts into an overall CTT-specific training data set, and retained CTT- and Ornitela-specific training data sets to account for brand-specific ACC measurement schemes. The final training data sets consisted of 150 stationary, 150 walking, 118 foraging, and 150 flying bursts (CTT), and 150 stationary, 75 walking, 120 foraging, and 150 flying bursts (Ornitela).We used the training data sets to predict behaviors of tagged, wild white-fronted geese during winter with temporally aligned GPS and ACC data. We used a suite of supervised machine-learning algorithms and selected the algorithm with greatest prediction accuracy based on an 80% training, 20% testing sample approach. We tested random forest, support vector machines, K-nearest neighbors, classification and regression trees, and linear discriminant analysis, all with cross validation in Program R18,29,42. We evaluated models using three metrics defined in Ref.42: (1) overall classification accuracy as the percent of classifications in the test data set that were predicted correctly, (2) precision of assignment, the probability that an assigned behavior in the test data set was correct, and (3) model recall, the probability that a sample with a specific behavior in the test data set was correctly classified as that behavior by the model. Random forests provided the highest overall classification accuracy (95.6% for CTT and 96.0% for Ornitela), as well as high precision and recall for each behavior (CTT range 93.1–99.3, Ornitela range 88.9–100.0%), and therefore we labeled behaviors from wild goose ACC data using the random forests.Habitat transition modelOur habitat-transition model required temporally matched GPS and ACC datasets. Therefore, we subset all GPS locations to match the time-series of ACC data per individual because devices typically acquired GPS data longer than ACC data before device failure or individual mortality. For each GPS location, we extracted the landcover type and wintering region from spatial layers and retained temperature recorded from the device. To link classified ACC behaviors to GPS locations, we matched ACC timestamps between two GPS locations with the previous GPS timestamp. That is, all ACC bursts between two GPS locations were assigned backward to the previous GPS location. In this way, an individual’s first location is collected in GPS landcover type A, ACC data are collected in 5 bursts, their behaviors are classified and assigned to the first GPS location A and associated landcover type, followed by collection of GPS location B, in which the subsequent 5 ACC bursts are associated to GPS location/landcover type B. In the case of missing GPS locations, we matched ACC bursts to the previous GPS location only if the ACC timestamps were within 60 min of the GPS timestamp, and ACC bursts occurring greater than 60 min after GPS acquisition were removed until the next GPS fix. To account for temporal variation in habitat-behavior relationships, we calculated two continuous covariates representing time-of-day based on the local time associated with the timestamp of each GPS location for each individual. The variable cos(Diel) represented diurnal (negative values) and nocturnal (positive values) periods, and sin(Time) represented midnight until 11:59 a.m. (positive values) and noon until the following 11:59 p.m. (negative values), where high and low values ranged continuously between 1 and − 143. Our temporally matched time series of GPS and ACC data yielded 53,502 GPS locations linked with 300,348 ACC bursts across both winters.We used a Bayesian Markov model with Pólya-Gamma sampling following43), [cf. Refs.44,45] to determine how transitions between landcover types were influenced by behavior, temperature, time-of-day, and wintering region. The proportion of time spent foraging, walking, and stationary between each successive GPS fix was included as a covariate; flight was not included to reduce multicollinearity due to behavior proportions summing to one. Markov models account for non-independence among observations by assuming that the current state (i.e., landcover type) is dependent upon the previous state, and allow the determination of covariate influences on the probability of transitioning among states through a logistic link function. The transition probability from habitat i to habitat j at time t for individual n is modeled with multinomial logistic regression:$$begin{aligned} & logitleft( {p_{nijt} } right) = logleft( {frac{{p_{nijt} }}{{p_{niJt} }}} right) = mathop sum limits_{{r in {mathcal{R}}_{j} }} beta_{0jr} Ileft( {Region_{nt} = r} right) + beta_{1j} {text{cos}}left( {Diel_{nt} } right) \ & quad + beta_{2j} {text{sin}}left( {Time_{nt} } right) + beta_{3ij} Forage_{nt} + beta_{4ij} Walk_{nt} + beta_{5ij} Stationary_{nt} + beta_{6ij} Temperature_{nt} , \ end{aligned}$$where ({mathcal{R}}_{j}) is the set of wintering regions (r) where habitat (j) occurs, (Regio{n}_{rnt}) indicated wintering region (r), and (mathrm{cos}left({Diel}_{nt}right)) and (mathrm{sin}({Time}_{nt})) were temporal covariates (described above) for habitat j. Quantities ({Forage}_{nt}, {Walk}_{nt},mathrm{ and }{Stationary}_{nt}) were the scaled (mean = 0, standard deviation = 1) proportion of time spent in each behavior between transitions from habitat i to habitat j, and ({Temperature}_{nt}) was scaled ambient temperature (°C) for transitions from habitat i to habitat j. All coefficients for transitions to the baseline habitat (J) were set to 0 (i.e., ({beta }_{0Jr}) for all (r), ({beta }_{1J}), ({beta }_{2J}), ({beta }_{3iJ}), ({beta }_{4iJ}), ({beta }_{5iJ}),({beta }_{6iJ}), for all (i)). We set the baseline habitat (J) as open water/unconsolidated shore because this habitat is used primarily for both nocturnal roosting and diurnal loafing, included all behaviors, and transitions to all other landcover types were frequent in each region.The prior for the set of winter region intercepts for each habitat was:$${beta }_{0jr}sim N({beta }_{0j},{sigma }_{0jr}^{2}),$$for (rin {mathcal{R}}_{j}), ({beta }_{0j}) was the mean intercept, and ({sigma }_{0jr}^{2}) was set to 100. For ({beta }_{0j}), a vague prior mean 0 and σ2 = 100 was used with an assumed normal distribution.The Markov model was executed within a Bayesian framework to robustly quantify uncertainty. The Markov model assumed that data were collected at regular time intervals for both GPS (30 min) and ACC (6 min), however imperfect collection by devices created irregular data sets. Therefore, we subsampled GPS locations and constrained time series data to sequences where GPS locations missing  > 120 min intervals (i.e., 4 locations) were separated into sequences of regular time series data for each individual46. We extended43 by including a mix of both transition-specific effects (i.e., behaviors, temperature) and habitat-specific effects (i.e., wintering region, cos(Diel), and sin(Time)), where transition-specific effects were allowed to vary for a current habitat state, while habitat-specific effects were not. We included a mix of coefficients because initial model runs indicated that some effects were similar regardless of the current habitat (i.e., were habitat- and not transition-specific decisions). We also incorporated a model feature to exclude estimation of transitions that did not occur either within the dataset as a whole or within each specific wintering region because landcover types varied among them by setting those specific transition probabilities to zero. We centered and standardized all behavior and temperature covariates, sampled 50,000 iterations from the model posterior using one chain, and discarded the first 10,000 iterations as burn-in. We assessed model convergence by evaluating trace plots and setting random initial values, examined autocorrelation plots, and Geweke diagnostics using the R package ‘coda’47,48,49. More

