Philippa Kaur

Study area and samplingSmall mammals (murid rodents and shrews) were captured using mouse-type snap traps in Tatarstan, Russian Federation (Fig. 1, Table S1). Area type (urban or rural), vegetation (forest or field) and distance from trapping points to the nearest human settlement were recorded. The distinction between forest and field was made based on the UN Food and Agriculture Organization’s criteria23,24. Each administrative division in the Tatarstan was defined to be urban or rural by the Federal Service of State Statistics of Russian Federation25. Based on these criteria, Kazan city and Naberezhnye Chelny city were classified as urban districts and Vysokogorsky district, Yelabuzhsky district, Laishevsky district, Mamadyshsky district, Nizhnekamsky district, Pestrechinsky district and Tukayevsky district were classified as rural districts. Small mammals were captured during the spring and fall periods of 2016 and 2017. Fifty traps were placed in a line every 5 m in one place. Traps were baited and left for one night. Animal suffering was minimized as snap traps cause rapid death in murid rodents and shrews. Each captured small mammal’s species, age, and sex were morphologically identified using a reference guide26, and the animals were then stored at − 20 °C until their brains were isolated.EthicsAll experiments were performed in compliance with relevant Russian and Japanese and institutional laws and guidelines and were approved by the Ministry of Health of the Russian Federation and the Animal Research Committee of Gifu University (Permit Nos. MU 3.1.1029-01, and 17060, respectively). Study was carried out in compliance with the ARRIVE guidelines (https://arriveguidelines.org).DNA extraction and PCRBrain tissue samples were prepared as described previously12. Brain samples stored at − 20 °C were transferred to a − 86 °C deep freezer. Each deep-frozen whole brain sample was homogenized in 1 ml of a 0.9% saline solution. Total DNA was extracted from the brain tissues of each small mammal using a Genomic DNA Purification Kit (Promega, Madison, WI, USA), following the manufacturer’s instructions. Nested PCR was performed with the Takara PCR Amplification Kit (Takara Bio Inc., Foster City, California, USA) according to the manufacturer’s instructions. The primer sets and PCR conditions used to detect the B1 gene from T. gondii were those described previously12.MappingSpatial referencing of the sampling sites was conducted using global positioning system navigation with a Garmin eTrex 10 device. Visualization of cartographic data and measurements of the distances from the trapping points to the nearest human settlements were performed using QGIS 3.12 software27. Geodetic coordinates were projected into planar rectangular coordinates in the Universal Transverse Mercator projection on the WGS-84 ellipsoid (Universal Transverse Mercator, zone 39N). The overview map of the European part of Russia was made in the Lambert Conformal Conic Projection. Map coordinates are represented as geodetic coordinates (WGS-84, degrees and minutes north latitude and east longitude). To visualize thematic objects (administrative boundaries, forests, agricultural lands, and water bodies), a set of vector data layers, NextGIS (Russia), was purchased from OpenStreetMap and contributors, 2021 (https://data.nextgis.com). Data license: ODbL.Dataset and statistical analysesMultivariate logistic regression was performed using the R statistical software package (version 3.6.3)28 to assess the trapping point area (urban or rural), vegetation (forest or field), small mammal species type (alien or non-alien species), age (0–2 months-old juveniles, 3–6 months-old adults or ≧ 6 months old), sex (male or female) and distance from trapping points to the nearest human settlements as risk factors for PCR positivity. According to previous reports2,13,16,17,18, four species, Mi. arvalis, A. flavicollis, A. agrarius, A. uralensis, and three species, My. glareolus, S. araneus and D. nitedula are considered alien and non-alien species, respectively. Quantitative data were replaced with 0 or 1 dummy variables, and age data were replaced by 0, 1 and 2 for juveniles, adults and elders, respectively. Multicollinearity of the explanatory variables was evaluated using Spearman’s coefficient29 calculated using dplyr, FSA and psych packages30,31,32. None of the Spearman’s coefficients were  > 0.6. To find the best fit model, a forward selection procedure was used. Predictive performance and model fitting were assessed using the area under the receiver operating characteristic (ROC) curve, area under the curve (AUC) and corrected Akaike’s information criterion (AICc) with Akaike weight (Wi). AICc and Wi were calculated using the MuMIN package33, and the AUC was calculated using the R pROC package34. P-values of  More

The micro-CT results from Arthrorhynchus agree perfectly with the previously known light microscope and transmission electron microscope images2. This emphasizes that microtomography is a good technique to visualize the type of fungal attachment to the host and especially the penetration of the cuticle, apart from the study of thallus in amber fossils17. As Jensen et al. (2019) demonstrated the presence of a haustorium in Arthrorhynchus using scanning electron microscopy, we are confident that the lack of penetration and haustorium in Rickia found by micro-CT is real. This is also in agreement with results from the scanning electron microscopical investigation of the attachment sites of R. gigas, which exhibits no indication of penetration and are very similar to those of R. wasmannii previously shown18.Despite the absence of a haustorium, and hence without any obvious means of obtaining nutrition, Rickia gigas is quite a successful fungus, being often abundant on several species of Afrotropical millipedes of the family Spirostreptidae10. It was originally described from Archispirostreptus gigas, and Tropostreptus (= ‘Spirostreptus’) hamatus20, and was subsequently reported from several other Tropostreptus species19.A further challenge for Laboulbeniales growing on millipedes is that infected millipedes, in some species even adults, may moult, shedding the exuviae with the fungus, as has been observed by us on an undescribed Rickia species on a millipede of the genus Spirobolus (family Spirobolidae).The question of how non-haustoriate Laboulbeniales obtain nutrients has been discussed by several authors18, including staining experiments using fungi of the non-haustoriate genus Laboulbenia on various beetles21. Whereas the surface of the main thallus was almost impenetrable to the dye applied (Nile Blue), the smaller appendages could sometimes be penetrated21. The dye injection into the beetle elytra upon which the fungi were sitting, actually spread from the elytron into the fungus, thus indicating that in spite of the lack of a haustorium, the fungus is able to extract nutrients from the interior of its host21.Such experiments have not been performed on Rickia species, but the possibility that nutrients may pass from the host into the basis of the fungus cannot be excluded. For this genus, or at least R. gigas, there may, however, be an alternative way to obtain nutrients: the small opening in the circular wall by which the thallus is attached to the host may allow nutrients from the surface of the millipede or from the environment to seep into the foot of the fungus. However, further experiments are needed in order to evaluate this hypothesis. Moreover, we should not exclude a potential role of primary and secondary appendages in Laboulbeniales nutrition, as we still do not understand exactly their functional role on the fungus life cycle11.The predominant position of the Laboulbeniales on the host might be related to the absence or presence of a haustorium. Thus, the haustoriate species of the genus Arthrorhynchus are most frequently encountered in large numbers on the arthrodial membranes of the host’s abdomen, although some thalli are found on legs2,22. At the arthrodial membranes the cuticle is more flexible and therefore might be easier to penetrate by a parasite. Furthermore, most tissues providing/storing nutrition (e.g., fat body) are located within the abdomen. In contrast, non-haustoriate fungi as are often located on more stiff and sclerotized body-parts like the genus Rickia on the legs or body-rings of millipedes7,20,23 or the genus Laboulbenia on the elytra of beetles21,24. A reason for this might be that the non-haustoriate forms, which are only superficially attached to the host need a more or less smooth surface for adherence and can easily become detached from a flexible surface, which is movable in itself, like the arthrodial membrane, while the haustoriate forms are firmly anchored within the hosts’ cuticle.Whereas the vast majority of the more than 2000 described species of Laboulbeniales show no sign of host penetration, haustoria have been reported from some other genera18, including Trenomyces parasitizing bird lice25,26, Hesperomyces growing on coccinellid beetles and Herpomyces on cockroaches (formerly a Laboulbeniales and now in the order Herpomycetales10), with pernicious consequences on the hosts’ fitness18,27. Micro-CT studies on these genera could help to understand the host penetration. In order to fully understand how Laboulbeniales obtain nourishment, although other approaches are, also needed—for the time being it remains a mystery how the non-haustoriate Laboulbeniales sustain themselves. More