Plant growth model without environmental forcingThe model without environmental forcing closely follows the original description of the Thornley transport resistance (TTR) model29. A summary of the model parameters is provided in Supplementary Table 2. The shoot and root mass pools (MS and MR, in kg structural dry matter) change as a function of growth and loss (equations (1) and (2)). The litter (kL) and maintenance respiration (r) loss rates (in kg kg−1 d−1) are treated as constants. In the original model description29 r = 0. The parameter KM (units kg) describes how loss varies with mass (MS or MR). Growth (Gs and Gr, in kg d−1) varies as a function of the carbon and nitrogen concentrations (equations (3) and (4)). CS, CR, NS and NR are the amounts (kg) of carbon and nitrogen in the roots and shoots. These assumptions yield the following equations for shoot and root dry matter,$${mathrm{MS}}[t+1]={mathrm{MS}}[t]+{G}_{{mathrm{S}}}[t]-frac{({k}_{{mathrm{L}}}+r){mathrm{MS}}[t]}{1+frac{{K}_{{{M}}}}{{mathrm{MS}}[t]}},$$
(1)
$${mathrm{MR}}[t+1]={mathrm{MR}}[t]+{G}_{{mathrm{R}}}[t]-frac{({k}_{{mathrm{L}}}+r){mathrm{MR}}[t]}{1+frac{{K}_{{{M}}}}{{mathrm{MR}}[t]}},$$
(2)
where GS and GR are$${G}_{{mathrm{S}}}=gfrac{{mathrm{CS}}times {mathrm{NS}}}{{mathrm{MS}}},$$
(3)
$${G}_{{mathrm{R}}}=gfrac{{mathrm{CR}}times {mathrm{NR}}}{{mathrm{MR}}},$$
(4)
and g is the growth coefficient (in kg kg−1 d−1).Carbon uptake UC is determined by the net photosynthetic rate (a, in kg kg−1 d−1) and the shoot mass (equation (5)). Similarly, nitrogen uptake (UN) is determined by the nitrogen uptake rate (b, in kg kg−1 d−1) and the root mass. The parameter KA (units kg) forces both photosynthesis and nitrogen uptake to be asymptotic with mass. The second terms in the denominators of equations (5) and (6) model product inhibitions of carbon and nitrogen uptake, respectively; that is, the parameters JC and JN (in kg kg−1) mimic the inhibition of source activity when substrate concentrations are high,$${U}_{{mathrm{C}}}=frac{a{mathrm{MS}}}{left(1+frac{{mathrm{MS}}}{{K}_{{mathrm{A}}}}right)left(1+frac{{mathrm{CS}}}{{mathrm{MS}}times {J}_{{mathrm{C}}}}right)},$$
(5)
$${U}_{{mathrm{N}}}=frac{b{mathrm{MR}}}{left(1+frac{{mathrm{MR}}}{{K}_{{mathrm{A}}}}right)left(1+frac{{mathrm{NR}}}{{mathrm{MR}}times {J}_{{mathrm{N}}}}right)}.$$
(6)
The substrate transport fluxes of C and N (τC and τN, in kg d−1) between roots and shoots are determined by the concentration gradients between root and shoot and by the resistances. In the original model description29, these resistances are defined flexibly, but we simplify and assume that they scale linearly with plant mass,$${tau }_{{mathrm{C}}}=frac{{mathrm{MS}}times {mathrm{MR}}}{{mathrm{MS}}+{mathrm{MR}}}left(frac{{mathrm{CS}}}{{mathrm{MS}}}-frac{{mathrm{CR}}}{{mathrm{MR}}}right)$$
(7)
$${tau }_{{mathrm{N}}}=frac{{mathrm{MS}}times {mathrm{MR}}}{{mathrm{MS}}+{mathrm{MR}}}left(frac{{mathrm{NR}}}{{mathrm{MR}}}-frac{{mathrm{NS}}}{{mathrm{MS}}}right)$$
(8)
The changes in mass of carbon and nitrogen in the roots and shoots are then$${mathrm{CS}}[t+1]={mathrm{CS}}[t]+{U}_{{mathrm{C}}}[t]-{f}_{{mathrm{C}}}{G}_{{mathrm{s}}}[t]-{tau }_{{mathrm{C}}}[t]$$
(9)
$${mathrm{CR}}[t+1]={mathrm{CR}}[t]+{tau }_{{mathrm{C}}}[t]-{f}_{{mathrm{C}}}{G}_{{mathrm{r}}}[t]$$
(10)
$${mathrm{NS}}[t+1]={mathrm{NS}}[t]+{tau }_{{mathrm{N}}}[t]-{f}_{{mathrm{N}}}{G}_{{mathrm{s}}}[t]$$
(11)
$${mathrm{NR}}[t+1]={mathrm{NR}}[t]+{U}_{{mathrm{N}}}[t]-{f}_{{mathrm{N}}}{G}_{{mathrm{r}}}[t]-{tau }_{{mathrm{N}}}[t]$$
(12)
where fC and fN (in kg kg−1) are the fractions of structural carbon and nitrogen in dry matter.Adding environmental forcing to the plant growth modelIn this section, we describe how the net photosynthetic rate (a), the nitrogen uptake rate (b), the growth rate (g) and the respiration rate (r) are influenced by environmental-forcing factors. These environmental-forcing effects are described in equations (13)–(17) and summarized graphically in Extended Data Fig. 1. All other model parameters are treated as constants. Previous work that implemented the TTR model as a species distribution model30 is used as a starting point for adding environmental forcing. As in this previous work30, we assume that parameters a, b and g are co-limited by environmental factors in a manner analogous to Liebig’s law of the minimum, which is a crude but pragmatic abstraction. The implementation here differs in some details.Unlike previous work30, we use the Farquhar model of photosynthesis47,48 to represent how solar radiation, atmospheric CO2 concentration and air temperature co-limit photosynthesis35. We assume that the Farquhar model parameters are universal and that all vegetation in our study uses the C3 photosynthetic pathway. The Farquhar model photosynthetic rates are rescaled to [0,amax] to yield afqr. The effects of soil moisture (Msoil) on photosynthesis are represented as an increasing step function ({{{{S}}}}(M_{mathrm{soil}},{beta }_{1},{beta }_{2})=max left{min left(frac{M_{mathrm{soil}}-{beta }_{1}}{{beta }_{2}-{beta }_{1}},1right),0right}). This allows us to redefine a as,$$a={a}_{{mathrm{fqr}}} {{{{S}}}}(M_{mathrm{soil}},{beta }_{1},{beta }_{2})$$
(13)
The processes influencing nitrogen availability are complex, and global data products on plant available nitrogen are uncertain. We therefore assume that nitrogen uptake will vary with soil temperature and soil moisture. That is, the nitrogen uptake rate b is assumed to have a maximum rate (bmax) that is co-limited by soil temperature Tsoil and soil moisture Msoil,$$b={b}_{{mathrm{max}}} {{{{S}}}}({T}_{soil},{beta }_{3},{beta }_{4}) {{{{Z}}}}(M_{mathrm{soil}},{beta }_{5},{beta }_{6},{beta }_{7},{beta }_{8}).$$
(14)
In equation (14), we have assumed that the nitrogen uptake rate is a simple increasing and saturating function of temperature. We have also assumed that the nitrogen uptake rate is a trapezoidal function of soil moisture with low uptake rates in dry soils, higher uptake rates at intermediate moisture levels and lower rates once soils are so moist as to be waterlogged. The trapezoidal function is ({{{{Z}}}}(M_{mathrm{soil}},{beta }_{5},{beta }_{6},{beta }_{7},{beta }_{8})=max left{min left(frac{M_{mathrm{soil}}-{{{{{beta }}}}}_{5}}{{{{{{beta }}}}}_{6}-{{{{{beta }}}}}_{5}},1,frac{{{{{{beta }}}}}_{8}-M_{mathrm{soil}}}{{beta }_{8}-{beta }_{7}}right),0right}).The previous sections describe how the assimilation of carbon and nitrogen by a plant are influenced by environmental factors. The TTR model describes how these assimilate concentrations influence growth (equations (3) and (4)). In our implementation, we additionally allow the growth rate to be co-limited by temperature (soil temperature, Tsoil) and soil moisture (Msoil),$$g={g}_{{mathrm{max}}} {{{{Z}}}}({T}_{{mathrm{soil}}},{beta }_{9},{beta }_{10},{beta }_{11},{beta }_{12}) {{{{S}}}}(M_{mathrm{soil}},{beta }_{13},{beta }_{14}).$$
(15)
We use Tsoil since we assume that growth is more closely linked to soil temperature, which varies slower than air temperature. The respiration rate (r, equations (1) and (2)) increases as a function of air temperature (Tair) to a maximum rmax,$$r={r}_{{mathrm{max}}}{{{{S}}}}({T}_{{mathrm{air}}},{beta }_{15},{beta }_{16}).$$
(16)
The parameter r is best interpreted as a maintenance respiration. Growth respiration is not explicitly considered; it is implicitly incorporated in the growth rate parameter (g, equation (15)), and any temperature dependence in growth respiration is therefore assumed to be accommodated by equation (15).Fire can reduce the structural shoot mass MS as follows,$${mathrm{MS}}[t+1]={mathrm{MS}}[t](1-{{{{S}}}}(F,{beta }_{17},{beta }_{18})).$$
(17)
where F is an indicator of fire severity at a point in time (for example, burnt area) and the function S(F, β17, β18) allows MS to decrease when the fire severity indicator F is high. If F = 0, this process plays no role. This fire impact equation was used in preliminary analyses, but the data on fire activity did not provide sufficient information to estimate β17 and β18; we therefore excluded this process from the final analyses.We further estimate two additional β parameters (βa and βb) so that each site can have unique maximum carbon and nitrogen uptake rates. Specifically, we redefine a as ({a}^{{prime} }={beta }_{a} a) and b as ({b}^{{prime} }={beta }_{b} b).Data sources and preparationTo describe vegetation activity, we use the GIMMS 3g v.1 NDVI data26,27 and the MODIS EVI28 data. The GIMMS data product has been derived from the AVHRR satellite programme and controls for atmospheric contamination, calibration loss, orbital drift and volcanic eruptions26,27. The data provide 24 NDVI raster grids for each year, starting in July 1981 and ending in December 2015. The spatial resolution is 1/12° (~9 × 9 km). The EVI data used are from the MODIS programme’s Terra satellite; it is a 1 km data product provided at a 16-day interval. We use data from the start of the record (February 2000) to December 2019. The MODIS data product (MOD13A2) uses a temporal compositing algorithm to produce an estimate every 16 days that filters out atmospheric contamination. The EVI is designed to reduce the effects of atmospheric, bare-ground and surface water on the vegetation index28.For environmental forcing, we use the ERA5-Land data31,32 (European Centre for Medium-Range Weather Forecasts Reanalysis v. 5; hereafter, ERA5). The ERA5 products are global reanalysis products based on hourly estimates of atmospheric variables and extend from present back to 1979. The data products are supplied at a variety of spatial and temporal resolutions. We used the monthly averages from 1981 to 2019 at a 0.1° spatial resolution (~11 km). The ERA5 data provide air temperature (2 m surface air temperature), soil temperature (0–7 cm soil depth), surface solar radiation and volumetric soil water (0–7 cm soil depth). Fire data were taken from the European Space Agency Fire Disturbance Climate Change Initiative’s AVHRR Long-Term Data Record Grid v.1.0 product49. This product provides gridded (0.25° resolution) data of monthly global (from 1982 to 2017) burned area derived from the AVHRR satellite programme. As mentioned, the fire data did not enrich our analysis, and the analyses we present here therefore exclude further consideration of the fire data.All data were resampled to the GIMMS grid. The mean pixel EVI was then calculated for each GIMMS cell for each time point in the MODIS EVI data. We used linear interpolation on the NDVI, EVI and ERA5 environmental-forcing data to estimate each variable on a weekly time step. This served to set the time step of the TTR difference equations to one week and to synchronize the different time series.Site selectionThe GIMMS grid cells define the spatial resolution of our sample points. GIMMS grid cells are large (1/12°, ~9 km), meaning that most grid cells contain multiple land-cover types. We focused on wilderness landscapes, and our aim was to find multiple grid cells for the major ecosystems of the world. We used the following classification of ecosystem types to guide the stratification of our grid-cell selection: tropical evergreen forest (RF), boreal forest (BF), temperate evergreen and temperate deciduous forest (TF), savannah (SA), shrubland (SH), grassland (GR), tundra (TU) and Mediterranean-type ecosystems (MT).We used the following criteria to select grid cells. (1) Selected grid cells should contain relatively homogeneous vegetation. Small-scale heterogeneity was allowed (for example, catenas, drainage lines, peatlands) as long as many of these elements are repeated in the scene (for example, rolling hills were accepted, but elevation gradients were rejected). (2) Sites should have no signs of transformative human activity (for example, tree harvesting, crop cultivation, paved surfaces). We used the Time Tool in Google Earth Pro, which provides annual satellite imagery of the Earth from 1984 onwards, to ensure that no such activity occurred during the observation period (note that the GIMMS record starts in July 1981; however, it is likely that evidence of transformative activity between July 1981 and 1984 would be visible in 1984). Grid cells with extensive livestock holding on non-improved pasture were included. In some cases, a small agricultural field or pasture was present, and such grid cells were used as long as the field or pasture was small and remained constant in size. (3) Grid cells should not include large water bodies, but small drainage lines or ponds were accepted as long as they did not violate the first criterion. (4) Grid cells should be independent (neighbouring grid cells were not selected) and cover the major ecosystems of the world. Using these criteria, we were able to include 100 sites in the study (Extended Data Figs. 2 and 3 and Supplementary Table 4).State-space modelWe used a Bayesian state-space approach. Conceptually, the analysis takes the form,$$M[t]=f(M[t-1],{boldsymbol{beta}},{boldsymbol{theta}}_{t-1},{epsilon }_{t-1})$$
(18)
$${mathrm{VI}}[t]=m M[t]+eta .$$
(19)
Here M[t] is the plant growth model’s prediction of biomass (M = MS + MR) at time t, and ϵt−1 is the process error associated with the state variables. In the model, each underlying state variable (MS, MR, CS, CR, NS and NR) has an associated process error term. The function f(M[t − 1], β, θt−1, ϵt−1) summarizes that the development of M is influenced by the state variables MS, MR, CS, CR, NS and NR, the environmental-forcing data θt−1 and the β parameters. The observation equation (equation (19)) uses the parameter m to link the VI (vegetation index, either NDVI or EVI) observations to the modelled state M. The parameter η is the observation error. Equation (19) assumes that there is a linear relationship between modelled biomass (M) and VI, which is a simplification of reality50,51,52. The observation error η is structured by our simplification of the data products quality scores (coded Q = 0, 1, 2, with 0 being good and 2 being poor; Supplementary Table 3) to allow the error to increase with each level of the quality score. Specifically, we define η = e0 + e1 × Q.The model was formulated using the R package LaplacesDemon53. All β parameters are given vague uniform priors. The parameter m is given a vague normal prior (truncated to be >0). The process error terms are modelled using normal distributions, and the variances of the error terms are given vague half-Cauchy priors. The ex parameters are given vague normal priors. The model also requires the parameterization of M[0], the initial vegetation biomass; M[0] is given a vague uniform prior. We used the twalk Markov chain Monte Carlo (MCMC) algorithm as implemented in LaplacesDemon53 and its default control parameters to estimate the posterior distributions of the model parameters. We further fitted the model using DEoptim54,55, which is a robust genetic algorithm that is known to perform stably on high-dimensional and multi-modal problems56, to verify that the MCMC algorithm had not missed important regions of the parameter space. The models estimated with MCMC had significantly lower log root-mean-square error than models estimated with DEoptim (paired t-test NDVI analysis: t = –2.9806, degrees of freedom (d.f.) = 99, P = 0.00362; EVI analysis: t = –4.6229, d.f. = 99, P = 1.144 × 10–5), suggesting that the MCMC algorithm performed well compared with the genetic algorithm.Anomaly extraction and trend estimationWe use the ‘seasonal and trend decomposition using Loess’ (STL57) as implemented in the R58 base function stl. STL extracts the seasonal component s of a time series using Loess smoothing. What remains after seasonal extraction (the anomaly or remainder, r) is the sum of any long-term trend and stochastic variation. We estimate the trend in two ways. First, we estimate the trend by fitting a quadratic polynomial (r = a + bx + cx2) to the remainder (r is the remainder, x is time and a, b and c are regression coefficients). The use of polynomials allows the data to specify whether a trend exists, whether the trend is linear, cup or hat shaped and whether the overall trend is increasing or decreasing. As a second method, we estimate the trend by fitting a bent-cable regression to the remainder. Bent-cable regression is a type of piecewise linear regression for estimating the point of transition between two linear phases in a time series59,60. The model takes the form r = b0 + b1x + b2 q(x, τ, γ)60. Here r is the remainder, x is time, b0 is the initial intercept, b1 is the slope in phase 1, the slope in phase 2 is b2 − b1 and q is a function that defines the change point: (q(x,tau ,gamma )=frac{{(x-tau +gamma )}^{2}}{4gamma }I(tau -gamma < tau +gamma )+(x-tau )I(x > tau +gamma )); τ represents the location of the change point and γ the span of the bent cable that joins the two linear phases; I(A) is an indicator function that returns 1 if A is true and 0 if A is false. The bent-cable model allows the data to specify whether a trend exists and whether there has been a switch in the trend, thereby allowing the identification of whether the trend is linear, cup or hat shaped and whether the overall trend is increasing or decreasing. Both the polynomial and bent-cable models were estimated using LaplacesDemon’s53 Adaptive Metropolis MCMC algorithm and vague priors, although for the bent-cable model we constrained τ to be in the middle 70% of the time series and γ to be at most two years.The STL extraction of the seasonal components in the air temperature, soil temperature, soil moisture and solar radiation data (there is no stochasticity or seasonal trend in the CO2 data we used) allows us to simulate detrended time series d of these forcing variables as (d=bar{y}+s+{{{{N}}}}(mu ,sigma )) where N(μ, σ) is a normally distributed random variable with mean and standard deviation estimated from the remainder r (we verified that r was well described by the normal distribution), (bar{y}) is the mean of the data over the time series and s is the seasonal component extracted by STL. For CO2, the detrended time series is simply the average CO2 over the time series. More

In 2008, I had just begun volunteering at Equilibrio Azul — a non-profit marine-research and -conservation organization based in Quito — when colleagues discovered a hawksbill sea turtle (Eretmochelys imbricata) nesting at La Playita beach in Ecuador. The eastern Pacific population of hawksbill sea turtles is one of the most endangered in the world and was considered functionally extinct in the region before this turtle and others were observed.That discovery was a tipping point for hawksbill research in Ecuador and throughout the Pacific Ocean. Since 2008, we’ve found about 20 nests each year at La Playita, and one season, we documented 50.We have tagged 11 adult females with satellite transmitters. Previously, most of our understanding of these turtles had been based on observations in the Caribbean, where the reptiles are strictly coral-reef dwellers. But Ecuador’s reefs are mostly rocky, with patches of coral, and we were surprised to see females migrate south to mangroves, mainly for food.
Women in science
In this image, we have just attached a transmitter to a baby turtle — a first for hawksbill turtles this young and in the eastern Pacific region. We did not know much about hawksbills at this young age. It is tricky working with baby turtles, because they grow very fast, and the transmitters, which give us location data, can easily fall off. We’ve used cement to glue the devices to the shells of six newborns so far. The longest the transmitters have lasted is three months and the shortest period was only six weeks — but the devices provided our first insights into the ‘lost years’ of sea-turtle biology.Our findings have overturned assumptions that neonates were just carried along by currents. Instead, we found that one-day-old turtles can swim against the current. They aim for a specific direction — north by northwest — as they learn to dive and swim. We tracked one-year-old hawksbills to Costa Rican waters, a journey of roughly 2,000 kilometres, before we lost their signal.Cristina Miranda is a scientific coordinator at Equilibrio Azul in Quito, Ecuador. Interview by Virginia Gewin. More

Anae, J. et al. Recent advances in biochar engineering for soil contaminated with complex chemical mixtures: Remediation strategies and future perspectives. Sci. Total Environ. 767, 144351. https://doi.org/10.1016/j.scitotenv.2020.144351 (2021).Article 
ADS 
CAS 

Google Scholar 
Kiran, B. R. & Prasad, M. N. V. Biochar and rice husk ash assisted phytoremediation potentials of Ricinus communis L. for lead-spiked soils. Ecotoxicol Environ Saf 183, 109574. https://doi.org/10.1016/j.ecoenv.2019.109574 (2019).Article 
CAS 

Google Scholar 
Bolan, N. et al. Remediation of heavy metal(loid)s contaminated soils – To mobilize or to immobilize?. J. Hazard. Mater. 266, 141–166. https://doi.org/10.1016/j.jhazmat.2013.12.018 (2014).Article 
CAS 

Google Scholar 
Burachevskaya, M. et al. The effect of granular activated carbon and biochar on the availability of Cu and Zn to Hordeum sativum distichum in contaminated soil. Plants https://doi.org/10.3390/plants10050841 (2021).Article 

Google Scholar 
Cao, P. et al. Mercapto propyltrimethoxysilane- and ferrous sulfate-modified nano-silica for immobilization of lead and cadmium as well as arsenic in heavy metal-contaminated soil. Environ. Pollut. 266, 115152. https://doi.org/10.1016/j.envpol.2020.115152 (2020).Article 
CAS 

Google Scholar 
Ok, Y. S. et al. Ameliorants to immobilize Cd in rice paddy soils contaminated by abandoned metal mines in Korea. Environ. Geochem. Health 33(Suppl 1), 23–30. https://doi.org/10.1007/s10653-010-9364-0 (2011).Article 
CAS 

Google Scholar 
Qin, Y. et al. Dual-wastes derived biochar with tailored surface features for highly efficient p-nitrophenol adsorption. J. Clean. Prod. 353, 131571. https://doi.org/10.1016/j.jclepro.2022.131571 (2022).Article 
CAS 