Collection and domestication of the wild populationsThe academic permission for collections and research on medicinal plants was obtained from the Head of Biotechnology Department, Research Institute of Modern Biological Techniques, University of Zanjan, Zanjan, Iran. The study complies with all relevant guidelines. Some populations of wild spinaches were harvested during spring season 2013 from the mountain habitat of this wild plant in the Tarom region of Zanjan province from an altitude of 2500–3000 m and were transferred to the greenhouses conditions. The domestication and cultivation experiments were conducted at Research Institute of Modern Biological Techniques, University of Zanjan, 1579° m above sea level, with 48° 28′ longitude and 36° 40′ latitude, from April 2013 to August 2020. The resulted seeds were cultured on pots to produce adequate seeds. The seedlings were transferred to the field with rows spaced 50 cm apart and also 50 cm between plants within the rows. Two seeds per hill were planted in an area of approximately 50 m2. Based on the organic conditions, no fertilization was performed. Thinning was done 25 days after emergence, leaving one plant per hill. The other cultural practices were those normally adopted for cultivation in the region.Mass selection of populationsIn the first year, phenotypic studies were performed during the growing season and weak, diseased and underdeveloped plants were removed from the field before the flowering stage. Then plants with the same phenotype and the desired traits were selected and after harvesting, their seeds were mixed. This election cycle was repeated for 5 years. In the final year, the new mass selected population was compared in a pilot project with cultivated spinach in traits such as yield, resistance to wilt, cold and pests, diseases, and mineral contents. This variety before the certification in the related national organization is a candida cultivar. It is a developed population that will be evaluated in the session of the Iranian variety of introduction committee.The seeds of cultivated spinach (Spinacia oleracea L. |Varamin 88|) were prepared from the Research Institute of Modern Biological Techniques, University of Zanjan, Zanjan, Iran.Performing tests of stability, uniformity and differentiationTo assess morphologically and differentiate advanced uniformity in the studied population (Candida cultivar), the population was managed as a randomized complete block design with three replications over 2 years according to the instructions for spinach differentiation, uniformity, and stability (DUS Testing) of the International Union New Plant Cultivation (UPOV) and some morphological traits on plants or parts of plants. The studied traits included: cotyledon length, presence or absence of anthocyanin in petiole and veins, green color intensity, shrinkage, presence of lobes in the petiole, petiole state, petiole length, foil shape, foil edge shape, tip shape, and part of the length of the petiole, the time of flowering and the color of the seeds.Mineral analysesTo compare the mineral content of mass-selected population-medicinal spinach (MSP) with cultivated spinach (Spinacia oleracea L. var. Varamin 88), both plants were planted in pots and fields on similar conditions. In five leaves stage, plant samples were taken from both leaf and crown sections. The sampling method was such that after removing half a meter from the beginning and end of each plot (to remove the marginal effect) and also removing the two sidelines, five plants were harvested randomly for plant mineral analysis. Atomic absorption spectroscopy was used to determine the mineral content including iron (Fe), zinc (Z), manganese (Mn), and copper (Cu).The dried samples of root-crown and leave were stored, and later grounded and analyzed for iron (Fe), zinc (Z), manganese (Mn), and copper (Cu) in mass-selected variety (MSP) and cultivated spinach (CSP). Studied minerals were measured using atomic absorption spectrometry in the model of GBC AVANTA (GBC scientific equipment Ltd., Melbourne, Vic., Australia).Calibration of AAS was done using the working standard prepared from commercially available metal/mineral standard solutions (1000 μg/mL, Merck, Germany). The most appropriate wavelength, hollow cathode lamp current, gas mixture flow rate, slit width, and other AAS instrument parameters for metals/minerals were selected as given in the instrument user’s manual, and background correction was used during the determination of metals/minerals. Measurements were made within the linear range of working standards used for calibration15,16.The concentrations of all the minerals were expressed as mg/1000 g (ppm) dry weight of the sample. Each value is the mean of three replicate determination ± standard deviation.Scanning electron microscopy (SEM)For SEM studies, the seeds enveloping were removed and were acetolyzed in a 1:9 sulfuric acid-acetic anhydride solution. The seeds were vigorously shaken for 5 min. Then, they were left for 24–48 h in the solution. After this time, seeds were again shaken for 5 min and then washed.in distilled water by shaking for a further 5 min. The seeds were dried overnight and then were mounted on stubs and covered with Au–Pd by sputter coater model SC 7620. After coating, coated seeds were photographed with an LEO 1450 VP Scanning Electron Microscope. All photographs were taken in the Taban laboratory (Tehran, Iran).Statistical analysisThe statistical evaluation including: data transformation, analysis of variance and comparison of means were performed (SPSS software, Version 11.0). The experiment was structured following a randomized complete block design (RCBD) with three replications. Means comparisons were conducted using an ANOVA protected the least significant difference (LSD) test, with the ANOVA confidence levels of 0.95. Data were presented with their standard deviations (SD). More

Dispersal modelsIn this study, the dispersal model consists of two parts, namely, kernel and observation model (Fig. 1). The main purpose of the kernel was employed to estimate the proportion of pollen dispersed from location s′ to location s and calculate the expected number of CP grains. The observation model used the expected number of CP grains as a parameter and described the number of CP grains at location s (Ys) by a specific distribution in the following:$${Y}_{s}sim fleft(left.{y}_{s}right|{{varvec{theta}}}_{s}right),$$
(1)
where f indicates the probability density function (PDF) of the specific distribution. The θs is the parameter vector of the distribution. This study constructed eight different dispersal models combined with two observation models, two kernels, and two conditions of the field border (FB) effect (Table 1). The details of the kernels and observation models were described in the following subsections.Figure 1Graphical summary of the establishment of the dispersal model using ZIP distribution observation model as an example.Full size imageTable 1 List of dispersal models constructed in this study.Full size tableKernelsThe kernel indicates the probability when the pollen emitted at location s′ and would fall down at location s. It can be expressed as γ(s, s′), where s′ is the source location closest to location s. Numerous kernels have been used to describe various dispersal phenomena24. The output of the kernel represents the donor pollen density of location s. In order to calculate the expected number of CP grains, the donor pollen density is multiplied by the average total grain number described as follows:$${lambda }_{s}=Ktimes gamma left(s,{s}^{^{prime}}right),$$
(2)
where λs and K indicate the expected number of CP grains at location s and the average number of grains per cob, respectively. The effect of the FB was introduced into the kernel to suit to the small-scale farming system in Asia. This study assumed that the relation between the pollen density at the first recipient row and the width of the FB displayed an exponential decrease25,26. To evaluate the improvement of the kernel with the FB effect, the kernels without the FB effect were also established in this study.The compound exponential kernel (γExpo) has been used in the previous pollen dispersal study27. Our study introduced the FB effect into this kernel. Therefore, the form of the compound exponential kernel can be expressed as follows:$$gamma_{{{text{Expo}}}} left( {s,s^{prime}} right) = left{ {begin{array}{*{20}l} {K_{e} exp left( { – a_{1} d^{*} left( {s,s^{prime}} right)} right)exp left( { – ksqrt {FB} } right),} \ {K_{e} exp left( { – a_{1} D – a_{2} left( {d^{*} left( {s,s^{prime}} right) – D} right)} right)exp left( { – ksqrt {FB} } right),} \ end{array} } right.begin{array}{*{20}l} {{text{if}},, d^{*} left( {s,s^{prime}} right) le D} \ {{text{if}} ,,d^{*} left( {s,s^{prime}} right) > D,} \ end{array}$$
(3)
where Ke, a1, a2, k, D are the parameters of the kernel. d*(s, s′) indicates the shortest distance between locations s′ and s in which the width of the FB has been subtracted. In the compound exponential kernel without the FB effect, the exponential term of the FB effect was removed and the d*(s, s′) was replaced directly by the shortest distance between s′ and s.The second kernel applied in this study was the modified Cauchy kernel (γCauchy) which was based on the PDF of the Cauchy distribution and the concept of compound distribution. The modified Cauchy kernel is represented as follows:$$gamma_{Cauchy} left( {s,s^{prime}} right) = left{ {begin{array}{*{20}l} {frac{2beta }{{pi left[ {beta^{2} + d^{*} left( {s,s^{prime}} right)^{2} } right]}}{text{exp}}left( { – ksqrt {FB} } right),} \ {frac{2beta }{{pi left[ {beta^{2} + D^{2} + c_{1} left( {d^{*} left( {s,s^{prime}} right) – D} right)^{2} } right]}}{text{exp}}left( { – ksqrt {FB} } right),} \ end{array} } right.begin{array}{*{20}l} {{text{if}} ,,d^{*} left( {s,s^{prime}} right) le D} \ {{text{if}} ,,d^{*} left( {s,s^{prime}} right) > D,} \ end{array}$$
(4)
where the β indicates the decline rate of the curve. Parameters of k and D are same as the compound exponential kernel. c1 indicates the relative slow decrease of pollen density at further distances. Similarly, in the modified Cauchy kernel without the FB effect, the term of the FB effect was removed and the d*(s, s′) was replaced directly by the shortest distance between s′ and s in which the row spacing (0.75 m) had been subtracted.Observation modelsBecause of the high proportions of zero value observations, the present study assumed that the CP grain count followed the zero-inflated Poisson (ZIP) distribution to account for zero-excess condition28. The ZIP distribution was first proposed by Lambert29, and several studies had applied the ZIP distribution to deal with the CP data27,30. The ZIP distribution consists of a Dirac distribution in zero and a Poisson distribution. Therefore, the distribution of CP grain count at location s (Ys) can be expressed as follows:$${Y}_{s}sim mathrm{ZIP}left(1-{q}_{s},{uplambda }_{s}right),$$
(5)
where qs indicates the probability of an observation following a Poisson distribution, and λs is the parameter of Poisson distribution calculated by Eq. (2). Furthermore, the parameter qs can be assumed to depend on the shortest distance between the recipient and donor plants. The border effect is also included in the estimation of qs because it is related to the distance effect. The relationship among distance, border, and the qs can be described using the following logistic function:$${q}_{s}=frac{1}{1+mathrm{exp}({b}_{1}-{b}_{2}{d}^{*}left(s,{s}^{^{prime}}right))},$$
(6)
where b1 and b2 are the parameters of the logistic function. The d*(s, s′) was the shortest distance between s′ and s in the version of dispersal models without the FB effect. The Poisson distribution was also used as an observation model for comparison with the ZIP observation model.Experimental and meteorological data collectionThe pollen dispersal data were collected from experiments performed in 2009 and 2010 at the geographic coordinates 23° 47′ N, 120° 26′ E, and an altitude of 20 m. These experiments were coded as 2009-1, 2009-2, and 2010-1, respectively. The experiment 2009-2 was divided into 2009-2A (without the FB) and 2009-2B (with the FB) based on the presence of the FB. The different layouts of the field experiments were designed to investigate the effect of the FB. Two commercial glutinous maize varieties, black pearl (purple grain) and Tainan No. 23 (white grain), were selected as the pollen donor and pollen recipient, respectively. The distance between the plants in a row was 25 cm, whereas the distance between the rows was 75 cm. The recipient plots consisted of 82 and 91 rows in 2009 and 2010 experiments, respectively.The CP rate was determined based on the differences in grain color on recipient cobs as a result of the xenia effect31. In the sampling framework, the whole field was divided into many grids and corn samples were collected from each grid in the whole field. The CP rate of each grid was calculated using the method presented in a previous study32 and defined as:$$mathrm{CP}left(%right)=left[sum_{i=1}^{n}{Cob}_{i}/left(ntimes Kright)right],$$
(7)

where Cobi and n indicate ith cob and total number of cobs in the grid, respectively. K is the average grain number per cob. Meteorological data were collected from the meteorological station at geographic coordinates 23° 35′ N, 120° 27′ E, and an altitude of 20 m. The detailed experimental setup was described in our previous study33. The study complies with relevant institutional, national, and international guidelines and legislation.Statistical analysesAll statistical analyses were performed using SAS (Statistical Analysis System, version 9.4). The dispersal model parameters were estimated by two methods. First, the nonlinear model estimation was conducted by PROC NLMIXED to evaluate the fitting and predictive abilities of dispersal models. Then the dispersal models with the observation model performed better fitting ability were re-estimated using the Bayesian estimation method to assess the uncertainty by PROC MCMC. In the Bayesian method, the noninformative prior distribution was used to estimate all parameters (Supplementary Table S1). The iteration of Markov Chain was 500,000 times and the burn-in was set to 450,000 iterations. In order to reduce the autocorrelations in the chain, the thinned value was set to 25.The validation method used in this study was the threefold cross-validation for the results of both estimation methods. The data from three experiments were combined and randomly partitioned into three sub-datasets. To avoid the heterogeneity of the different field designs and distances among sub-datasets, the observations from the same field design and same distance were considered as a group, and then partitioned into three parts. Each sub-dataset contained one part of all groups. At each validation run, two sub-datasets were selected as the training set, and the remaining one was used for validation.The fitting ability of the dispersal models was evaluated based on two criteria, namely, Akaike information criterion (AIC), Deviance, and coefficient of determination (R2). The smaller values of AIC or deviance indicate a better fitting. The higher R2 value represents a better fitting performance. The correlation coefficient (r) between the predicted and actual CP rates was used to assess the predictive ability. The deviance information criterion (DIC) was used to evaluate the performance of dispersal model fitting for the Bayesian estimation. The criterion values calculated from three training and validation sets were averaged to assess the overall results. The uncertainty of the model parameter was quantified by the standard deviation (SD) of parameter posterior distribution. The 95% credible intervals of posterior predictive distribution constructed by the 2.5th and 97.5th percentiles of 200,000 samples generated from the posterior predictive distribution were used to assess the predictive uncertainty. Furthermore, to assess the zero-excess condition, the percentage of observed zero CP grain events was compared with the Poisson probability of the zero CP grain event. A zero-excess condition occurred if the observed percentage was higher than the Poisson probability34. More