Google Scholar 
Rajput, V. D. et al. Nano-biochar: A novel solution for sustainable agriculture and environmental remediation. Environ. Res. 210, 112891. https://doi.org/10.1016/j.envres.2022.112891 (2022).Article 
CAS 

Google Scholar 
Ding, Y. et al. Biochar to improve soil fertility. A review. Agron. Sustain. Dev. 36, 36. https://doi.org/10.1007/s13593-016-0372-z (2016).Article 
CAS 

Google Scholar 
Oni, B. A., Oziegbe, O. & Olawole, O. O. Significance of biochar application to the environment and economy. Ann. Agric. Sci. 64, 222–236. https://doi.org/10.1016/j.aoas.2019.12.006 (2019).Article 

Google Scholar 
He, E. et al. Two years of aging influences the distribution and lability of metal(loid)s in a contaminated soil amended with different biochars. Sci. Total Environ. 673, 245–253. https://doi.org/10.1016/j.scitotenv.2019.04.037 (2019).Article 
ADS 
CAS 

Google Scholar 
Netherway, P. et al. Phosphorus-rich biochars can transform lead in an urban contaminated soil. J. Environ. Qual. 48, 1091–1099. https://doi.org/10.2134/jeq2018.09.0324 (2019).Article 
CAS 

Google Scholar 
O’Connor, D. et al. Biochar application for the remediation of heavy metal polluted land: A review of in situ field trials. Sci. Total Environ. 619–620, 815–826. https://doi.org/10.1016/j.scitotenv.2017.11.132 (2018).Article 
ADS 
CAS 

Google Scholar 
Xu, X. et al. Effect of physicochemical properties of biochar from different feedstock on remediation of heavy metal contaminated soil in mining area. Surf. Interfaces 32, 102058. https://doi.org/10.1016/j.surfin.2022.102058 (2022).Article 
CAS 

Google Scholar 
Melo, L. C. A. et al. Sorption and desorption of cadmium and zinc in two tropical soils amended with sugarcane-straw-derived biochar. J. Soils Sediments 16, 226–234. https://doi.org/10.1007/s11368-015-1199-y (2016).Article 

Google Scholar 
Uchimiya, M., Chang, S. & Klasson, K. T. Screening biochars for heavy metal retention in soil: Role of oxygen functional groups. J. Hazard. Mater. 190, 432–441. https://doi.org/10.1016/j.jhazmat.2011.03.063 (2011).Article 
CAS 

Google Scholar 
Jatav, H. S. et al. Sustainable approach and safe use of biochar and its possible consequences. Sustainability https://doi.org/10.3390/su131810362 (2021).Article 

Google Scholar 
Varalta, F. & Sorvari, J. In Organic Waste Composting through Nexus Thinking: Practices, Policies, and Trends (eds Hettiarachchi, H. et al.) 213–232 (Springer International Publishing, 2020).Chapter 

Google Scholar 
Pinotti, L. et al. Recycling food leftovers in feed as opportunity to increase the sustainability of livestock production. J. Clean. Prod. 294, 126290. https://doi.org/10.1016/j.jclepro.2021.126290 (2021).Article 

Google Scholar 
Jafri, N., Wong, W. Y., Doshi, V., Yoon, L. W. & Cheah, K. H. A review on production and characterization of biochars for application in direct carbon fuel cells. Process Saf. Environ. Prot. 118, 152–166. https://doi.org/10.1016/j.psep.2018.06.036 (2018).Article 
CAS 

Google Scholar 
Jin, Y. et al. Characterization of biochars derived from various spent mushroom substrates and evaluation of their adsorption performance of Cu(II) ions from aqueous solution. Environ. Res. 196, 110323. https://doi.org/10.1016/j.envres.2020.110323 (2021).Article 
CAS 

Google Scholar 
Tomczyk, A., Sokołowska, Z. & Boguta, P. Biomass type effect on biochar surface characteristic and adsorption capacity relative to silver and copper. Fuel 278, 118168. https://doi.org/10.1016/j.fuel.2020.118168 (2020).Article 
CAS 

Google Scholar 
FAO. Food Outlook – Biannual Report on Global Food Markets: November 2020. Rome. Phytoremediation of copper-contaminated soil by Artemisia absinthium: comparative effect of chelating agents. Environmental Geochemistry and Health. (2020). https://doi.org/10.4060/cb1993enRussian-Statistical-Year-Book. Statistical handbook. P76 M., 2020 – 700 p. ISBN 978-5-89476-497-9 (2020).Cheng, C.-H., Lehmann, J., Thies, J. E. & Burton, S. D. Stability of black carbon in soils across a climatic gradient. J. Geophys. Res. Biogeosci. 113, 55. https://doi.org/10.1029/2007JG000642 (2008).Article 
CAS 

Google Scholar 
Singh, B. P., Cowie, A. L. & Smernik, R. J. Biochar carbon stability in a clayey soil as a function of feedstock and pyrolysis temperature. Environ. Sci. Technol. 46, 11770–11778. https://doi.org/10.1021/es302545b (2012).Article 
ADS 
CAS 

Google Scholar 
He, Y. et al. Effects of biochar application on soil greenhouse gas fluxes: A meta-analysis. GCB Bioenergy 9, 743–755. https://doi.org/10.1111/gcbb.12376 (2017).Article 
CAS 

Google Scholar 
Janu, R. et al. Biochar surface functional groups as affected by biomass feedstock, biochar composition and pyrolysis temperature. Carbon Resour. Convers. 4, 36–46. https://doi.org/10.1016/j.crcon.2021.01.003 (2021).Article 
CAS 

Google Scholar 
Tan, X. et al. Application of biochar for the removal of pollutants from aqueous solutions. Chemosphere 125, 70–85. https://doi.org/10.1016/j.chemosphere.2014.12.058 (2015).Article 
ADS 
CAS 

Google Scholar 
Ni, B.-J. et al. Competitive adsorption of heavy metals in aqueous solution onto biochar derived from anaerobically digested sludge. Chemosphere 219, 351–357. https://doi.org/10.1016/j.chemosphere.2018.12.053 (2019).Article 
ADS 
CAS 

Google Scholar 
Park, J.-H. et al. Competitive adsorption of heavy metals onto sesame straw biochar in aqueous solutions. Chemosphere 142, 77–83. https://doi.org/10.1016/j.chemosphere.2015.05.093 (2016).Article 
ADS 
CAS 

Google Scholar 
Methodological-Guidelines. Methodological guidelines for the determination of heavy metals in the soils of agricultural land and crop production – M., TSINAO, 61 (1992)Zhang, A., Li, X., Xing, J. & Xu, G. Adsorption of potentially toxic elements in water by modified biochar: A review. J. Environ. Chem. Eng. 8, 104196. https://doi.org/10.1016/j.jece.2020.104196 (2020).Article 
CAS 

Google Scholar 
Avramiotis, E., Frontistis, Z., Manariotis, I. D., Vakros, J. & Mantzavinos, D. On the performance of a sustainable rice husk biochar for the activation of persulfate and the degradation of antibiotics. Catalysts 11, 1303 (2021).Article 
CAS 

Google Scholar 
Maiti, S., Dey, S., Purakayastha, S. & Ghosh, B. Physical and thermochemical characterization of rice husk char as a potential biomass energy source. Biores. Technol. 97, 2065–2070. https://doi.org/10.1016/j.biortech.2005.10.005 (2006).Article 
CAS 

Google Scholar 
Herrera, K., Morales, L. F., Tarazona, N. A., Aguado, R. & Saldarriaga, J. F. Use of biochar from rice husk pyrolysis: Part A: Recovery as an adsorbent in the removal of emerging compounds. ACS Omega 7, 7625–7637. https://doi.org/10.1021/acsomega.1c06147 (2022).Article 
CAS 

Google Scholar 
Szewczuk-Karpisz, K., Tomczyk, A., Grygorczuk-Płaneta, K. & Naveed, S. Rhizobium leguminosarum bv. trifolii exopolysaccharide and sunflower husk biochar as factors affecting immobilization of both tetracycline and Cd2+ ions on soil solid phase. J. Soils Sediments 22, 2620–2639. https://doi.org/10.1007/s11368-022-03255-3 (2022).Article 
CAS 

Google Scholar 
Hubetska, T. S., Kobylinska, N. G. & García, J. R. Sunflower biomass power plant by-products: Properties and its potential for water purification of organic pollutants. J. Anal. Appl. Pyrolysis 157, 105237. https://doi.org/10.1016/j.jaap.2021.105237 (2021).Article 
CAS 

Google Scholar 
Braghiroli, F. L. et al. The influence of pilot-scale pyro-gasification and activation conditions on porosity development in activated biochars. Biomass Bioenerg. 118, 105–114. https://doi.org/10.1016/j.biombioe.2018.08.016 (2018).Article 
CAS 

Google Scholar 
Braghiroli, F. L. et al. The conversion of wood residues, using pilot-scale technologies, into porous activated biochars for supercapacitors. J. Porous Mater. 27, 537–548. https://doi.org/10.1007/s10934-019-00823-w (2020).Article 
CAS 

Google Scholar 
Boraah, N., Chakma, S. & Kaushal, P. Attributes of wood biochar as an efficient adsorbent for remediating heavy metals and emerging contaminants from water: A critical review and bibliometric analysis. J. Environ. Chem. Eng. 10, 107825. https://doi.org/10.1016/j.jece.2022.107825 (2022).Article 
CAS 

Google Scholar 
Phillips, C. L. et al. Towards predicting biochar impacts on plant-available soil nitrogen content. Biochar 4, 9. https://doi.org/10.1007/s42773-022-00137-2 (2022).Article 
CAS 

Google Scholar 
Sun, L. & Gong, K. Silicon-based materials from rice husks and their applications. Ind. Eng. Chem. Res. 40, 5861–5877. https://doi.org/10.1021/ie010284b (2001).Article 
CAS 

Google Scholar 
Islam, T. et al. Synthesis of rice husk-derived magnetic biochar through liquefaction to adsorb anionic and cationic dyes from aqueous solutions. Arab. J. Sci. Eng. 46, 233–246. https://doi.org/10.1007/s13369-020-04537-z (2021).Article 
CAS 

Google Scholar 
Mohan, D. et al. Biochar production and applications in soil fertility and carbon sequestration – a sustainable solution to crop-residue burning in India. RSC Adv. 8, 508–520. https://doi.org/10.1039/C7RA10353K (2018).Article 
ADS 
CAS 

Google Scholar 
Li, F. et al. Preparation and characterization of biochars from Eichornia crassipes for cadmium removal in aqueous solutions. PLoS ONE 11, e0148132. https://doi.org/10.1371/journal.pone.0148132 (2016).Article 
CAS 

Google Scholar 
Song, H. et al. Potential of novel biochars produced from invasive aquatic species outside food chain in removing ammonium nitrogen: Comparison with conventional biochars and clinoptilolite. Sustainability https://doi.org/10.3390/su11247136 (2019).Article 

Google Scholar 
Yang, G. et al. Effects of pyrolysis temperature on the physicochemical properties of biochar derived from vermicompost and its potential use as an environmental amendment. RSC Adv. 5, 40117–40125. https://doi.org/10.1039/C5RA02836A (2015).Article 
ADS 
CAS 

Google Scholar 
Enders, A., Hanley, K., Whitman, T., Joseph, S. & Lehmann, J. Characterization of biochars to evaluate recalcitrance and agronomic performance. Bioresour. Technol. 114, 644–653. https://doi.org/10.1016/j.biortech.2012.03.022 (2012).Article 
CAS 

Google Scholar 
Zhang, Y., Wang, J. & Feng, Y. The effects of biochar addition on soil physicochemical properties: A review. CATENA 202, 105284. https://doi.org/10.1016/j.catena.2021.105284 (2021).Article 
CAS 

Google Scholar 
Özçimen, D. & Ersoy-Meriçboyu, A. Characterization of biochar and bio-oil samples obtained from carbonization of various biomass materials. Renew. Energy 35, 1319–1324. https://doi.org/10.1016/j.renene.2009.11.042 (2010).Article 
CAS 

Google Scholar 
Lin, Q. et al. Effects of biochar-based materials on the bioavailability of soil organic pollutants and their biological impacts. Sci. Total Environ. 826, 153956. https://doi.org/10.1016/j.scitotenv.2022.153956 (2022).Article 
ADS 
CAS 

Google Scholar 
Yang, H. et al. Thermogravimetric analysis−fourier transform infrared analysis of palm oil waste pyrolysis. Energy Fuels 18, 1814–1821. https://doi.org/10.1021/ef030193m (2004).Article 
CAS 

Google Scholar 
Pasangulapati, V. et al. Effects of cellulose, hemicellulose and lignin on thermochemical conversion characteristics of the selected biomass. Biores. Technol. 114, 663–669. https://doi.org/10.1016/j.biortech.2012.03.036 (2012).Article 
CAS 

Google Scholar 
Kim, P. et al. Surface functionality and carbon structures in lignocellulosic-derived biochars produced by fast pyrolysis. Energy Fuels 25, 4693–4703. https://doi.org/10.1021/ef200915s (2011).Article 
CAS 

Google Scholar 
Keiluweit, M., Nico, P. S., Johnson, M. G. & Kleber, M. dynamic molecular structure of plant biomass-derived black carbon (biochar). Environ. Sci. Technol. 44, 1247–1253. https://doi.org/10.1021/es9031419 (2010).Article 
ADS 
CAS 

Google Scholar 
Wijeyawardana, P. et al. Removal of Cu, Pb and Zn from stormwater using an industrially manufactured sawdust and paddy husk derived biochar. Environ. Technol. Innov. 28, 102640. https://doi.org/10.1016/j.eti.2022.102640 (2022).Article 
CAS 

Google Scholar 
Kołodyńska, D., Krukowska, J. & Thomas, P. Comparison of sorption and desorption studies of heavy metal ions from biochar and commercial active carbon. Chem. Eng. J. 307, 353–363. https://doi.org/10.1016/j.cej.2016.08.088 (2017).Article 
CAS 

Google Scholar 
Uchimiya, M. et al. Immobilization of heavy metal ions (CuII, CdII, NiII, and PbII) by broiler litter-derived biochars in water and soil. J. Agric. Food Chem. 58, 5538–5544. https://doi.org/10.1021/jf9044217 (2010).Article 
CAS 

Google Scholar 
Misono, M., Ochiai, E. I., Saito, Y. & Yoneda, Y. A new dual parameter scale for the strength of lewis acids and bases with the evaluation of their softness. J. Inorg. Nucl. Chem. 29, 2685–2691. https://doi.org/10.1016/0022-1902(67)80006-X (1967).Article 
CAS 