Coastline segmentsFor reasons of data availability and socioeconomic relevance, the analysis was limited to latitudes between 66° N and −60° S. In this area of interest, the world was divided in 1 arcmin (~2 km) grid cells. To define a logical position for the establishment of an efficient levee, the coastline location was derived from the OpenStreetMap68, moved 100 m land inward and smoothed. For every cell containing a coastline segment, coastline length and a coast-normal transect were derived at the center of segments resulting in 495.361 transects that are on average 1.1 km apart. Bootstrapping revealed that transect distances up to 2 km give very similar results. All transects stretch 4 km seaward and 4 km inland to fully capture most foreshores.Elevation dataA global intertidal bathymetry/elevation dataset from high-resolution EO data (USGS Landsat and Copernicus Sentinel-2), the Foreshore Assessment using Space Technology (FAST) intertidal elevation map69, was produced to compliment commonly used global data products with low resolution and higher inaccuracy in intertidal zones. Global coastlines were divided over 25000 tiles of each 40 × 40 km2. For these tiles, all available images were collected for the period between 1997 and 2017. Surface water was identified, using normalized difference spectral indices (NDSI, here SWIR1 and Green band) for all images (median of 317 images per tile) covering various tidal conditions, and the per pixel mean calculated to derive time-ensemble average (TEA) NDSI images. We developed a new technique to transform TEA images to intertidal elevation independently of in situ calibration data. TEA-NDSI images were normalized by the spatially averaged NDSI values of regions identified (using global elevation datasets) as land and water, respectively. This resulted in a single image per tile that represented the inundation probability for each pixel in the intertidal zone. The inundation probability represents the long-term average tidal inundation, because it was derived from a collection of images that span a time period similar to the tidal epoch (period of 19 years). Pixels having a probability of 1 represent permanent water, and have elevations less than or equal to the lowest astronomical tide (LAT), whereas land (p = 0) represents elevations higher than or equal to the highest astronomical tide (HAT). By deduction, p = 0.5 is equivalent to local mean sea level (LMSL). Tidal statistics from the global tide model FES2012 were used to couple the derived inundation probability to an elevation. The main source of bed level data originates from this map and has a 20 m horizontal resolution and typically a 30–50 cm vertical accuracy (RMSE = 0.52 m, MAE 0.42 m, as assessed at a number of sites with high quality elevation data (Supplementary Fig. 7)). Bathymetry data (GEBCO35; 30 arc-second horizontally, tens of metres vertically) and topography data (MERIT36; 3 arc-seconds, 2 m vertically) were merged to create a continuous bathymetry-elevation map by changing the vertical datum of MERIT from EGM96 to MSL by assuming 0 m +MSL at the OSM coastline. Global bathymetry datasets (e.g. GEBCO) and elevation datasets (e.g. SRTM and MERIT) lack accuracy (especially nearshore), but are commonly used17,18,23,34. The final bed level was constructed using FAST intertidal data where sufficient valid data points were available, complemented by the merged GEBCO-MERIT data where these points were lacking.Vegetation extentThe FAST coastal vegetation map69 was based on Landsat-8 and Sentinel-2 satellite images collected between 2013 and 2017. The map provides actual vegetation presence at 10 m resolution. Vegetation presence was obtained by applying an individual NDVI threshold per tile, with a total of 25,000 tiles, based on the yearly NDVI average and NDVI amplitude. The FAST coastal vegetation map is validated based on NDVI comparison with local measurements taken at Zuidgors, The Netherlands (R2 = 0.92) (Supplementary Fig. 8). If vegetation was present, the vegetation type was determined by global salt marsh32 and mangrove14 maps, complemented with Corine Land Cover30 (CLC, Europe only) and GlobCover v2.231 maps when there is no coverage. Determining global coastal vegetation extent is difficult and affected by eutrophication in coastal environments. This behaviour is observed on the coast along the Persian Gulf and the Red Sea. To improve accuracy only vegetated transects identified by the global salt marsh32 and mangrove14 map and confirmed by the FAST coastal vegetation map are included for these areas. Moreover, vegetated transects with a green belt width smaller than 250 m identified by GlobCover are excluded from the study for accuracy reasons (Supplementary Fig. 8). To avoid mixed vegetation types, the vegetation type was determined by the most dominant type. The vegetation width constituted of the sum of vegetated grid cells between the start and the end of the vegetated zone.Water level and wave dataThe design water levels were based on a combination of tide and storm surge for the selected probability of occurrence (return periods 2, 5, 10, 25, 50, 100 default, 250, 500, 1000 years) and came from the GTSR dataset34. SLR and subsidence were not taken into account because this study focuses on the present situation. Moreover, quantifying the future role of vegetated foreshores would not only require SLR scenarios but also an insight in the development of wetlands over time, which is strongly determined by local conditions such as sediment supply56,57,60. Offshore wave conditions were obtained from ERA-Interim33 re-analysis, based on data from 1979 till 2017 and reprojected to Dynamic Interactive Vulnerability Assessment (DIVA)70 points. Next, the Peak Over Threshold method was applied to construct representative values for the significant offshore wave height, Hs and the peak wave period Tp for all the return periods. The nearshore wave height was limited by the local water depth at the start of the (vegetated) foreshore using a breaker criterion (gamma = 0.55). This is a fairly low value considering the range of values cited in literature71 leading to conservative wave attenuation by vegetation results. Wave-bottom interactions in the sub-tidal zone and processes such as refraction and diffraction are not explicitly simulated. The conservative breaker criterion is chosen to implicitly account for these processes in a conservative manner. The wave period remained unchanged and the wave direction was assumed coast normal and wave growth along the transect due to wind effects was excluded. However, for the current study a more sophisticated approach to account for longshore wave variability based on topography was considered infeasible at the global scale and considered to yield limited outcome looking at the uncertainty in socioeconomic factors. The average Hs,offshore = 4.6 m (std = 2.0 m) and the average Hs,startforeshore = 0.7 m (std = 0.7 m).Profile constructionThe 8 kilometre coast-normal transects consisted of 321 gridpoints, thus a horizontal grid resolution of 25 m. We used four different methods: Foreshore method 1 (based on the FAST intertidal elevation map), Foreshore method 2–4 (based on MERIT-GEBCO). The properties of the FAST intertidal elevation map, MERIT and GEBCO are described under the header ‘Elevation data’. Foreshore method 1 produced the most accurate profiles and foreshore method 4 the least accurate profiles. The profile construction steps are described hereafter. Validity checks were performed to identify false indications of intertidal area in the FAST intertidal elevation map. Individual data points were marked invalid and removed in case: (1) MERIT points were situated above the surge level with a return period of 2 years, while data from the intertidal map indicated a lower elevation. (2) Data from the FAST intertidal map was situated at open sea. (3) Data from the FAST intertidal map along the transect dropped below a minimum range threshold of 10 cm. A fourth check was performed based on the continuity of the data. Data from the FAST intertidal map contain discontinuities along the profile. These continuities exist on pixel level due to the use of the modified normalized difference water index and in some instances cloud coverage was preventing full coverage. Lastly, discontinuities arise due to the presence of (high elevated) tidal flats and banks in coastal areas. (4) Data length was defined as the length of continuous data points along the transect. If the data length of a patch decreased below a threshold of 100 m, the points were marked invalid. Gaps between valid data patches were filled using linear interpolation if the gap was smaller than 250 m. Eventually, one, none or multiple valid data patches were found along the transects. See Supplementary Fig. 2 for example transects.Global coastline shapes range from straight sandy coastal stretches to complex coastlines often found in estuaries. With a transect length of 8 km, the start and the end of the transects could both be situated on land, hampering an unambiguous identification of the foreshore of interest. We designed the algorithm such that the last foreshore was selected. For profiles using data from the FAST intertidal map (foreshore method 1, 50.9% of populated susceptible coastlines), the last valid patch corresponds to the last foreshore. The inclusion of tidal flats as part of the foreshore was determined based on the gap length. In case no (sufficient, thus not satisfying the minimum data length criterion of 100 m) valid data was available from the FAST intertidal map based on the four described checks, the profile was based on a merged GEBCO-MERIT set (methods 2, 3 and 4), respectively, 46.1%, 3.0% and 0.01%. For the second method, data points were selected between a minimum threshold of −2 m MSL and a maximum threshold equal to the surge level with a return period of 2 years. Next, for the selected points the direction of the slope was determined by comparing elevation between the data point concerned and the next data point. This resulted in patches of upward sloping sets of data points between the minimum and maximum threshold. Similar to foreshore method 1, the validity of the patches was checked using data length, gap length and the corresponding thresholds of 100 m and 250 m. The start and the end of the foreshore were determined by the first and last valid point of the last patch. Foreshore method 3 was used if not sufficient foreshore data were available to satisfy the minimum data length threshold (100 m). In these cases, the start of the foreshore was defined as the first upcrossing intersection with −2 m MSL along the transect. The end of the foreshore corresponded to the intersection between the elevation profile and the governing surge level with a return period of 2 years. Foreshore method 4 was used if no start and or end of the foreshore could be found. In this case the start and/or end point of the foreshore corresponded to the first and last data point, respectively.In some cases, elevation for the end of the foreshore was missing due to several reasons. First, the upper part of the intertidal zone was sometimes missing from the FAST intertidal map, due to low frequency of inundation of the upper intertidal zone or cloud cover. Second, bed elevation in mangrove belts was hard to define based on satellite imagery, as the canopy is detected as the earth surface. These uncertainties were counteracted by consulting the mangrove and salt marsh maps. If vegetation was present in one of these maps, the derived foreshore was extended until the end of the vegetated zone. An elevation equal to the surge level with a return period of 2 years was chosen as elevation for extended foreshore points with an elevation exceeding this surge level.Vegetation parametersAs deducting the type and size of mangrove trees and salt marshes from EO data at global scale is not possible (yet), the current modelling approach relies on field and literature observations. For the scope of this research the properties of the mangrove trees occurring at the seaward side of the mangrove belt are the most relevant. To avoid overestimation of wave attenuation in young mangrove forests, the mangrove dimensions are chosen such to be representative for young fringing pioneering mangroves up to a height of 3 m that are practically vertically uniform compared to mature trees. The modelling approach uses four parameters to represent vegetation: height, diameter, number of stems and drag coefficient. The exact characteristics are based on observations in literature8,9,72,73,74,75,76 (N = 30 m−2, d = 35 mm, h = 3.0 m).High quality observations on wave attenuation by mangroves under storm conditions do not exist. For the drag coefficient the theoretical value, 1, of a rigid cylinder is chosen, because mangrove trunks can be considered rigid. For salt marshes a winter state representative as found in NW Europe is chosen. The values are defined based on FAST field tests (Romania, UK, Spain and the Netherlands) and literature10,24,77,78 (N = 1225 m−2, d = 1.25 mm, h = 0.30 m). A drag coefficient (CD) of 0.19 is chosen, which is the lower limit found during large-scale flume tests10. The drag coefficient depends on biophysical characters as well hydrodynamics. The drag coefficient represents drag due to skin friction and pressure differences, but also effects like swaying motion of stems24. The 1D modelling approach takes into account gaps in vegetation cover, e.g. due to the presence of channels. Zonation of vegetation types is not implemented, because this level of detail is insignificant in relation to the inaccuracies induced by the use of global datasets.Wave attenuation modelTo determine wave attenuation along the foreshore transects and the resulting significant wave heights relevant for the flood defence on a transect, we used a lookup-table approach. The lookup table was generated by combining 668,304 model output values for different combinations of foreshore slopes, vegetation covers and hydrodynamic conditions. The table contained wave heights modelled by XBeach79 in surfbeat mode (a nearshore numerical wave model that accounts for the presence of vegetation) at regular intervals along a steady slope, both with and without vegetation. XBeach uses for wave-vegetation interaction the rigid cylinder80 approach and includes an energy sink term to the wave energy balance to implement wave dampening81. We used conservative vegetation characteristics, winter state salt marshes and young pioneering mangroves. We characterized foreshores by their width and slope. The foreshore profile was the same for simulations with and without vegetation. The foreshore width was determined by calculating the distance between the start and the end of the foreshore. The slope was estimated using a linear regression. This approach has two advantages over detailed