Google Scholar 
McBride, M. B. Environmental Chemistry of Soils (Oxford University Press, 1994).
Google Scholar 
Basta, N. T. & Tabatabai, M. A. Effect of cropping systems on adsorption of metals by soils: III. Competitive adsorption1. Soil Sci. 153, 331–337 (1992).Article 
ADS 
CAS 

Google Scholar 
Sposito, G. The Chemistry of Soils (Oxford University Press, 2016).Bauer, T. V. et al. Application of XAFS and XRD methods for describing the copper and zinc adsorption characteristics in hydromorphic soils. Environ. Geochem. Health 44, 335–347. https://doi.org/10.1007/s10653-020-00773-2 (2022).Article 
CAS 

Google Scholar 
Abd-Elfattah, A. L. Y. & Wada, K. Adsorption of lead, copper, zinc, cobalt, and cadmium by soils that differ in cation-exchange materials. J. Soil Sci. 32, 271–283. https://doi.org/10.1111/j.1365-2389.1981.tb01706.x (1981).Article 
CAS 

Google Scholar 
Etesami, H., Fatemi, H. & Rizwan, M. Interactions of nanoparticles and salinity stress at physiological, biochemical and molecular levels in plants: A review. Ecotoxicol. Environ. Saf. 225, 112769. https://doi.org/10.1016/j.ecoenv.2021.112769 (2021).Article 
CAS 

Google Scholar 
Soria, R. I., Rolfe, S. A., Betancourth, M. P. & Thornton, S. F. The relationship between properties of plant-based biochars and sorption of Cd(II), Pb(II) and Zn(II) in soil model systems. Heliyon 6, e05388. https://doi.org/10.1016/j.heliyon.2020.e05388 (2020).Article 

Google Scholar 
Alfarra, A., Frackowiak, E. & Béguin, F. The HSAB concept as a means to interpret the adsorption of metal ions onto activated carbons. Appl. Surf. Sci. 228, 84–92. https://doi.org/10.1016/j.apsusc.2003.12.033 (2004).Article 
ADS 
CAS 

Google Scholar 
Hu, J., Zhou, X., Shi, Y., Wang, X. & Li, H. Enhancing biochar sorption properties through self-templating strategy and ultrasonic fore-modified pre-treatment: Characteristic, kinetic and mechanism studies. Sci. Total Environ. 769, 144574. https://doi.org/10.1016/j.scitotenv.2020.144574 (2021).Article 
ADS 
CAS 

Google Scholar 
Ward, J., Rasul, M. G. & Bhuiya, M. M. K. Energy recovery from biomass by fast pyrolysis. Proced. Eng. 90, 669–674. https://doi.org/10.1016/j.proeng.2014.11.791 (2014).Article 
CAS 

Google Scholar 
Al-Wabel, M. I., Al-Omran, A., El-Naggar, A. H., Nadeem, M. & Usman, A. R. A. Pyrolysis temperature induced changes in characteristics and chemical composition of biochar produced from conocarpus wastes. Biores. Technol. 131, 374–379. https://doi.org/10.1016/j.biortech.2012.12.165 (2013).Article 
CAS 

Google Scholar 
Calvelo Pereira, R. et al. Contribution to characterisation of biochar to estimate the labile fraction of carbon. Org. Geochem. 42, 1331–1342. https://doi.org/10.1016/j.orggeochem.2011.09.002 (2011).Article 
CAS 

Google Scholar 
Vorob’eva, L. A. Theory and Practice Chemical Analysis of Soils (GEOS Press, Moscow, 2006).
Google Scholar 
Pinskii, D. L. et al. Copper adsorption by chernozem soils and parent rocks in Southern Russia. Geochem. Int. 56, 266–275. https://doi.org/10.1134/S0016702918030072 (2018).Article 
CAS 

Google Scholar 
Wang, Q., Wang, B., Lee, X., Lehmann, J. & Gao, B. Sorption and desorption of Pb(II) to biochar as affected by oxidation and pH. Sci. Total Environ. 634, 188–194. https://doi.org/10.1016/j.scitotenv.2018.03.189 (2018).Article 
ADS 
CAS 

Google Scholar 
Pourret, O. & Houben, D. Characterization of metal binding sites onto biochar using rare earth elements as a fingerprint. Heliyon 4, e00543. https://doi.org/10.1016/j.heliyon.2018.e00543 (2018).Article 

Google Scholar 
Huang, L. et al. High-resolution insight into the competitive adsorption of heavy metals on natural sediment by site energy distribution. Chemosphere 197, 411–419. https://doi.org/10.1016/j.chemosphere.2018.01.056 (2018).Article 
ADS 
MathSciNet 
CAS 

Google Scholar 
Ming, H. et al. Competitive sorption of cadmium and zinc in contrasting soils. Geoderma 268, 60–68. https://doi.org/10.1016/j.geoderma.2016.01.021 (2016).Article 
ADS 
CAS 

Google Scholar 
Musso, T. B., Parolo, M. E., Pettinari, G. & Francisca, F. M. Cu(II) and Zn(II) adsorption capacity of three different clay liner materials. J. Environ. Manag. 146, 50–58. https://doi.org/10.1016/j.jenvman.2014.07.026 (2014).Article 
CAS 

Google Scholar 
Cui, H. et al. Immobilization of Cu and Cd in a contaminated soil: One- and four-year field effects. J. Soils Sediments 14, 1397–1406. https://doi.org/10.1007/s11368-014-0882-8 (2014).Article 
CAS 

Google Scholar 
Elbana, T. A. et al. Freundlich sorption parameters for cadmium, copper, nickel, lead, and zinc for different soils: Influence of kinetics. Geoderma 324, 80–88. https://doi.org/10.1016/j.geoderma.2018.03.019 (2018).Article 
ADS 
CAS 

Google Scholar  More

de Sousa, L. L., Silva, S. M. & Xavier, R. DNA metabarcoding in diet studies: Unveiling ecological aspects in aquatic and terrestrial ecosystems. Environ. DNA 1, 199–214. https://doi.org/10.1002/edn3.27 (2019).Article 

Google Scholar 
Pompanon, F. et al. Who is eating what: Diet assessment using next generation sequencing. Mol. Ecol. 21, 1931–1950. https://doi.org/10.1111/j.1365-294X.2011.05403.x (2012).Article 
CAS 

Google Scholar 
Liu, M. X., Clarke, L. J., Baker, S. C., Jordan, G. J. & Burridge, C. P. A practical guide to DNA metabarcoding for entomological ecologists. Ecol. Entomol. 45, 373–385. https://doi.org/10.1111/een.12831 (2020).Article 

Google Scholar 
Traugott, M., Thalinger, B., Wallinger, C. & Sint, D. Fish as predators and prey: DNA-based assessment of their role in food webs. J. Fish Biol. 98, 367–382. https://doi.org/10.1111/jfb.14400 (2021).Article 

Google Scholar 
Clare, E. L. Molecular detection of trophic interactions: Emerging trends, distinct advantages, significant considerations and conservation applications. Evol. Appl. 7, 1144–1157. https://doi.org/10.1111/eva.12225 (2014).Article 

Google Scholar 
Deagle, B. E. et al. Counting with DNA in metabarcoding studies: How should we convert sequence reads to dietary data?. Mol. Ecol. 28, 391–406. https://doi.org/10.1111/mec.14734 (2019).Article 

Google Scholar 
Ando, H. et al. Methodological trends and perspectives of animal dietary studies by noninvasive fecal DNA metabarcoding. Environ. DNA 2, 391–406. https://doi.org/10.1002/edn3.117 (2020).Article 

Google Scholar 
Masonick, P., Hernandez, M. & Weirauch, C. No guts, no glory: Gut content metabarcoding unveils the diet of a flower-associated coastal sage scrub predator. Ecosphere. https://doi.org/10.1002/ecs2.2712 (2019).Article 

Google Scholar 
Eitzinger, B. et al. Assessing changes in arthropod predator–prey interactions through DNA-based gut content analysis-variable environment, stable diet. Mol. Ecol. 28, 266–280. https://doi.org/10.1111/mec.14872 (2019).Article 
CAS 

Google Scholar 
Kim, T. N. et al. Using high-throughput amplicon sequencing to determine diet of generalist lady beetles in agricultural landscapes. Biol. Control. https://doi.org/10.1016/j.biocontrol.2022.104920 (2022).Article 

Google Scholar 
Wallinger, C. et al. The effect of plant identity and the level of plant decay on molecular gut content analysis in a herbivorous soil insect. Mol. Ecol. Resour. 13, 75–83. https://doi.org/10.1111/1755-0998.12032 (2013).Article 
CAS 

Google Scholar 
Seabra, S. G. et al. PCR-based detection of prey DNA in the gut contents of the tiger-fly, Coenosia attenuata (Diptera: Muscidae), a biological control agent in Mediterranean greenhouses. Eur. J. Entomol. 118, 335–343. https://doi.org/10.14411/eje.2021.035 (2021).Article 

Google Scholar 
Panni, S. & Pizzolotto, R. Fast molecular assay to detect the rate of decay of Bactrocera oleae (Diptera: Tephritidae) DNA in Pterostichus melas (Coleoptera: Carabidae) gut contents. Appl. Entomol. Zool. 53, 425–431. https://doi.org/10.1007/s13355-018-0564-x (2018).Article 
CAS 

Google Scholar 
Greenstone, M. H., Payton, M. E., Weber, D. C. & Simmons, A. M. The detectability half-life in arthropod predator–prey research: What it is, why we need it, how to measure it, and how to use it. Mol. Ecol. 23, 3799–3813. https://doi.org/10.1111/mec.12552 (2014).Article 

Google Scholar 
Fülöp, D., Szita, E., Gerstenbrand, R., Tholt, G. & Samu, F. Consuming alternative prey does not influence the DNA detectability half-life of pest prey in spider gut contents. PeerJ https://doi.org/10.7717/peerj.7680 (2019).Article 

Google Scholar 
Zhang, G. F., Lu, Z. C., Wan, F. H. & Lovei, G. L. Real-time PCR quantification of Bemisia tabaci (Homoptera: Aleyrodidae) B-biotype remains in predator guts. Mol. Ecol. Notes 7, 947–954. https://doi.org/10.1111/j.1471-8286.2007.01819.x (2007).Article 
CAS 

Google Scholar 
Weber, D. C. & Lundgren, J. G. Detection of predation using qPCR: Effect of prey quantity, elapsed time, chaser diet, and sample preservation on detectable quantity of prey DNA. J. Insect Sci. https://doi.org/10.1673/031.009.4101 (2009).Article 

Google Scholar 
Paula, D. P. et al. Detection and decay rates of prey and prey symbionts in the gut of a predator through metagenomics. Mol. Ecol. Resour. 15, 880–892. https://doi.org/10.1111/1755-0998.12364 (2015).Article 
CAS 

Google Scholar 
Hindson, B. J. et al. High-throughput droplet digital PCR system for absolute quantitation of DNA copy number. Anal. Chem. 83, 8604–8610. https://doi.org/10.1021/ac202028g (2011).Article 
CAS 

Google Scholar 
Wood, S. A. et al. A comparison of droplet digital polymerase chain reaction (PCR), quantitative PCR and metabarcoding for species-specific detection in environmental DNA. Mol. Ecol. Resour. 19, 1407–1419. https://doi.org/10.1111/1755-0998.13055 (2019).Article 
CAS 

Google Scholar 
Nathan, L. M., Simmons, M., Wegleitner, B. J., Jerde, C. L. & Mahon, A. R. Quantifying environmental DNA signals for aquatic invasive species across multiple detection platforms. Environ. Sci. Technol. 48, 12800–12806. https://doi.org/10.1021/es5034052 (2014).Article 
ADS 
CAS 

Google Scholar 
Kim, T. G., Jeong, S. Y. & Cho, K. S. Comparison of droplet digital PCR and quantitative real-time PCR for examining population dynamics of bacteria in soil. Appl. Microbiol. Biotechnol. 98, 6105–6113. https://doi.org/10.1007/s00253-014-5794-4 (2014).Article 
CAS 

Google Scholar 
Thalinger, B., Pütz, Y. & Traugott, M. Endpoint PCR coupled with capillary electrophoresis (celPCR) provides sensitive and quantitative measures of environmental DNA in singleplex and multiplex reactions. PLoS ONE https://doi.org/10.1371/journal.pone.0254356 (2021).Article 

Google Scholar 
Mata, V. A. et al. How much is enough? Effects of technical and biological replication on metabarcoding dietary analysis. Mol. Ecol. 28, 165–175. https://doi.org/10.1111/mec.14779 (2019).Article 
CAS 

Google Scholar 
Sint, D., Guenay, Y., Mayer, R., Traugott, M. & Wallinger, C. The effect of plant identity and mixed feeding on the detection of seed DNA in regurgitates of carabid beetles. Ecol. Evol. 8, 10834–10846. https://doi.org/10.1002/ece3.4536 (2018).Article 

Google Scholar 
Nielsen, J. M., Clare, E. L., Hayden, B., Brett, M. T. & Kratina, P. Diet tracing in ecology: Method comparison and selection. Methods Ecol. Evol. 9, 278–291. https://doi.org/10.1111/2041-210x.12869 (2018).Article 

Google Scholar 
Schrader, C., Schielke, A., Ellerbroek, L. & Johne, R. PCR inhibitors—Occurrence, properties and removal. J. Appl. Microbiol. 113, 1014–1026. https://doi.org/10.1111/j.1365-2672.2012.05384.x (2012).Article 
CAS 

Google Scholar 
Juen, A. & Traugott, M. Amplification facilitators and multiplex PCR: Tools to overcome PCR-inhibition in DNA-gut-content analysis of soil-living invertebrates. Soil Biol. Biochem. 38, 1872–1879. https://doi.org/10.1016/j.soilbio.2005.11.034 (2006).Article 
CAS 

Google Scholar 
Wallinger, C. et al. Evaluation of an automated protocol for efficient and reliable DNA extraction of dietary samples. Ecol. Evol. 7, 6382–6389. https://doi.org/10.1002/ece3.3197 (2017).Article 

Google Scholar 
Marotz, C. et al. DNA extraction for streamlined metagenomics of diverse environmental samples. Biotechniques 62, 290–293. https://doi.org/10.2144/000114559 (2017).Article 
CAS 

Google Scholar 
Dingle, T. C., Sedlak, R. H., Cook, L. & Jerome, K. R. Tolerance of droplet-digital PCR vs real-time quantitative PCR to inhibitory substances. Clin. Chem. 59, 1670–1672. https://doi.org/10.1373/clinchem.2013.211045 (2013).Article 
CAS 