modelling of wave attenuation over all transects: it is much quicker, allowing for iterative improvements of the workflow and it does not suggest the precision one would expect from detailed models but cannot be delivered with global data. Average Hs,endforeshore,noveg = 0.6 m (std = 0.5 m) and Hs, endforeshore,veg = 0.3 m (std = 0.4 m).Coastline susceptible to flooding, urban and rural extents and population densityTo assess the need for coastal flood defences, we made a distinction between areas susceptible to coastal flooding and higher, non-susceptible areas. We determined susceptible areas based on possible inundation using coastal flood maps of 1 km resolution for a 1/1000 year surge level. These maps were created with a global geographic information system (GIS) based inundation model that is forced with a spatially varying sea level, accounting for attenuation of the water level due to land surface roughness82. A method that is more sophisticated compared to a simple ‘bathtub’ inundation method. Topographic features, as visible in MERIT, protecting the land from flooding are considered. To classify coastlines as urban or rural a distinction was made based on gridded population from the LandScan database83 using the 2UP model84. A transect is characterized ‘urban’ if it intersects at least one cell with an urban population with a minimum of 1. Populated coasts have been identified by assigning the population density of the population susceptible to flooding in the proximity of the transects. We used WorldPop201785 population data and assigned population to the transects using a buffer of 15 kilometre radius. The population density is the division of the assigned population and the total area of the assigned cells. This procedure is repeated for buffer radius of 5, 10 and 20 km, giving fairly comparable outcomes. Following this approach we found a ratio between rural and urban transects of 73/27.Levee crest heightsThe empirical EuroTop formulations47 gave the required levee heights with respect to water levels and wave heights, assuming the presence of a levee at the end of the vegetated foreshore. We hereby neglected the position and characteristics of levees present in the current situation, as no global dataset of coastal protection structures exists. The assumed levee had a standard 1:3 levee profile without berms and an allowed overtopping discharge of 1 l s−1 m−1. These parameters are representative for simple, low-cost levees in developing countries but conservative for well-constructed and maintained levees. Consequently, savings on levee heights in countries with strict protection standards are overestimated, as reduction in required levee height due to vegetation presence is likely less than predicted here. However, this may be balanced out by the fact that we calculated with an average national construction cost per kilometre and levees applying to stricter protection standards may actually be more expensive (Supplementary Fig. 5).Costs for levee construction and crest height reductionThe calculated levee crest height reductions were monetized using a levee unit price per kilometre length per metre heightening. We used an unit investment costs of levees (metre heightening per kilometre length) of USD 7.0 million42. This estimate represents an average of construction costs in the USA and the Netherlands stated in several studies86,87,88,89. It pertains to all investments costs, including ground work, construction, engineering costs, property or land acquisition, environmental compensation, and project management. Investment costs per metre heightening are well described by a linear function without intercept90. They concluded that for large-scale studies it is sufficient to assume linear costs for each metre of heightening, including the initial costs and the 95% confidence range is between 3x and x/3, where x is the unit cost value. Subsequently we applied three unit levee investment cost prices (low: USD 2.33 million, mid: USD 7.0 million, high: USD 21 million) in line with previous studies42,90. These cost estimates were then adjusted for all other countries by applying construction index multipliers (based on civil engineering construction costs91), to account for differences in construction costs across countries92. Costs were converted to USD2005 power purchasing parity (PPP), to be consistent with the SSPs, using GDP deflators from the World Bank (https://data.worldbank.org/), and annual average market exchange rates between Euros and USD taken from the European Central Bank (unit levee cost per country = unit levee cost x construction index per country / PPP MER rate 2005 index per country). Example: mid unit levee costsUSA = 7.0 ×1 / 1 = 7.0 million USD2005 PPP km m−1. If for a country data was not available in the database, we used the average of all countries in the same World Bank income group. For the reference year 2005, this applies to Western Sahara (ESH), North-Korea (PKR) and Somalia (SOM).ReliabilityA scoring table was used to get insight in the reliability of the results of the global analysis. Results were grouped into four reliability classes ranging from “poor” to “very good”. Transects were placed in these classes based on data accuracy for three characteristics: hydrodynamics, vegetation and profile elevation. In Supplementary Fig. 6 the (sub) results of the analysis are presented. The first category, hydrodynamics, included known inaccuracies in the hydrodynamic data (GTSM and ERA-I). Data from the GTSM model was considered less reliable in areas with a low tidal range and/or with tropical storms, such as cyclones or hurricanes, as those were not included in our analyses. Also wave data from ERA-I are less reliable in these areas, because the effects of tropical storms are flattened due to the relatively coarse grid size. Hence, transects in these areas were pinpointed by linking them to NOAA data of historical hurricane tracks93. In Supplementary Fig. 6B, areas where tropical storms occur can clearly be recognized. In addition, the Mediterranean Sea, the Red Sea, the Black sea and the Caspian sea stand out in inaccuracy, because of limited tidal action.Reliability of vegetation characteristics was determined by data source and vegetation width. For transects with extensive vegetation widths, crest height reduction was less sensitive for possible deviations of the vegetation width, due the non-linear relation between vegetation width and wave reduction. Vegetation cover proved most reliable in areas where data from the salt marsh32—and mangrove map14 were available. Hence, this resulted in a ‘good’ score (Supplementary Fig. 6C). Only in cases of extensive vegetation presence was a ‘very good’ score assigned. Transects were appointed as “very good” if vegetation extended 500 m for mangroves, and 1000 m for salt marshes. These thresholds are chosen based on our model results, which show that after ~500 m (salt marshes) and 1000 m (mangroves) maximum reduced wave transmission by foreshore vegetation is reached. Vegetation cover reliability in Europe was classified as ‘good’, due to reliable vegetation type classification based on CLC30 and the salt marsh map32 in combination accompanied by relatively small vegetation widths. The reliability of the derived vegetation characteristics is especially lacking at the east coast of Canada, at Latin America’s south coast, at Africa’s coasts facing the Mediterranean Sea, coasts along the Red Sea and the Persian Gulf, and along the coasts of China, Japan and Russia. For example, in the Persian Gulf states the vegetation presence map tends to falsely identify foreshores as vegetated.The time-ensemble average (TEA) technique applied for the FAST intertidal elevation map relies on the availability of a reasonable number of images at different tidal stages where the differences in horizontal extent of water coverage can be identified, thus allowing a composite of inundation frequency to be derived. However, the technique is limited by the effective sensor resolution (~30 m, including uncertainty in georeferencing) relative to the horizontal extent of changes in inundation, a function of the tidal range and bed slope. Hence, changes in tidal water extent in microtidal or very high bed-slope regions tend to be too small for reliable discerning differences, leading to poor performance of the technique. However, the merged GEBCO-MERIT dataset was considered less reliable than the FAST intertidal map, based on the resolution and the merging of the two underlying datasets in the intertidal zone. In addition, MERIT tends to overestimate the elevation in mangrove areas, as it measures the canopies as the earth’s surface. Besides the elevation data, the foreshore definition method is used as a profile reliability indicator. The total score per transect is given by the sum of the sub-scores. The sub-scores are normalized to give equal weight to the scoring categories.ValidationFor validation of our method to assess vegetation presence, a comparison of 280 randomly located transects with aerial imagery was carried out. The area accessed in the global assessment was divided in tiles of 90 degrees longitude and 15 degrees latitude. From each tile 6 vegetated and 2 non-vegetated transects were selected. Next, a reference dataset was created by manually identifying vegetation presence using present imagery. Lastly, the vegetation width derived by the model and the manually derived set were compared (Supplementary Fig. 8). For this comparison we made three distinctions, based on (1) vegetation type, (2) foreshore derivation method and (3) vegetation cover source. Comparison showed that the used algorithm on global EO data performs satisfactorily (Supplementary Fig. 8), but in some cases tends to assign a vegetation cover of up to 250 m where there is none. Deviation between observation and the global assessment, is caused by methodological error in the global assessment and inaccuracy in the global datasets, e.g. different timestamps are inevitably compared. This would induce an exaggeration of the effect of vegetation. However, due to the limited dimension of the vegetation extent, the threshold for substantial crest height reduction is falsely exceeded in not more than 2.4% of the cases and the effect is largely balanced out by underestimation of the vegetation cover at larger lengths.To validate wave reduction by vegetation calculated through our lookup table approach, we compared results with local modelling results for the South-Western part of the Netherlands for 38 vegetated transects. The numerical model SWAN94 in stationary mode was used to translate wave conditions from offshore to nearshore. The simulations were performed with a grid size of 0.01 deg and bathymetry from EMODNET95. Extreme water levels were included by a water depth correction, using data from GTSR18. Both wind and wave boundary conditions were derived from the earlier described ERA-I re-analysis. The governing wave direction was based on the average of the fifteenth highest wave events in the available wave data. The wind direction was assumed to be aligned with the wave direction. A parametric JONSWAP spectrum shape was used, using a peak enhancement factor of 3.3 and directional spreading of 20 degrees. Foreshore profiles were constructed using an approach similar to foreshore method 2 in the global study but using local high-resolution bathymetry and topography data. Vegetation width was extracted from the salt marsh map32, which was confirmed locally using aerial imagery. Foreshore wave propagation was determined using XBeach in surfbeat mode79.Our results showed an overestimation of the water depth at the start of the vegetated zone by 0.73 m on average. In addition, the global model derived milder slopes in comparison to the local analysis for narrow vegetated transects. The largest errors were found further away from the mouth of the estuary. Here, the deviation between the wave calculated by SWAN and the depth limited approach is largest. The wave height at the start of the vegetated zone was overestimated on average by 1.12 m, due to the complex geometry and the sheltered configuration of the estuary. The algorithm approximated the wave transmission reduction (RMSE 13%) and the levee crest height reduction relative to the required crest height without vegetation presence (RMSE 19%) with reasonable accuracy (Supplementary Fig. 9).Sensitivity analysisA sensitivity analysis has been performed to provide insight in the uncertainty in the presented potential global levee costs savings. The analysis focused specifically on single key parameters, such as the levee unit cost, the critical overtopping discharge and the wave breaker index. High, mid and low levee unit cost scenarios are taken from previous studies42,90. A high, mid, low for the critical overtopping discharge are respectively 10, 1 and 0.1 l s−1 m−1 to incorporate the quality of the levee cover47. We chose RP10 and RP1000 for, respectively, the low and high storm return period scenario. The uncertainty spread of vegetation width is based on the 75% confidence intervals of the underestimated and overestimated vegetation widths of mangroves (+436 m, −136 m) and salt marshes (+597 m, −104 m) in the vegetation presence validation study. For the breaker index we solely chose a high scenario of 0.78, because the index of the global assessment (0.55) was already quite conservative71. For topography we applied a range corresponding to the typical vertical accuracy of the FAST intertidal elevation dataset (±50 cm). Two representative subsets of 500 transects for respectively mangroves and salt marshes have been derived using the clustering method k-means96, based on hydrodynamic conditions, vegetation cover, profile characteristics and geographical location. With these subsets, we repeated the analysis procedure of the global assessment for the sensitivity scenarios. The results point out that the largest spread is caused by the uncertainty in the unit levee cost with −66% and +200% for, respectively, the low and high scenario with respect to the global reference analysis. The other scenarios: topography (−39%, +47%), critical overtopping discharge (−40%, + 40%), storm return period (−28%, +34%), vegetation width (−28%, +39%), breaker index (+21%) (Supplementary Fig. 10). Larger water depths result in a decrease of depth-induced wave energy dissipation and more dissipation due to wave-vegetation interaction, which explains the outcomes of the topography sensitivity results. Similarly, an increase of the storm return period or the breaker index shifts the ratio of wave energy dissipation by wave-bottom interaction and wave-vegetation interaction. The coastal protection costs by vegetation are sensitive to critical overtopping discharge changes, because of the non-linear relation between the wave height in front of the levee and the overtopping discharge47. More

1.Rabosky, D. L. Diversity-dependence, ecological speciation, and the role of competition in macroevolution. Annu. Rev. Ecol. Evol. Syst. 44, 481–502 (2013).
Google Scholar 
2.Callaway, R. M. et al. Positive interactions among alpine plants increase with stress. Nature 417, 844–848 (2002).ADS 
CAS 
PubMed 

Google Scholar 
3.Maestre, F. T., Callaway, R. M., Valladares, F. & Lortie, C. J. Refining the stress-gradient hypothesis for competition and facilitation in plant communities. J. Ecol. 97, 199–205 (2009).
Google Scholar 
4.Verdú, M., Jordano, P. & Valiente-Banuet, A. The phylogenetic structure of plant facilitation networks changes with competition. J. Ecol. 98, 1454–1461 (2010).
Google Scholar 
5.Gavini, S. S., Ezcurra, C. & Aizen, M. A. Plant–plant interactions promote alpine diversification. Evol. Ecol. 33, 195–209 (2019).
Google Scholar 
6.Eriksson, O. Evolution of angiosperm seed disperser mutualisms: the timing of origins and their consequences for coevolutionary interactions between angiosperms and frugivores. Biol. Rev. 91(1), 168–186 (2016).PubMed 

Google Scholar 
7.Tur, C., Sáez, A., Traveset, A. & Aizen, M. A. Evaluating the effects of pollinator-mediated interactions using pollen transfer networks: Evidence of widespread facilitation in south Andean plant communities. Ecol. Lett. 19, 576–586 (2016).CAS 
PubMed 

Google Scholar 
8.Braun, J. & Lortie, C. J. Finding the bees knees: A conceptual framework and systematic review of the mechanisms of pollinator-mediated facilitation. Perspect. Plant Ecol. Evol. Syst. 36, 33–40 (2019).
Google Scholar 
9.Ollerton, J., Winfree, R. & Tarrant, S. How many flowering plants are pollinated by animals?. Oikos 120, 321–326 (2011).
Google Scholar 
10.Waser, N. M. & Ollerton, J. Plant-Pollinator Interactions: From Specialization to Generalization (University of Chicago Press, 2006).
Google Scholar 
11.Biella, P. et al. Experimental loss of generalist plants reveals alterations in plant-pollinator interactions and a constrained flexibility of foraging. Sci. Rep. 9, 7376 (2019).ADS 
PubMed 
PubMed Central 

Google Scholar 
12.Arceo-Gómez, G. et al. Global geographic patterns of heterospecific pollen receipt help uncover potential ecological and evolutionary impacts across plant communities worldwide. Sci. Rep. 9(1), 8086 (2019).ADS 
PubMed 
PubMed Central 

Google Scholar 
13.Morales, C. L. & Traveset, A. Interspecific pollen transfer: Magnitude, prevalence and consequences for plant fitness. Crit. Rev. Plant Sci. 27, 221–238 (2008).CAS 

Google Scholar 
14.Mitchell, R. J., Flanagan, R. J., Brown, B. J., Waser, N. M. & Karron, J. D. New frontiers in competition for pollination. Ann. Bot. 103, 1403–1413 (2009).PubMed 
PubMed Central 

Google Scholar 
15.Moeller, D. A. Facilitative interactions among plants via shared pollinators. Ecology 85, 3289–3301 (2004).
Google Scholar 
16.Ghazoul, J. Floral diversity and the facilitation of pollination. J. Ecol. 94, 295–304 (2006).
Google Scholar 
17.Muñoz, A. A. & Cavieres, L. A. The presence of a showy invasive plant disrupts pollinator service and reproductive output in native alpine species only at high densities. J. Ecol. 96, 459–467 (2008).
Google Scholar 
18.Hegland, S. J., Grytnes, J. A. & Totland, O. The relative importance of positive and negative interactions for pollinator attraction in a plant community. Ecol. Res. 24, 929–936 (2009).
Google Scholar 
19.Ashman, T. L. & Arceo-Gómez, G. Toward a predictive understanding of the fitness costs of heterospecific pollen receipt and its importance in co-flowering communities. Am. J. Bot. 100(6), 1061–1070 (2013).PubMed 

Google Scholar 
20.Fang, Q. & Huang, S. Q. A directed network analysis of heterospecific pollen transfer in a biodiverse community. Ecology 94, 1176–1185 (2013).PubMed 

Google Scholar 
21.Arceo-Gómez, G. et al. Patterns of among- and within-species variation in heterospecific pollen receipt: The importance of ecological generalization. Am. J. Bot. 103, 396–407 (2016).PubMed 

Google Scholar 
22.Fang, Q., Gao, J., Armbruster, W. S. & Huang, S. Q. Multi-year stigmatic pollen-load sampling reveals temporal stability in interspecific pollination of flowers in a subalpine meadow. Oikos 128, 1739–1747 (2019).
Google Scholar 
23.Bartomeus, I., Bosch, J. & Vila, M. High invasive pollen transfer, yet low deposition on native stigmas in a Carpobrotus-invaded community. Ann. Bot. 102, 417–424 (2008).PubMed 
PubMed Central 

Google Scholar 
24.Lázaro, A., Jakobsson, A. & Totland, Ø. How do pollinator visitation rate and seed set relate to species’ floral traits and community context?. Oecologia 173(3), 881–893 (2013).ADS 
PubMed 

Google Scholar 
25.Matsumoto, T., Takakura, K. I. & Nishida, T. Alien pollen grains interfere with the reproductive success of native congener. Biol. Invasions 12, 1617–1626 (2010).
Google Scholar 
26.Flanagan, R. J., Mitchell, R. J. & Karron, J. D. Effects of multiple competitors for pollination on bumblebee foraging patterns and Mimulus ringens reproductive success. Oikos 120(2), 200–207 (2011).
Google Scholar 
27.Arceo-Gómez, G. & Ashman, T. L. Heterospecific pollen deposition: Does diversity alter the consequences?. New Phytol. 192(3), 738–746 (2011).PubMed 