Google Scholar 
Racki, N., Dreo, T., Gutierrez-Aguirre, I., Blejec, A. & Ravnikar, M. Reverse transcriptase droplet digital PCR shows high resilience to PCR inhibitors from plant, soil and water samples. Plant Methods https://doi.org/10.1186/s13007-014-0042-6 (2014).Article 

Google Scholar 
Juen, A. & Traugott, M. Detecting predation and scavenging by DNA gut-content analysis: A case study using a soil insect predator-prey system. Oecologia 142, 344–352. https://doi.org/10.1007/s00442-004-1736-7 (2005).Article 
ADS 

Google Scholar 
Lundgren, J. G. & Lehman, M. Bacterial gut symbionts contribute to seed digestion in an omnivorous beetle. PLoS ONE https://doi.org/10.1371/journal.pone.0010831 (2010).Article 

Google Scholar 
Waldner, T. & Traugott, M. DNA-based analysis of regurgitates: A noninvasive approach to examine the diet of invertebrate consumers. Mol. Ecol. Resour. 12, 669–675. https://doi.org/10.1111/j.1755-0998.2012.03135.x (2012).Article 

Google Scholar 
Kamenova, S. et al. Comparing three types of dietary samples for prey DNA decay in an insect generalist predator. Mol. Ecol. Resour. 18, 966–973. https://doi.org/10.1111/1755-0998.12775 (2018).Article 
CAS 

Google Scholar 
Cheeseman, M. T. & Pritchard, G. Spatial organization of digestive processes in an adult carabid beetle, Scaphinotus marginatus (Coleoptera: Carabidae). Can. J. Zool. 62, 1200–1203. https://doi.org/10.1139/z84-173 (1984).Article 

Google Scholar 
Sunderland, K. D. Diet of some predatory arthropods in cereal crops. J. Appl. Ecol. 12, 507–515. https://doi.org/10.2307/2402171 (1975).Article 

Google Scholar 
Sunderland, K. D., Lovei, G. L. & Fenlon, J. Diets and reproductive phenologies of the introduced ground beetles Harpalus affinis and Clivina australasiae (Coleoptera: Carabidae) in New Zealand. Aust. J. Zool. 43, 39–50. https://doi.org/10.1071/zo9950039 (1995).Article 

Google Scholar 
Deagle, B. E. & Tollit, D. J. Quantitative analysis of prey DNA in pinniped faeces: Potential to estimate diet composition?. Conserv. Genet. 8, 743–747. https://doi.org/10.1007/s10592-006-9197-7 (2007).Article 
CAS 

Google Scholar 
Snider, A. M., Bonisoli-Alquati, A., Perez-Umphrey, A. A., Stouffer, P. C. & Taylor, S. S. Metabarcoding of stomach contents and fecal samples provide similar insights about Seaside Sparrow diet. Ornithol. Appl. https://doi.org/10.1093/ornithapp/duab060 (2022).Article 

Google Scholar 
Paula, D. P., Timbo, R. V., Togawa, R. C., Vogler, A. P. & Andow, D. A. Quantitative prey species detection in predator guts across multiple trophic levels by mapping unassembled shotgun reads. Mol Ecol Resour 23, 64–80. https://doi.org/10.1111/1755-0998.13690 (2023).Article 
CAS 

Google Scholar 
Elbrecht, V. & Leese, F. Can DNA-based ecosystem assessments quantify species abundance? Testing primer bias and biomass-sequence relationships with an innovative metabarcoding protocol. PLoS ONE https://doi.org/10.1371/journal.pone.0130324 (2015).Article 

Google Scholar 
Krehenwinkel, H. et al. Estimating and mitigating amplification bias in qualitative and quantitative arthropod metabarcoding. Sci. Rep. 7, 17668. https://doi.org/10.1038/s41598-017-17333-x (2017).Article 
ADS 
CAS 

Google Scholar 
Piñol, J., San Andrés, V., Clare, E. L., Mir, G. & Symondson, W. O. C. A pragmatic approach to the analysis of diets of generalist predators: The use of next-generation sequencing with no blocking probes. Mol. Ecol. Resour. 14, 18–26. https://doi.org/10.1111/1755-0998.12156 (2014).Article 
CAS 

Google Scholar 
Baksay, S. et al. Experimental quantification of pollen with DNA metabarcoding using ITS1 and trnL. Sci. Rep. https://doi.org/10.1038/s41598-020-61198-6 (2020).Article 

Google Scholar 
Valentini, A. et al. New perspectives in diet analysis based on DNA barcoding and parallel pyrosequencing: The trnL approach. Mol. Ecol. Resour. 9, 51–60. https://doi.org/10.1111/j.1755-0998.2008.02352.x (2009).Article 
CAS 

Google Scholar 
Murray, D. C. et al. DNA-based faecal dietary analysis: A comparison of qPCR and high throughput sequencing approaches. PLoS ONE https://doi.org/10.1371/journal.pone.0025776 (2011).Article 

Google Scholar 
Hansen, B. K. et al. From DNA to biomass: Opportunities and challenges in species quantification of bulk fisheries products. ICES J. Mar. Sci. 77, 2557–2566. https://doi.org/10.1093/icesjms/fsaa115 (2020).Article 

Google Scholar 
Pawluczyk, M. et al. Quantitative evaluation of bias in PCR amplification and next-generation sequencing derived from metabarcoding samples. Anal. Bioanal. Chem. 407, 1841–1848. https://doi.org/10.1007/s00216-014-8435-y (2015).Article 
CAS 

Google Scholar 
Piñol, J., Mir, G., Gomez-Polo, P. & Agusti, N. Universal and blocking primer mismatches limit the use of high-throughput DNA sequencing for the quantitative metabarcoding of arthropods. Mol. Ecol. Resour. 15, 819–830. https://doi.org/10.1111/1755-0998.12355 (2015).Article 
CAS 

Google Scholar 
Czernik, M. et al. Fast and efficient DNA-based method for winter diet analysis from stools of three cervids: Moose, red deer, and roe deer. Acta Theriol. 58, 379–386. https://doi.org/10.1007/s13364-013-0146-9 (2013).Article 

Google Scholar 
Richardson, R. T. et al. Quantitative multi-locus metabarcoding and waggle dance interpretation reveal honey bee spring foraging patterns in Midwest agroecosystems. Mol. Ecol. 28, 686–697. https://doi.org/10.1111/mec.14975 (2019).Article 
CAS 

Google Scholar 
Taberlet, P. et al. Power and limitations of the chloroplast trnL (UAA) intron for plant DNA barcoding. Nucleic Acids Res. https://doi.org/10.1093/nar/gkl938 (2007).Article 

Google Scholar 
Briem, F. et al. Identifying plant DNA in the sponging-feeding insect pest Drosophila suzukii. J. Pest. Sci. 91, 985–994. https://doi.org/10.1007/s10340-018-0963-3 (2018).Article 

Google Scholar 
Frei, B., Guenay, Y., Bohan, D. A., Traugott, M. & Wallinger, C. Molecular analysis indicates high levels of carabid weed seed consumption in cereal fields across Central Europe. J. Pest. Sci. https://doi.org/10.1007/s10340-019-01109-5 (2019).Article 

Google Scholar 
Luff, M. L. The biology of the ground beetle Harpalus rufipes in a strawberry field in Northumberland. Ann. Appl. Biol. 94, 153–164. https://doi.org/10.1111/j.1744-7348.1980.tb03907.x (1980).Article 

Google Scholar 
Illumina. Effects of index Misassignment on multiplexing and downstream analysis. https://www.illumina.com/content/dam/illumina-marketing/documents/products/whitepapers/index-hopping-white-paper-770-2017-004.pdf?linkId=36607862 accessed 2022-11-10 (2018).Guenay-Greunke, Y., Bohan, D. A., Traugott, M. & Wallinger, C. Handling of targeted amplicon sequencing data focusing on index hopping and demultiplexing using a nested metabarcoding approach in ecology. Sci. Rep. https://doi.org/10.1038/s41598-021-98018-4 (2021).Article 

Google Scholar 
Staudacher, K., Wallinger, C., Schallhart, N. & Traugott, M. Detecting ingested plant DNA in soil-living insect larvae. Soil Biol. Biochem. 43, 346–350. https://doi.org/10.1016/j.soilbio.2010.10.022 (2011).Article 
CAS 

Google Scholar 
Espunyes, J. et al. Comparing the accuracy of PCR-capillary electrophoresis and cuticle microhistological analysis for assessing diet composition in ungulates: A case study with Pyrenean chamois. PLoS ONE https://doi.org/10.1371/journal.pone.0216345 (2019).Article 

Google Scholar 
Wallinger, C. et al. Detection of seed DNA in regurgitates of granivorous carabid beetles. Bull. Entomol. Res. 105, 728–735. https://doi.org/10.1017/s000748531500067x (2015).Article 
CAS 

Google Scholar 
Taberlet, P., Gielly, L., Pautou, G. & Bouvet, J. Universal primers for amplification of 3 noncoding regions of chloroplast DNA. Plant Mol. Biol. 17, 1105–1109. https://doi.org/10.1007/bf00037152 (1991).Article 
CAS 

Google Scholar 
FastQC: A Quality Control Tool for High Throughput Sequence Data [Online]. (2010).Danecek, P. et al. Twelve years of SAMtools and BCFtools. Gigascience https://doi.org/10.1093/gigascience/giab008 (2021).Article 

Google Scholar 
Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. Next Gener. Seq. Data Anal. https://doi.org/10.14806/ej.17.1.200 (2011).Article 

Google Scholar 
Edgar, R. C. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460–2461. https://doi.org/10.1093/bioinformatics/btq461 (2010).Article 
CAS 

Google Scholar 
Camacho, C. et al. BLAST plus: Architecture and applications. BMC Bioinform. https://doi.org/10.1186/1471-2105-10-421 (2009).Article 

Google Scholar 
R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2018).Wickham, H. ggplot2: Elegant Graphics for Data Analysis. (Springer, 2016).Arnold, J. B. ggthemes: Extra Themes, Scales and Geoms for ‘ggplot2’. R package version 4.2.0 https://CRAN.R-project.org/package=ggthemes (2019).Hebbali, A. olsrr: Tools for Building OLS Regression Models. R package version 0.5.3. https://CRAN.R-project.org/package=olsrr (2020).Fox, J. & Weisberg, S. An {R} Companion to Applied Regression. 2nd ed. (Sage, 2011).Zeileis, A. & Hothorn, T. Diagnostic checking in regression relationships. R News 2, 7–10 (2002).
Google Scholar 
Zeileis, A. Econometric computing with HC and HAC covariance matrix estimators. J. Stat. Softw. 11, 1–17. https://doi.org/10.18637/jss.v011.i10 (2004).Article 

Google Scholar 
Zeileis, A., Köll, S. & Graham, N. Various versatile variances: An object-oriented implementation of clustered covariances in {R}. J. Stat. Softw. 95, 1–36. https://doi.org/10.18637/jss.v095.i01 (2020).Article 

Google Scholar 
boot: Bootstrap R (S-Plus) Functions v. R package version 1.3-28 (2021).Davison, A. C. & Hinkley, D. V. Bootstrap Methods and Their Applications. (Cambridge University Press, 1997). More

IUCN. The IUCN red list of threatened species. Version 2022-1. https://www.iucnredlist.org. Accessed on 17 September 2022. (2022).O’Hanlon, S., Rieux, A., Farrer, R. A. & Rosa, G. M. Recent Asian origin of chytrid fungi causing global amphibian declines. Science 360, 621–627 (2018).ADS 

Google Scholar 
Scheele, B. C. et al. Amphibian fungal panzootic causes catastrophic and ongoing loss of biodiversity. Science 363, 1459–1463 (2019).ADS 
CAS 

Google Scholar 
La Marca, E. et al. Catastrophic population declines and extinctions in neotropical Harlequin frogs (Bufonidae: Atelopus). Biotropica 37, 190–201 (2005).
Google Scholar 
Rovito, S. M., Parra-Olea, G., Vasquez-Almazan, C. R., Papenfuss, T. J. & Wake, D. B. Dramatic declines in neotropical salamander populations are an important part of the global amphibian crisis. Proc. Natl. Acad. Sci. U.S.A. 106, 3231–3236 (2009).ADS 
CAS 

Google Scholar 
Stegen, G. et al. Drivers of salamander extirpation mediated by Batrachochytrium salamandrivorans. Nature 544, 353–356 (2017).ADS 
CAS 

Google Scholar 
Martel, A. et al. Recent introduction of a chytrid fungus endangers western palearctic salamanders. Science 346, 630–631 (2014).ADS 
CAS 

Google Scholar 
Green, D. E., Converse, K. A. & Schrader, A. K. Epizootiology of sixty-four amphibian morbidity and mortality events in the USA, 1996–2001. Annu. NY Acad. Sci. 969, 323–339 (2002).ADS 

Google Scholar 
Duffus, A. L. J. & Cunningham, A. A. Major disease threats to European amphibians. Herpetol. J. 20, 117–127 (2010).
Google Scholar 
Teacher, A. G. F., Cunningham, A. A. & Garner, T. W. J. Assessing the long-term impact of Ranavirus infection in wild common frog populations. Anim. Conserv. 13, 514–522 (2010).
Google Scholar 
Chinchar, V. G. & Waltzek, T. B. Ranaviruses: Not just for frogs. PLoS Pathog. 10, e1003850 (2014).
Google Scholar 
Nickerson, M. A. & Mays, C. E. The hellbenders: North American giant salamanders. Milwaukee Public Mus. Publ. Biol. Geol. 1, 1–106 (1973).
Google Scholar 
Wheeler, B. A., Prosen, E., Mathis, A. & Wilkinson, R. F. Population declines of a long- lived salamander: A 20+ year study of hellbenders, Cryptobranchus alleganiensis. Biol. Conserv. 109, 151–156 (2003).
Google Scholar 
Freake, M. J. & DePerno, C. S. Importance of demographic surveys and public lands for the conservation of eastern hellbenders Cryptobranchus alleganiensis alleganiensis in southeast USA. PLoS ONE 12, e0179153 (2017).
Google Scholar 
USFWS. Endangered and threatened wildlife and plants; Endangered status for the Ozark Hellbender salamander. 50 CFR Part 23. Fed. Reg. 76, 61956–61978 (2011).
Google Scholar 
USFWS. Species status assessment report for the Eastern Hellbender (Cryptobranchus alleganiensis alleganiensis). p 104 (2018).Pugh, M., Hutchins, M., Madritch, M., Siefferman, L. & Gangloff, M. M. Land-use and local physical and chemical habitat parameters predict site occupancy by hellbender salamanders. Hydrobiologia 770, 105–116 (2015).
Google Scholar 
Bodinof-Jachowski, C. M. & Hopkins, W. A. Loss of catchment-wide riparian forest cover is associated with reduced recruitment in a long-lived amphibian. Biol. Cons. 202, 215–227 (2018).
Google Scholar 
Bodinof, C. M., Briggler, J. T. & Duncan, M. C. Historic occurrence of the amphibian chytrid fungus Batrachochytrium dendrobatidis in hellbender Cryptobranchus alleganiensis populations from Missouri. Dis. Aquat. Org. 96, 1–7 (2011).
Google Scholar 
Hardman, R. H. et al. Geographic and individual determinants of important amphibian pathogens in hellbenders (Cryptobranchus alleganiensis) in Tennessee and Arkansas, USA. J. Wildl. Dis. 56, 803–814 (2020).CAS 

Google Scholar 
Bales, E. K. et al. Pathogenic chytrid fungus Batrachochytrium dendrobatidis, but not B. salamandrivorans, detected on eastern hellbenders. PLoS ONE 10, e0116405 (2015).
Google Scholar 
Souza, M. J., Gray, M. J., Colclough, P. & Miller, D. L. Prevalence of infection by Batrachochytrium dendrobatidis and ranavirus in eastern hellbenders (Cryptobranchus alleganiensis alleganiensis) in eastern Tennessee. J. Wildl. Dis. 48, 560–566 (2012).
Google Scholar 
Gonynor, J. L., Yabsley, M. J. & Jensen, J. B. A preliminary survey of Batrachochytrium dendrobatidis exposure in hellbenders from a stream in Georgia, USA. Herpetol. Rev. 42, 58–59 (2011).
Google Scholar 
Briggler, J. T., Larson, K. A. & Irwin, K. J. Presence of the amphibian chytrid fungus (Batrachochytrium dendrobatidis) on hellbenders (Cryptobranchus alleganiensis) in the Ozark highlands. Herpetol. Rev. 39, 443–444 (2008).
Google Scholar 
Dusick, A., Flatland, B., Craig, L. & Ferguson, S. What is your diagnosis? Skin scraping from a hellbender. Vet. Clin. Pathol. 46, 183–184 (2017).
Google Scholar 
Dean, N., Ossiboff, R., Bunting, E., Schuler, K., Rothrock, A., & Roblee, K. The eastern hellbender and Batrachochytrium dendrobatidis (Bd) in western New York. In Proceedings of the 65th International Conference of the Wildlife Disease Association p. 151 (2016).Cusaac, J. P. et al. Emerging pathogens and a current-use pesticide: potential impacts on eastern hellbenders. J. Aquat. Anim. Health 33, 24–32 (2021).CAS 

Google Scholar 
Geng, Y. et al. First report of a ranavirus associated with morbidity and mortality in farmed Chinese giant salamanders (Andrias davidianus). J. Comp. Pathol. 145, 96–102 (2011).
Google Scholar 
Hardman, R. H., Irwin, K. J., Sutton, W. B. & Miller, D. L. Evaluation of severity and factors contributing to foot lesions in endangered Ozark Hellbenders, Cryptobranchus alleganiensis bishopi. Front. Vet. Sci. 7, 1–10 (2020).
Google Scholar 
Hernández-Gómez, O., Kimble, S. J. A., Briggler, J. T. & Williams, R. T. Characterization of the cutaneous bacterial communities of two giant salamander subspecies. Microb. Ecol. 73, 445–454 (2017).
Google Scholar 
Miller, B. T. & Miller, J. L. Prevalence of physical abnormalities in eastern hellbender (Cryptobranchus alleganiensis alleganiensis) populations of middle Tennessee. Southeast. Nat. 4, 513–520 (2005).
Google Scholar 
Shoemaker, V. H. & Nagy, K. Osmoregulation in amphibians and reptiles. Annu. Rev. Physiol. 39, 449–471 (1977).CAS 

Google Scholar 
Guimond, R. W. & Hutchison, V. H. Aquatic respiration: An unusual strategy in the hellbender Cryptobranchus alleganiensis alleganiensis (Daudin). Science 182, 1263–1265 (1973).ADS 
CAS 

Google Scholar 
Rollins-Smith, L. A. & Conlon, J. M. Antimicrobial peptide defenses against chytridiomycosis, an emerging infectious disease of amphibian populations. Dev. Comp. Immunol. 29, 589–598 (2005).CAS 

Google Scholar 
Brogden, K. A. Antimicrobial peptides: Pore formers or metabolic inhibitors in bacteria. Nat. Rev. Microbiol. 3, 238–250 (2005).CAS 

Google Scholar 
Xu, X. & Lai, R. The chemistry and biological activities of peptides from amphibian skin secretions. Chem. Rev. 115, 1760–1846 (2015).CAS 

Google Scholar 
Woodhams, D. C. et al. Population trends associated with antimicrobial peptide defenses against chytridiomycosis in Australian frogs. Oecologica 146, 531–540 (2006).ADS 

Google Scholar 
Rollins-Smith, L. A. et al. Antimicrobial peptide defenses of the mountain yellow-legged frog (Rana muscosa). Dev. Comp. Immunol. 30, 831–842 (2006).CAS 

Google Scholar 
Van Rooij, P., Martel, A., Haesebrouck, F. & Pasmans, F. Amphibian chytridiomycosis: A review with focus on fungus-host interactions. Vet. Res. 46, 137 (2015).
Google Scholar 
Demori, I. et al. Peptides for skin protection and healing in amphibians. Molecules 24, 347 (2019).
Google Scholar 
Wu, J. et al. A frog cathelicidin peptide effectively promotes cutaneous wound healing in mice. Biochem. J. 475, 2785–2799 (2018).CAS 

Google Scholar 
Tennessen, J. A. et al. Variations in the expressed antimicrobial peptide repertoire of northern leopard frog (Rana pipiens) populations suggest intraspecies differences in resistance to pathogens. Dev. Comp. Immunol. 33, 1247–1257 (2009).CAS 

Google Scholar 
Tatiersky, L. et al. Effect of glucocorticoids on expression of cutaneous antimicrobial peptides in northern leopard frogs (Lithobates pipiens). BMC Vet. Res. 11, 191 (2015).
Google Scholar 
Pereira, K. E. & Woodley, S. K. Skin defenses of North American salamanders against a deadly salamander fungus. Anim. Conserv. 24, 552–567 (2021).
Google Scholar 
Pereira, K. E. et al. Skin glands of an aquatic salamander vary in size and distribution and release antimicrobial secretions effective against chytrid fungal pathogens. J. Exp. Biol. 221, jeb183707 (2018).
Google Scholar 
Smith, H. K. et al. Skin mucosome activity as an indicator of Batrachochytrium salamandrivorans susceptibility in salamanders. PLoS ONE 13, e0199295 (2018).
Google Scholar 
Meng, P. et al. The first salamander defensin antimicrobial peptide. PLoS ONE 8, e83044 (2013).ADS 

Google Scholar 
Sheafor, B., Davidson, E. W., Parr, L. & Rollins-Smith, L. A. Antimicrobial peptide defenses in the salamander, Ambystoma tigrinum, against emerging amphibian pathogens. J. Wildl. Dis. 44, 226–236 (2008).CAS 

Google Scholar 
Fredericks, L. P. & Dankert, J. R. Antibacterial and hemolytic activity of the skin of the terrestrial salamander, Plethodon cinereus. J. Exp. Zool. 287, 340–345 (2000).CAS 

Google Scholar 
Pei, J. & Jiang, L. Antimicrobial peptide from mucus of Andrias davidianus: Screening and purification by magnetic cell membrane separation technique. Int. J. Antimicrob. Agents 50, 41–46 (2017).CAS 

Google Scholar 
Woodhams, D. C. et al. Adaptations of skin peptide defences and possible response to the amphibian chytrid fungus in populations of Australian green-eyed treefrogs, Litoria genimaculata. Div. Distrib. 16, 703–712 (2010).
Google Scholar 
Hernández-Gómez, O., Briggler, J. T. & Williams, R. N. Influence of immunogenetics, sex and body condition on the cutaneous microbial communities of two giant salamanders. Mol. Ecol. 27, 1915–1929 (2018).
Google Scholar 
Niyonsaba, F., Kiatsurayanon, C., Chieosilapatham, P. & Ogawa, H. Friends or foes? Host defense (antimicrobial) peptides and proteins in human skin diseases. Exp. Dermatol. 26, 989–998 (2017).CAS 

Google Scholar 
Rollins-Smith, L. A., Ramsey, J. P., Pask, J. D., Reinert, L. K. & Woodhams, D. C. Amphibian immune defenses against chytridiomycosis: Impacts of changing environments. Integr. Comp. Biol. 51, 552–562 (2011).CAS 

Google Scholar 
Chinchar, V. G. et al. Inactivation of viruses infecting ectothermic animals by amphibian and piscine antimicrobial peptides. Virology 323, 268–275 (2004).CAS 

Google Scholar 
Woodhams, D. C. et al. Interacting symbionts and immunity in the amphibian skin mucosome predict disease risk and probiotic effectiveness. PLoS ONE 9, e96375 (2014).ADS 

Google Scholar 
Becker, M. H., Brucker, R. M., Schwantes, C. R., Harris, R. N. & Minbiole, K. P. The bacterially produced metabolite violacein is associated with survival of amphibians infected with a lethal fungus. Appl. Environ. Microbiol. 75, 6635–6638 (2009).ADS 
CAS 

Google Scholar 
Bell, S. C., Garland, S. & Alford, R. A. Increased numbers of culturable inhibitory bacterial taxa may mitigate the effects of Batrachochytrium dendrobatidis in Australian wet tropics frogs. Front. Microbiol. 9, 1604 (2018).
Google Scholar 
Zhang, L. & Gallo, R. L. Antimicrobial peptides. Curr. Biol. 26, R14–R19 (2016).CAS 

Google Scholar 
Rollins-Smith, L. A. et al. Antimicrobial peptide defenses of the Tarahumara frog, Rana tarahumarae. Biochem. Biophys. Res. Commun. 297, 361–367 (2002).CAS 

Google Scholar 
R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL http://www.R-project.org/ (2013).Bates, D., Maechler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).
Google Scholar 
Hime, P. M. et al. Genomic data reveal conserved female heterogamety in giant salamanders with gigantic nuclear genomes. G3 Genes Genomes Genet. 9, 3467–3476 (2019).CAS 

Google Scholar 
Mazerolle, M. J. AICcmodavg: Model selection and multimodel inference based on (Q)AIC(c). R package version 2.2–1. https://cran.r-project.org/package=AICcmodavg (2019).Burnham, K. P. & Anderson, D. R. Model Selection and Inference: A Practical Information-Theoretic Approach 2nd edn, 454 (Springer, 2002).MATH 

Google Scholar 
Holden, W. M., Reinert, L. K., Hanlon, S. M., Parris, M. J. & Rollins-Smith, L. A. Development of antimicrobial peptide defenses of southern leopard frogs, Rana sphenocephala, against the pathogenic chytrid fungus, Batrachochytrium dendrobatidis. Dev. Comp. Immunol. 48, 65–75 (2015).CAS 

Google Scholar 
De Caceres, M. & Legendre, P. Associations between species and groups of sites: Indices and statistical inference. Ecology 90, 3 (2009).
Google Scholar  More

Bay, R. A. et al. Genetic coupling of female mate choice with polygenic ecological divergence facilitates stickleback speciation. Curr. Biol. 27, 3344–3349 (2017).CAS 

Google Scholar 
Johannesson, K., Butlin, R. K., Panova, M. & Westram, A. M. Population Genomics: Marine Organisms (eds. Oleksiak, M. F. & Rajora, O. P.) 277–301 (Springer, 2017).Riesch, R. et al. Transitions between phases of genomic differentiation during stick-insect speciation. Nat. Ecol. Evol. 1, 1–13 (2017).
Google Scholar 
Feder, J. L. & Nosil, P. The efficacy of divergence hitchhiking in generating genomic islands during ecological speciation. Evolution 64, 1729–1747 (2010).
Google Scholar 
Rosenblum, E. B., Hickerson, M. J. & Moritz, C. A multilocus perspective on colonization accompanied by selection and gene flow. Evolution 61, 2971–2985 (2007).CAS 

Google Scholar 
Nosil, P., Egan, S. P. & Funk, D. J. Heterogeneous genomic differentiation between walking‐stick ecotypes: “isolation by adaptation” and multiple roles for divergent selection. Evolution 62, 316–336 (2008).
Google Scholar 
Orsini, L., Vanoverbeke, J., Swillen, I., Mergeay, J. & Meester, L. Drivers of population genetic differentiation in the wild: isolation by dispersal limitation, isolation by adaptation and isolation by colonization. Mol. Ecol. 22, 5983–5999 (2013).
Google Scholar 
Sexton, J. P., Hangartner, S. B. & Hoffmann, A. A. Genetic isolation by environment or distance: which pattern of gene flow is most common? Evolution 68, 1–15 (2014).CAS 

Google Scholar 
Roderick, G. K. & Gillespie, R. G. Speciation and phylogeography of Hawaiian terrestrial arthropods. Mol. Ecol. 7, 519–531 (1998).CAS 

Google Scholar 
Juan, C., Emerson, B. C., Oromı́, P. & Hewitt, G. M. Colonization and diversification: towards a phylogeographic synthesis for the Canary Islands. Trends Ecol. Evol. 15, 104–109 (2000).CAS 

Google Scholar 
Brown, R. P., Hoskisson, P. A., Welton, J. H. & Báez, M. Geological history and within‐island diversity: a debris avalanche and the Tenerife lizard Gallotia galloti. Mol. Ecol. 15, 3631–3640 (2006).CAS 

Google Scholar 
O’Connell, K. A., Prates, I., Scheinberg, L. A., Mulder, K. P. & Bell, R. C. Speciation and secondary contact in a fossorial island endemic, the São Tomé caecilian. Mol. Ecol. 30, 2859–2871 (2021).
Google Scholar 
Malhotra, A. & Thorpe, R. S. The dynamics of natural selection and vicariance in the Dominican anole: patterns of within‐island molecular and morphological divergence. Evolution 54, 245–258 (2000).CAS 