Google Scholar 
28.Arceo-Gómez, G., Kaczorowski, R. L., Patel, C. & Ashman, T. L. Interactive effects between donor and recipient species mediate fitness costs of heterospecific pollen receipt in a co-flowering community. Oecologia 189, 1041–1047 (2019).ADS 
PubMed 

Google Scholar 
29.Montgomery, B. R. Pollination of Sisyrinchium campestre (Iridaceae) in prairies invaded by the introduced plant Euphorbia ésula (Euphorbiaceae). Am. Midl. Nat. 162, 239–252 (2009).
Google Scholar 
30.Huang, Z. H., Liu, H. L. & Huang, S. Q. Interspecific pollen transfer between two coflowering species was minimized by bumblebee fidelity and differential pollen placement on the bumblebee body. J. Plant Ecol. 8(2), 109–115 (2015).
Google Scholar 
31.Moreira-Hernández, J. I., Terzich, N., Zambrano-Cevallos, R., Oleas, N. H. & Muchhala, N. Differential tolerance to increasing heterospecific pollen deposition in two sympatric species of Burmeistera (Campanulaceae: Lobelioideae). Int. J. Plant Sci. 180, 987–995 (2019).
Google Scholar 
32.Makino, T. T., Ohashi, K. & Sakai, S. How do floral display size and the density of surrounding flowers influence the likelihood of bumble bee revisitation to a plant?. Funct. Ecol. 21, 87–95 (2007).
Google Scholar 
33.Liao, K., Gituru, R. W., Guo, Y. H. & Wang, Q. F. The presence of co-flowering species facilitates reproductive success of Pedicularis monbeigiana (Orobanchaceae) through variation in bumble-bee foraging behaviour. Ann. Bot. 108, 877–884 (2011).PubMed 
PubMed Central 

Google Scholar 
34.Sieber, Y. et al. Do alpine plants facilitate each other’s pollination? Experiments at a small spatial scale. Acta Oecol. 37, 369–374 (2011).ADS 

Google Scholar 
35.Yang, C. F., Wang, Q. F. & Guo, Y. H. Pollination in a patchily distributed lousewort is facilitated by presence of a co-flowering plant due to enhancement of quantity and quality of pollinator visits. Ann. Bot. 112, 1751–1758 (2013).PubMed 
PubMed Central 

Google Scholar 
36.Losapio, G. & Schöb, C. Pollination interactions reveal direct costs and indirect benefits of plant–plant facilitation for ecosystem engineers. J. Plant Ecol. 13, 107–113 (2020).
Google Scholar 
37.Molina-Montenegro, M., Badano, E. & Cavieres, L. Positive interactions among plant species for pollinator service: Assessing the “magnet species” concept with invasive species. Oikos 117, 1833–1839 (2008).
Google Scholar 
38.Arceo-Gómez, G. & Ashman, T. L. Invasion status and phylogenetic relatedness predict cost of heterospecific pollen receipt: Implications for native biodiversity decline. J. Ecol. 104, 1003–1008 (2016).
Google Scholar 
39.Streher, N. S., Bergamo, P. J., Ashman, T. L., Wolowski, M. & Sazima, M. Effect of heterospecific pollen deposition on pollen tube growth depends on the phylogenetic relatedness between donor and recipient. AoB Plants 12, plaa016 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
40.Suárez-Mariño, A., Arceo-Gómez, G., Sosenski, P. & Parra-Tabla, V. Patterns and effects of heterospecific pollen transfer between an invasive and two native plant species: The importance of pollen arrival time to the stigma. Am. J. Bot. 106, 1308–1315 (2019).PubMed 

Google Scholar 
41.Celaya, I. N., Arceo-Gómez, G., Alonso, C. & Parra-Tabla, V. Negative effects of heterospecific pollen receipt vary with abiotic conditions: Ecological and evolutionary implications. Ann. Bot. 116(5), 789–795 (2015).PubMed 
PubMed Central 

Google Scholar 
42.Johnson, A. L. & Ashman, T. L. Consequences of invasion for pollen transfer and pollination revealed in a tropical island ecosystem. New Phytol. 221, 142–154 (2019).PubMed 

Google Scholar 
43.Albor, C., Arceo-Gómez, G. & Parra-Tabla, V. Integrating floral trait and flowering time distribution patterns help reveal a more dynamic nature of co-flowering community assembly processes. J. Ecol. 108, 2221–2231 (2020).
Google Scholar 
44.Brooker, R. W. et al. Facilitation in plant communities: The past, the present, and the future. J. Ecol. 96, 18–34 (2008).MathSciNet 

Google Scholar 
45.He, Q., Bertness, M. D. & Altieri, A. H. Global shifts towards positive species interactions with increasing environmental stress. Ecol. Lett. 16, 695–706 (2013).PubMed 

Google Scholar 
46.Körner, C. Alpine Plant Life: Functional Plant Ecology of High Mountain Ecosystems 2nd edn. (Springer, 2003).
Google Scholar 
47.Butterfield, B. J. et al. Alpine cushion plants inhibit the loss of phylogenetic diversity in severe environments. Ecol. Lett. 16, 478–486 (2013).CAS 
PubMed 

Google Scholar 
48.Cavieres, L. A., Hernández-Fuentes, C., Sierra-Almeida, A. & Kikvidze, Z. Facilitation among plants as an insurance policy for diversity in Alpine communities. Funct. Ecol. 30(1), 52–59 (2016).
Google Scholar 
49.Gavini, S. S., Ezcurra, C. & Aizen, M. A. Patch-level facilitation fosters high-Andean plant diversity at regional scales. J. Veg. Sci. 31, 1135–1145 (2020).
Google Scholar 
50.Valiente-Banuet, A. & Verdú, M. Facilitation can increase the phylogenetic diversity of plant communities. Ecol. Lett. 10, 1029–1036 (2007).PubMed 

Google Scholar 
51.McCormick, M. L., Aslan, C. E., Chaudhry, T. A. & Potter, K. A. Benefits and limitations of isolated floral patches in a pollinator restoration project in Arizona. Restor Ecol. 27, 1282–1290 (2019).
Google Scholar 
52.Vamosi, J. C. et al. Pollination decays in biodiversity hotspots. Proc. Natl. Acad. Sci. USA 10, 956–961 (2006).ADS 

Google Scholar 
53.Parra-Tabla, V. et al. Pollen transfer networks reveal alien species as main heterospecific pollen donors with fitness consequences for natives. J. Ecol. 109, 939–951 (2021).
Google Scholar 
54.Ballantyne, G., Baldock, K. C. R., Rendell, L. & Willmer, P. G. Pollinator importance networks illustrate the crucial value of bees in a highly speciose plant community. Sci. Rep. 7(1), 8389 (2017).ADS 
PubMed 
PubMed Central 

Google Scholar 
55.Johnson, S. D., Peter, C. I., Nilsson, L. A. & Agren, J. Pollination success in a deceptive orchid is enhance by co-occuring magnet plants. Ecology 84, 2919–2927 (2003).
Google Scholar 
56.Ashman, T. L., Alonso, C., Parra-Tabla, V. & Arceo-Gómez, G. Pollen on stigmas as proxies of pollinator competition and facilitation: Complexities, caveats, and future directions. Ann. Bot. 125(7), 1003–1012 (2020).PubMed 
PubMed Central 

Google Scholar 
57.Arroyo, M. T. K., Primack, R. & Armesto, J. Community studies in pollination ecology in the high temperate Andes of central Chile. I. Pollination mechanisms and altitudinal variation. Am. J. Bot. 69(1), 82–97 (1982).
Google Scholar 
58.Arroyo, M. T. K., Armesto, J. J. & Primack, R. B. Community studies in population ecology in the high temperate Andes of central Chile II. Effect of temperature on visitation rates and pollination possibilities. Pl. Syst. Evol. 149, 187–203 (1985).
Google Scholar 
59.Arroyo, M. T. K. & Squeo, F. A. Relationship between plant breeding systems and pollination. In Biological Approaches and Evolutionary Trends in Plants (ed. Kawano, S.) 205–227 (Academic Press, 1990).
Google Scholar 
60.Jakobsson, A., Padrón, B. & Traveset, A. Pollen transfer from invasive Carpobrotus spp. to natives—A study of pollinator behaviour and reproduction success. Biol. Conserv. 141, 136–145 (2008).
Google Scholar 
61.Heinrich, B. Bumblebee foraging and the economics of sociality: How have bumblebees evolved to use a large variety of flowers efficiently? Individual bees have specialized behavioral repertories, and the colony, collectively, can harvest food from many different resources. Am. Sci. 64, 384–395 (1976).ADS 