Google Scholar 
Brown, R. P., Woods, M. & Thorpe, R. S. Historical volcanism and within-island genetic divergence in the Tenerife skink (Chalcides viridanus). Biol. J. Linnean Soc. 122, 166–175 (2017).
Google Scholar 
Losos, J. Lizards in an Evolutionary Tree: Ecology and Adaptive Radiation of Anoles (University of California Press, 2009).Mahler, D. L., Revell, L. J., Glor, R. E. & Losos, J. B. Ecological opportunity and the rate of morphological evolution in the diversification of Greater Antillean anoles. Evolution 64, 2731–2745 (2010).
Google Scholar 
Wang, I. J., Glor, R. E. & Losos, J. B. Quantifying the roles of ecology and geography in spatial genetic divergence. Ecol. Lett. 16, 175–182 (2013).
Google Scholar 
Beerli, P. & Felsenstein, J. Maximum-likelihood estimation of migration rates and effective population numbers in two populations using a coalescent approach. Genetics 152, 763–773 (1999).CAS 

Google Scholar 
Hey, J. & Nielsen, R. Multilocus methods for estimating population sizes, migration rates and divergence time, with applications to the divergence of Drosophila pseudoobscura and D. persimilis. Genetics 167, 747–760 (2004).CAS 

Google Scholar 
Hey, J. Recent advances in assessing gene flow between diverging populations and species. Curr. Opin. Genet. Dev. 16, 592–596 (2006).CAS 

Google Scholar 
Excoffier, L., Dupanloup, I., Huerta-Sánchez, E., Sousa, V. C. & Foll, M. Robust demographic inference from genomic and SNP data. PLoS Genet. 9, 1003905 (2013).
Google Scholar 
Butlin, R. K. et al. Parallel evolution of local adaptation and reproductive isolation in the face of gene flow. Evolution 68, 935–949 (2014).
Google Scholar 
Rosenblum, E. B., Hoekstra, H. E. & Nachman, M. W. Adaptive reptile color variation and the evolution of the MCIR gene. Evolution 58, 1794–1808 (2004).CAS 

Google Scholar 
Rosenblum, E. B. Convergent evolution and divergent selection: lizards at the White Sands ecotone. Am. Nat. 167, 1–15 (2006).
Google Scholar 
Sumner, F. B. An analysis of geographic variation in mice of the Peromyscus polionotus group from Florida and Alabama. J. Mammal. 7, 149–184 (1926).
Google Scholar 
Davenport, J., & Dellinger, T. Melanism and foraging behaviour in an intertidal population of the Madeiran lizard Podarcis (= Lacerta) dugesii (Milne-Edwards, 1829). Herpetol. J. 5, 200–203 (1995).
Google Scholar 
Galán, P. Demography and population dynamics of the lacertid lizard Podarcis bocagei in north-west Spain. J. Zool. 249, 203–218 (1999).
Google Scholar 
Censky, E. J., Hodge, K. & Dudley, J. Over-water dispersal of lizards due to hurricanes. Nature 395, 556 (1998).CAS 

Google Scholar 
Rolán‐Alvarez, E., Erlandsson, J., Johannesson, K. & Cruz, R. Mechanisms of incomplete prezygotic reproductive isolation in an intertidal snail: testing behavioural models in wild populations. J. Evol. Biol. 12, 879–890 (1999).
Google Scholar 
Ludt, W. B. & Rocha, L. A. Shifting seas: the impacts of Pleistocene sea‐level fluctuations on the evolution of tropical marine taxa. J. Biogeogr. 42, 25–38 (2015).
Google Scholar 
Lambeck, K. Late Pleistocene, Holocene and present sea-levels: constraints on future change. Glob. Planet Change 3, 205–217 (1990). & J.
Google Scholar 
Rosenblum, E. B. The role of phenotypic plasticity in color variation of Tularosa Basin lizards. Copeia 2005, 586–596 (2005).
Google Scholar 
Jin, Y. et al. Dorsal pigmentation and its association with functional variation in MC1R in a lizard from different elevations on the Qinghai–Tibetan plateau. Genome Biol. Evol. 12, 2303–2313 (2020).CAS 

Google Scholar 
Corl, A. et al. The genetic basis of adaptation following plastic changes in coloration in a novel environment. Curr. Biol. 28, 2970–2977 (2018).CAS 

Google Scholar 
Sacchi, R. et al. Genetic and phenotypic component in head shape of common wall lizard Podarcis muralis. Amphib.-Reptilia 37, 301–310 (2016).
Google Scholar 
Dice, L. R. Variation of the deer-mouse (Peromyscus maniculatus) on the Sand Hills of Nebraska and adjacent areas. Contrib. Lab Vertebrate Biol. Univ. Mich. 15, 1–19 (1941).
Google Scholar 
Vitt, L. J., Caldwell, J. P., Zani, P. A. & Titus, T. A. The role of habitat shift in the evolution of lizard morphology: evidence from tropical Tropidurus. Proc. Natl Acad. Sci. USA 94, 3828–3832 (1997).CAS 

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 
Scherrer, R., Donihue, C. M., Reynolds, R. G., Losos, J. B. & Geneva, A. J. Dewlap colour variation in Anolis sagrei is maintained among habitats within islands of the West Indies. J. Evol. Biol. 35, 680–692 (2022).
Google Scholar 
Janson, K. Selection and migration in two distinct phenotypes of Littorina saxatilis in Sweden. Oecologia 59, 58–61 (1983).CAS 

Google Scholar 
Richardson, J. L., Urban, M. C., Bolnick, D. I. & Skelly, D. K. Microgeographic adaptation and the spatial scale of evolution. Trends Ecol. Evol. 29, 165–176 (2014).
Google Scholar 
Engelstoft, C., Robinson, J., Fraser, D. & Hanke, G. Recent rapid expansion of common wall lizards (Podarcis muralis) in British Columbia, Canada. Northwest. Naturalist 101, 50–55 (2020).
Google Scholar 
Cascio, P. L. & Pasta, S. Preliminary data on the biometry and the diet of a microinsular population of Podarcis wagleriana (Reptilia: Lacertidae). Acta Herpetol. 1, 147–152 (2006).
Google Scholar 
Janssen, J., Towns, D. R., Duxbury, M. & Heitkönig, I. M. Surviving in a semi-marine habitat: dietary salt exposure and salt secretion of a New Zealand intertidal skink. Comp. Biochem Physiol. A Mol. Integr. Physiol. 189, 21–29 (2015).CAS 

Google Scholar 
Grismer, L. L. Three new species of intertidal side-blotched lizards (genus Uta) from the Gulf of California, Mexico. Herpetologica 50, 451–474 (1994).
Google Scholar 
Sepúlveda, M., Sabat, P., Porter, W. P. & Fariña, J. M. One solution for two challenges: the lizard Microlophus atacamensis avoids overheating by foraging in intertidal shores. PLoS One 9, 97735 (2014).
Google Scholar 
Hobson, E. S. Observations on diving in the Galapagos marine iguana, Amblyrhynchus cristatus (Bell). Copeia 1965, 249–250 (1965).Balakrishna, S., Amdekar, M. S. & Thaker, M. Morphological divergence, tail loss, and predation risk in urban lizards. Urban Ecosyst. 24, 1391–1398 (2021).
Google Scholar 
Falvey, C. H., Aviles-Rodriguez, K. J., Hagey, T. J. & Winchell, K. M. The finer points of urban adaptation: intraspecific variation in lizard claw morphology. Biol. J. Linn. Soc. 131, 304–318 (2020).
Google Scholar 
Marnocha, E., Pollinger, J. & Smith, T. B. Human‐induced morphological shifts in an island lizard. Evol. Appl 4, 388–396 (2011).
Google Scholar 
Rocha, R., Paixão, M. & Gouveia, R. Predation note: Anthus berthelotii madeirensis (Passeriformes: Motacillidae) catches Teira dugesii mauli (Squamata: Lacertidae) in Deserta Grande, Madeira Archipel. Herpetol. Notes 3, 77–78 (2010).
Google Scholar 
Völkl, W. & Brandl, R. Tail break rate in the Madeiran lizard (Podarcis dugesii). Amphibia-Reptilia 9, 213–218 (1988).Malhotra, A. & Thorpe, R. S. Microgeographic variation in Anolis oculatus, on the island of Dominica, West Indies. J. Evol. Biol. 4, 321–335 (1991).
Google Scholar 
Thorpe, R. S. & Brown, R. P. Microgeographic variation in the colour pattern of the lizard Gallotia galloti within the island of Tenerife: distribution, pattern and hypothesis testing. Biol. J. Linn. Soc. 38, 303–322 (1989).
Google Scholar 
Brown, R. P., Thorpe, R. S. & Báez, M. Parallel within-island microevolution of lizards on neighbouring islands. Nature 352, 60–62 (1991).
Google Scholar 
Báez, M. & Brown, R. P. Testing multivariate patterns of within‐island differentiation in Podarcis dugesii from Madeira. J. Evol. Biol. 10, 575–587 (1997).
Google Scholar 
Cook, L. M. Density of lizards in Madeira. Bocagiana (Funchal) 66, 1–3 (1983).
Google Scholar 
Sadek, R. A. The diet of the Madeiran lizard Lacerta dugesii. Zool. J. Linn. Soc. 73, 313–341 (1981).
Google Scholar 
Brehm, A. et al. Phylogeography of the Madeiran endemic lizard Lacerta dugesii inferred from mtDNA sequences. Mol. Phylogenetics Evol. 26, 222–230 (2003).CAS 

Google Scholar 
Suárez, N. M., Pestano, J. & Brown, R. P. Ecological divergence combined with ancient allopatry in lizard populations from a small volcanic island. Mol. Ecol. 23, 4799–4812 (2014).
Google Scholar 
Towns, D. R. Ecology of the black shore skink, Leiolopisma suteri (Lacertilia: Scincidae), in boulder beach habitats. N. Z. J. Zool. 2, 389–407 (1975).
Google Scholar 
Cook, L. M. Variation in the Madeiran lizard Lacerta dugesii. J. Zool. 187, 327–340 (1979).
Google Scholar 
Troscianko, J. & Stevens, M. Image calibration and analysis toolbox–a free software suite for objectively measuring reflectance, colour, and pattern. Methods Ecol. Evol. 6, 1320–1331 (2015).
Google Scholar 
Schneider, C. A., Rasband, W. S. & Eliceiri, K. W. NIH Image to ImageJ: 25 years of image analysis. Nat. Methods 9, 671–675 (2012).CAS 

Google Scholar 
Rohlf, F. J. The tps series of software. Hystrix, Ital. J. Mammal. 26, 9–12 (2015).
Google Scholar 
Bookstein, F. L. Morphometric Tools for Landmark Data: Geometry and Biology (Cambridge University Press, 1991).Klingenberg, C. P. MorphoJ: an integrated software package for geometric morphometrics. Mol. Ecol. Resour. 11, 353–357 (2011).
Google Scholar 
Rohlf, F. J. & Slice, D. Extensions of the Procrustes method for the optimal superimposition of landmarks. Syst. Biol. 39, 40–59 (1990).
Google Scholar 
Klingenberg, C. P., Barluenga, M. & Meyer, A. Shape analysis of symmetric structures: quantifying variation among individuals and asymmetry. Evolution 56, 1909–1920 (2002).
Google Scholar 
Andrews, S. FastQC: a Quality Control Tool for High Throughput Sequence Data. Babraham Bioinformatics version 0.11.7. https://www.bioinformatics.babraham.ac.uk/projects/fastqc/ (2010).Melo, A. T., Bartaula, R. & Hale, I. GBS-SNP-CROP: a reference-optional pipeline for SNP discovery and plant germplasm characterization using variable length, paired-end genotyping-by-sequencing data. BMC Bioinform. 17, 1–15 (2016).
Google Scholar 
Sabadin, F., Carvalho, H. F., Galli, G. & Fritsche-Neto, R. Population-tailored mock genome enables genomic studies in species without a reference genome. Mol. Genet. Genom. 297, 33–46 (2022).CAS 

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

Google Scholar 
Pfeifer, B., Wittelsbürger, U., Ramos-Onsins, S. E. & Lercher, M. J. PopGenome: an efficient swiss army knife for population genomic analyses in R. Mol. Biol. Evol. 31, 1929–1936 (2014).CAS 

Google Scholar 
Team, R. C. R: A language and environment for statistical computing. R Foundation for Statistical Computing. https://www.R-project.org/ (2022).Jombart, T. adegenet: a R package for the multivariate analysis of genetic markers. Bioinformatics 24, 1403–1405 (2008).CAS 

Google Scholar 
Luu, K., Bazin, E. & Blum, M. G. pcadapt: an R package to perform genome scans for selection based on principal component analysis. Mol. Ecol. Resour. 17, 67–77 (2017).CAS 

Google Scholar 
Günther, T. & Coop, G. Robust identification of local adaptation from allele frequencies. Genetics 195, 205–220 (2013).
Google Scholar 
Dray, S. et al. Package ‘adespatial.’ Available from: https://cran.r-project.org/package=adespatial (2018).Montano, V. & Jombart, T. An eigenvalue test for spatial principal component analysis. BMC Bioinform. 18, 1–7 (2017).
Google Scholar 
Pickrell, J. K. & Pritchard, J. K. Inference of population splits and mixtures from genome-wide allele frequency data. PLoS Genet. 8, e1002967 (2012).CAS 

Google Scholar  More

Muller, Z. et al. Giraffa camelopardalis. The IUCN red list of threatened species 2016:e.T9194A109326950 (2018).Oconnor, D. et al. Updated geographic range maps for giraffe, Giraffa spp., throughout sub-Saharan Africa, and implications of changing distributions for conservation. Mamm. Rev. 49, 285–299. https://doi.org/10.1111/mam.12165 (2019).Article 

Google Scholar 
Brown, M. B. et al. Conservation status of giraffe: Evaluating contemporary distribution and abundance with evolving taxonomic perspectives. Ref. Module Earth Syst. Environ. Sci. https://doi.org/10.1016/B978-0-12-821139-7.00139-2 (2021).Article 

Google Scholar 
Dunn, M. E. et al. Investigating the international and pan-African trade in giraffe parts and derivatives. Conserv. Sci. Pract. 3, e390. https://doi.org/10.1111/csp2.390 (2021).Article 

Google Scholar 
Hassanin, A. et al. Mitochondrial DNA variability in Giraffa camelopardalis: Consequences for taxonomy, phylogeography and conservation of giraffes in West and Central Africa. C.R. Biol. 330, 265–274. https://doi.org/10.1016/j.crvi.2007.02.008 (2007).Article 
CAS 

Google Scholar 
Groves, C. & Grubb, P. Ungulate Taxonomy (Johns Hopkins University Press, 2011).Book 

Google Scholar 
Fennessy, J. et al. Multi-locus analyses reveal four giraffe species instead of one. Curr. Biol. 26, 1–7. https://doi.org/10.1016/j.cub.2016.07.036 (2016).Article 
CAS 

Google Scholar 
Winter, S., Fennessy, J. & Janke, A. Limited introgression supports division of giraffe into four species. Ecol. Evol. 8, 10156–10165. https://doi.org/10.1002/ece3.4490 (2018).Article 

Google Scholar 
Bercovitch, F. B. Giraffe taxonomy, geographic distribution, and conservation. Afr. J. Ecol. 58, 150–158. https://doi.org/10.1111/aje.12741 (2020).Article 

Google Scholar 
Petzold, A. & Hassanin, A. A comparative approach for species delimitation based on multiple methods of multi-locus DNA sequence analysis: A case study of the genus Giraffa (Mammalia, Cetartiodactyla). PLoS ONE 15, e0217956. https://doi.org/10.1371/journal.pone.0217956 (2020).Article 
CAS 

Google Scholar 
Petzold, A. et al. First insights into past biodiversity of giraffes based on mitochondrial sequences from museum specimens. Eur. J. Taxon. 703, L57-63. https://doi.org/10.1371/journal.pone.0217956 (2020).Article 
CAS 

Google Scholar 
Coimbra, R. T. F. et al. Whole-genome analysis of giraffe supports four distinct species. Curr. Biol. 31, 2929-2938.e5. https://doi.org/10.1016/j.cub.2021.04.033 (2021).Article 
CAS 

Google Scholar 
Muneza, A. B. et al. Giraffa camelopardalis ssp. reticulata. The IUCN Red List of Threatened Species 2018:e.T88420717A88420720 (2018).Miller, M. F. Dispersal of Acacia seeds by ungulates and ostriches in an African Savanna. J. Trop. Ecol. 12, 345–356. https://doi.org/10.1017/S0266467400009548 (1996).Article 

Google Scholar 
Palmer, T. M. et al. Breakdown of an ant-plant mutualism follows the loss of large herbivores from an African savanna. Science 319, 192–195. https://doi.org/10.1126/science.1151579 (2008).Article 
ADS 
CAS 

Google Scholar 
Kalema, G. Investigation of a skin disease in giraffe in Murchison Falls National Park. Report Submitted to Uganda National Park. Uganda National Parks. Kampala, Uganda (1996).Muneza, A. B. et al. Regional variation of the manifestation, prevalence, and severity of giraffe skin disease: A review of an emerging disease in wild and captive giraffe populations. Biol. Conserv. 198, 145–156. https://doi.org/10.1016/j.biocon.2016.04.014 (2016).Article 

Google Scholar 
Epaphras, A. M., Karimuribo, E. D., Mpanduji, D. G. & Meing’ataki, G. E. Prevalence, disease description and epidemiological factors of a novel skin disease in giraffes (Giraffa camelopardalis) in Ruaha National Park, Tanzania. Res. Opin. Anim. Vet. Sci. 2, 60–65 (2012).
Google Scholar 
Lee, D. E. & Bond, M. L. The occurrence and prevalence of giraffe skin disease in protected areas of northern Tanzania. J. Wildl. Dis. 52, 753–755. https://doi.org/10.7589/2015-09-24 (2016).Article 

Google Scholar 
Muneza, A. B. et al. Examining disease prevalence for species of conservation concern using non-invasive spatial capture–recapture techniques. J. Appl. Ecol. 54, 709–717. https://doi.org/10.1111/1365-2664.12796 (2017).Article 

Google Scholar 
Brown, M. Murchison falls giraffe project: Field report. Giraffid 9, 5–10 (2015).
Google Scholar 
Muneza, A. B. et al. Quantifying the severity of an emerging skin disease affecting giraffe populations using photogrammetry analysis of camera trap data. J. Wildl. Dis. 55, 770–781. https://doi.org/10.7589/2018-06-149 (2019).Article 

Google Scholar 
Han, S. et al. Giraffe skin disease: Clinicopathologic characterization of cutaneous filariasis in the critically endangered Nubian giraffe (Giraffa camelopardalis camelopardalis). Vet. Pathol. https://doi.org/10.1177/03009858221082606 (2022).Article 

Google Scholar 
Whittier, C. A. et al. Cutaneous filariasis in free-ranging Rothschild’s giraffes (Giraffa Camelopardalis rothschildi) in Uganda. J. Wildl. Dis. 56, 1–5. https://doi.org/10.7589/2018-09-212 (2020).Article 

Google Scholar 
Pellew, R. Food consumption and energy budgets of the giraffe. J. Appl. Ecol. 21, 141–159. https://doi.org/10.2307/2403043 (1984).Article 

Google Scholar 
Strauss, M. K. L. & Packer, C. Using claw marks to study lion predation on giraffes of the Serengeti. J. Zool. 289, 134–142. https://doi.org/10.1111/j.1469-7998.2012.00972.x (2013).Article 

Google Scholar 
Muneza, A. B. et al. Exploring the connections between giraffe skin disease and lion predation. J. Zool. https://doi.org/10.1111/jzo.12930 (2021).Article 

Google Scholar 
Lindsey, P. A. et al. The bushmeat trade in African savannas: Impacts, drivers, and possible solutions. Biol. Conserv. 160, 80–96. https://doi.org/10.1016/j.biocon.2012.12.020 (2013).Article 

Google Scholar 
Becker, M. et al. Evaluating wire-snare poaching trends and the impacts of by-catch on elephants and large carnivores. Biol. Conserv. 158, 26–36. https://doi.org/10.1016/j.biocon.2012.08.017 (2013).Article 

Google Scholar 
Mudumba, T., Jingo, S., Heit, D. & Montgomery, R. A. The landscape configuration and lethality of snare poaching of sympatric guilds of large carnivores and ungulates. Afr. J. Ecol. 59, 51–62. https://doi.org/10.1111/aje.12781 (2020).Article 

Google Scholar 
Strauss, M. K. L., Kilewo, M., Rentsch, D. & Packer, C. Food supply and poaching limit giraffe abundance in the Serengeti. Popul. Ecol. 57, 505–516. https://doi.org/10.1007/s10144-015-0499-9 (2015).Article 

Google Scholar 
Munn, J. Effects of injury on the locomotion of free-ranging chimpanzees in the Budongo Forest Reserve, Uganda. In Primates of Western Uganda: Developments in Primatology: Progress and Prospects (eds. Newton-Fisher, N. E., Notman, H., Paterson, J. D., & Reynolds, V.) 259–280 (Springer, 2006).Yersin, H., Asiimwe, C., Voordouw, M. J. & Zuberbühler, K. Impact of snare injuries on parasite prevalence in wild chimpanzees (Pan troglodytes). Int. J. Primatol. 38, 21–30. https://doi.org/10.1007/s10764-016-9941-x (2017).Article 

Google Scholar 
Dagg, A. I. Gaits of the giraffe and okapi. J. Mammal. 41, 282–282. https://doi.org/10.2307/1376381 (1960).Article 

Google Scholar 
Dagg, A. I. The role of the neck in the movements of the giraffe. J. Mammal. 43, 88–97. https://doi.org/10.2307/1376883 (1962).Article 

Google Scholar 
Dagg, A. I. & Vos, A. D. The walking gaits of some species of Pecora. J. Zool. 155, 103–110. https://doi.org/10.1111/j.1469-7998.1968.tb03031.x (1968).Article 

Google Scholar 
Alexander, R. M. N., Langman, V. A. & Jayes, A. S. Fast locomotion of some African ungulates. J. Zool. 183, 291–300. https://doi.org/10.1111/j.1469-7998.1977.tb04188.x (1977).Article 

Google Scholar 
Basu, C., Deacon, F., Hutchinson, J. R. & Wilson, A. M. The running kinematics of free-roaming giraffes, measured using a low cost unmanned aerial vehicle (UAV). PeerJ 7, e6312. https://doi.org/10.7717/peerj.6312 (2019).Article 

Google Scholar 
Basu, C., Wilson, A. M. & Hutchinson, J. R. The locomotor kinematics and ground reaction forces of walking giraffes. J. Exp. Biol. 222, jeb159277. https://doi.org/10.1242/jeb.159277 (2019).Article 

Google Scholar 
Hildebrand, M. The adaptive significance of tetrapod gait selection. Am. Zool. 20, 255–267. https://doi.org/10.1093/icb/20.1.255 (1980).Article 

Google Scholar 
Flower, F. C., Sanderson, D. J. & Weary, D. M. Hoof pathologies influence kinematic measures of dairy cow gait. J. Dairy Sci. 88, 3166–3173. https://doi.org/10.3168/jds.s0022-0302(05)73000-9 (2005).Article 
CAS 

Google Scholar 
Brown, M. B., Bolger, D. T. & Fennessy, J. All the eggs in one basket: A countrywide assessment of current and historical giraffe population distribution in Uganda. Glob. Ecol. Conserv. 19, e00612. https://doi.org/10.1016/j.gecco.2019.e00612 (2019).Article 

Google Scholar 
Foster, J. B. The giraffe of Nairobi National Park: Home range, sex ratios, the herd, and food. Afr. J. Ecol. 4, 139–148. https://doi.org/10.1111/j.1365-2028.1966.tb00889.x (1966).Article 

Google Scholar 
Bond, M. L., Strauss, M. K. L. & Lee, D. E. Soil correlates and mortality from giraffe skin disease in Tanzania. J. Wildl. Dis. 52, 953–958. https://doi.org/10.7589/2016-02-047 (2016).Article 

Google Scholar 
Dunham, N. T., McNamara, A., Shapiro, L., Hieronymus, T. & Young, J. W. A user’s guide for the quantitative analysis of substrate characteristics and locomotor kinematics in free-ranging primates. Am. J. Phys. Anthropol. 167, 569–584. https://doi.org/10.1002/ajpa.23686 (2018).Article 

Google Scholar 
Rueden, C. T. et al. Imagej 2: Imagej for the next generation of scientific image data. BMC Bioinform. 18, 529. https://doi.org/10.1186/s12859-017-1934-z (2017).Article 

Google Scholar 
Cartmill, M., Lemelin, P. & Schmitt, D. Support polygons and symmetrical gaits in mammals. Zool. J. Linn. Soc. 136, 401–420. https://doi.org/10.1046/j.1096-3642.2002.00038.x (2002).Article 

Google Scholar 
Hildebrand, M. Analysis of the symmetrical gaits of tetrapods. Folia Biotheoretica 6, 1–22. https://doi.org/10.2307/1379571 (1966).Article 

Google Scholar 
Shapiro, L. J. & Young, J. W. Kinematics of quadrupedal locomotion in sugar gliders (Petaurus breviceps): Effects of age and substrate size. J. Exp. Biol. 215, 480–496. https://doi.org/10.1242/jeb.062588 (2012).Article 

Google Scholar 
Shapiro, L. J., Young, J. W. & VandeBerg, J. L. Body size and the small branch niche: Using marsupial ontogeny to model primate locomotor evolution. J. Hum. Evol. 68, 14–31. https://doi.org/10.1016/j.jhevol.2013.12.006 (2014).Article 

Google Scholar 
Dunham, N. T., McNamara, A., Shapiro, L., Phelps, T. & Young, J. W. Asymmetrical gait kinematics of free-ranging callitrichines in response to changes in substrate diameter, orientation, and displacement. J. Exp. Biol. 223, jeb217562. https://doi.org/10.1242/jeb.217562 (2020).Article 

Google Scholar 
Robinson, R., Herzog, W. & Nigg, B. Use of force platform variables to quantify the effects of chiropractic manipulation on gait symmetry. J. Manipulative Physiol. Ther. 10, 172–176 (1987).CAS 

Google Scholar 
Vanden Hole, C. et al. How innate is locomotion in precocial animals? A study on the early development of spatiotemporal gait variables and gait symmetry in piglets. J. Exp. Biol. 220, 2706–2716. https://doi.org/10.1242/jeb.157693 (2017).Article 

Google Scholar 
Jacobs, B. Y., Kloefkorn, H. E. & Allen, K. D. Gait analysis methods for rodent models of osteoarthritis. Curr. Pain Headache Rep. 18, 456–475. https://doi.org/10.1007/s11916-014-0456-x (2014).Article 

Google Scholar 
Pfau, T., Spence, A., Starke, S., Ferrari, M. & Wilson, A. Modern riding style improves horse racing times. Science 325, 289–289. https://doi.org/10.1126/science.1174605 (2009).Article 
ADS 
CAS 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing. (R Foundation for Statistical Computing, 2019). http://www.R-project.org/.Kuznetsova, A., Brockhoff, P. B. & Christensen, R. H. B. LmerTest package: Tests in linear mixed effects models. J. Stat. Softw. https://doi.org/10.18637/jss.v082.i13 (2017).Article 

Google Scholar 
Length, R. emmeans: Estimated marginal means, aka least‐squares means. R package version 0.9. https://CRAN.R-project.org/package=emmeans (2017).Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: A practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B (Methodol.) 57, 289–300. https://doi.org/10.1111/j.2517-6161.1995.tb02031.x (1995).Article 
MathSciNet 
MATH 

Google Scholar 
Merkens, H. W. & Schamhardt, H. C. Evaluation of equine locomotion during different degrees of experimentally induced lameness I: Lameness model and quantification of ground reaction force patterns of the limbs. Equine Vet. J. 20, 99–106. https://doi.org/10.1111/j.2042-3306.1988.tb04655.x (1988).Article 

Google Scholar 
Fanchon, L. & Grandjean, D. Accuracy of asymmetry indices of ground reaction forces for diagnosis of hind limb lameness in dogs. Am. J. Vet. Res. 68, 1089–1094. https://doi.org/10.2460/ajvr.68.10.1089 (2007).Article 

Google Scholar 
Bragança, F. M. S., Rhodin, M. & van Weeren, P. R. On the brink of daily clinical application of objective gait analysis: What evidence do we have so far from studies using an induced lameness model?. Vet. J. 234, 11–23. https://doi.org/10.1016/j.tvjl.2018.01.006 (2018).Article 

Google Scholar 
Brown, M. B. & Bolger, D. T. Male-biased partial migration in a giraffe population. Front. Ecol. Evol. 7, 524. https://doi.org/10.3389/fevo.2019.00524 (2020).Article 

Google Scholar 
Dagg, A. I. Giraffe: Biology, Behaviour and Conservation (Cambridge University Press, 2014).Book 

Google Scholar 
Castles, M. P. et al. Relationships between male giraffes’ colour, age and sociability. Anim. Behav. 157, 13–25. https://doi.org/10.1016/j.anbehav.2019.08.003 (2019).Article 

Google Scholar  More