Google Scholar 
62.Rasmann, S., Alvarez, N. & Pellissier, L. The altitudinal niche-breadth hypothesis in insect–plant interactions. In Annual Plant Reviews (Eds. C. Voelckel, & G. Jander) volume 47. (pp. 339–360). (Wiley-​Blackwell Publishing, Oxford 2014).
Google Scholar 
63.Gegear, R. J. & Laverty, T. M. Flower constancy in bumblebees: A test of the trait variability hypothesis. Anim. Behav. 69(4), 939–949 (2005).
Google Scholar 
64.Iler, A. M. & Goodell, K. Relative floral density of an invasive plant affects pollinator foraging behaviour on a native plant. J. Pollinat. Ecol. 13, 174–183 (2014).
Google Scholar 
65.Dauber, J. et al. Effects of patch size and density on flower visitation and seed set of wild plants: A pan-European approach. J. Ecol. 98, 188–196 (2010).
Google Scholar 
66.Totland, Ø. Pollination in alpine Norway: Flowering phenology, insect visitors, and visitation rates in two plant communities. Canad. J. Bot. 71, 1072–1079 (1993).
Google Scholar 
67.Zhao, Z. G. & Wang, Y. K. Selection by pollinators on floral traits in generalized Trollius ranunculoides (Ranunculaceae) along altitudinal gradients. PLoS ONE 10(2), e0118299 (2015).PubMed 
PubMed Central 

Google Scholar 
68.Hagen, M., Wikelski, M. & Kissling, W. D. Space use of bumblebees (Bombus spp.) revealed by radio-tracking. PLoS ONE 6(5), e19997 (2011).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
69.Hegland, S. J. & Boeke, L. Relationships between the density and diversity of floral resources and flower visitor activity in a temperate grassland community. Ecol. Entomol. 31, 532–538 (2006).
Google Scholar 
70.Lázaro, A., Lundgren, R. & Totland, Ø. Co-flowering neighbors influence the diversity and identity of pollinator groups visiting plant species. Oikos 118, 691–702 (2009).
Google Scholar 
71.Potts, S. G. et al. Nectar resource diversity organises flower-visitor community structure. Entomol. Exp. Appl. 113, 103–107 (2004).
Google Scholar 
72.Hoyle, H. et al. Plant species or flower colour diversity? Identifying the drivers of public and invertebrate response to designed annual meadows. Landsc. Urban. Plan. 180, 103–113 (2018).
Google Scholar 
73.Walker, B. H. Biodiversity and ecological redundancy. Biol. Conserv. 6, 18–23 (1992).
Google Scholar 
74.Arroyo, M. T. K., Pacheco, D. A. & Dudley, L. S. Functional role of long-lived flowers in preventing pollen limitation in a high elevation outcrossing species. AoB Plants 9(6), plx050 (2017).PubMed 
PubMed Central 

Google Scholar 
75.Nuñez, C., Aizen, M. & Ezcurra, C. Species associations and nurse effects in patches of high-Andean vegetation. J. Veg. Sci. 10, 357–364 (1999).
Google Scholar 
76.Ferreyra, M., Clayton, S. & Ezcurra, C. High Mountain of the Patagonian Andes (LOLA, 2020).
Google Scholar 
77.Riveros, M. Biología reproductiva en especies vegetales de dos comunidades de la zona templada del sur de Chile, 40° S. Ph.D. Dissertation, Universidad de Chile, Santiago, Chile (1991).78.Riveros, M., Humaña, A. M. & Lanfranco, D. Actividad de los polinizadores en el Parque Nacional Puyehue, X region, Chile. Medio Ambiente 11, 5–12 (1991).
Google Scholar 
79.Alexander, M. P. A versatile stain for pollen, fungi, yeast and bacteria. Stain Technol. 55, 13–18 (1980).CAS 
PubMed 

Google Scholar 
80.- R Core Development Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org/. Accessed April 2021 (2018).81.- Magnusson, A. et al. glmmTMB: Generalized linear mixed models using template model builder. https://github.com/glmmTM. Accessed April 2021 (2017).82.Kock, N. & Lynn, G. S. Lateral collinearity and misleading results in variance-based SEM: An illustration and recommendations. J. Assoc. Inf. Syst. 13(7), 546–580 (2012).
Google Scholar 
83.Kock, N. Common method bias in PLS-SEM: A full collinearity assessment approach. Int. J. e-Collab. 11(4), 1–10 (2015).
Google Scholar 
84.Bolker, B. M. et al. Generalized linear mixed models: A practical guide for ecology and evolution. Trends Ecol. Evol. 24, 127–135 (2009).PubMed 

Google Scholar 
85.Gelman, A. & Hill, J. Data Analysis Using Regression and Multilevel/Hierarchical Models (Cambridge University Press, 2007).
Google Scholar 
86.Arceo-Gómez, G., Alonso, C., Ashman, T. L. & Parra-Tabla, V. Variation in sampling effort affects the observed richness of plant–plant interactions via heterospecific pollen transfer: Implications for interpretation of pollen transfer networks. Am. J. Bot. 105, 1601–1608 (2018).PubMed 

Google Scholar 
87.Colwell, R. K. & Coddington, J. A. Estimating terrestrial biodiversity through extrapolation. Philos. Trans. R Soc. Lond. B Biol. Sci. 345, 101–118 (1994).ADS 
CAS 
PubMed 

Google Scholar 
88.Baselga, A. Partitioning the turnover and nestedness components of beta diversity. Glob. Ecol. Biogeogr. 19, 134–143 (2010).
Google Scholar 
89.Baselga, A. & Orme, C. D. L. betapart: An R package for the study of beta diversity. Methods Ecol. Evol. 3(5), 808–812 (2012).
Google Scholar  More

1.Huettmann, F. (ed.) Protection of the Three Poles 337 (Springer, 2012).
Google Scholar 
2.Krupnik, I. & Crowell, A. L. Arctic Crashes: People and Animals in the Changing North (Smithsonian Institutional Press, 2020).
Google Scholar 
3.Portenkorupnik, L. A. Birds of Chukchi Peninsula and Wrangel Island, Part 1 424 (Nauka, 1972) (in Russian).
Google Scholar 
4.Vorobiev, K. A. Birds of Yakutia 336 (Nauka, 1963) (in Russian).
Google Scholar 
5.Egorov, O. V. State of population of the waterbirds and other bird species in the Lena Delta and Yana-Indigirka Tundra by the materials of air-record. In Nature of Yakutia and Its Protection 124–127 (Yakutsk .Kn. Izd., 1965) (in Russian).
Google Scholar 
6.Egorov, O. V. & Perfil’ev, V. I. Inventory of the population of the waterbirds from airplane in the North Yakutia. In Methods of Inventory of Population 71–78 (Yakutsk. Kn. Izd., 1970) (in Russian).
Google Scholar 
7.Kishchinskii, A. A. & Flint, V. E. Waterbirds of the Near-Indigirka Tundra. In Resources of the Waterbirds of the USSR, Their Reproduction and Use 62–65 (Mosk. Gos. Univ., 1972) (in Russian).
Google Scholar 
8.Krechmar, A. V., Andreev, A. V. & Kondratyev, A. Y. Bird of Northern Plains 228 (Nauka, 1991) (in Russian).
Google Scholar 
9.Andreev, A. V. Monitoring of the Geese in the North Asia. In Species Diversity and State of Populations of the Waterbirds in the North-East of Asia 5–36 (SVNTs DVO RAN, 1997) (in Russian).
Google Scholar 
10.Krechmar, A. V. & Kondratyev, A. V. Waterfowl in North-East Asia 458 (NESC FEB RAN, 2006).
Google Scholar 
11.Kistchinski, A. A. Waterfowl in north-east Asia. Wildfowl 24, 88–102 (1973).
Google Scholar 
12.Pearce, J. M., Eesler, D. & Degtyarev, A. G. Birds of the Indigirka River Delta, Russia: Historical and biogeographic comparisons. Arctic 51(4), 361–370 (1998).13.Hodges, J. I. & Eldridge, W. D. Aerial surveys of eiders and other waterbirds on the eastern Arctic coast of Russia. Wildfowl 52, 127–142 (2001).
Google Scholar 
14.Hupp, J. W. et al. Moult migration of emperor geese Chen Canagica between Alaska and Russia. Avian Biol. https://doi.org/10.1111/j.0908-8857.2007.03969.x (2007).Article 

Google Scholar 
15.Degtyarev, A. G. Monitoring of the Bewick’s Swan in the tundra zone in Yakutia. Contemp. Probl. Ecol. 3, 90–99. https://doi.org/10.1134/S1995425510010157` (2010).Article 

Google Scholar 
16.Stroud, D. A., Fox, A. D., Urquhart, C. & Francis, I. S. International Single Species Action Plan for the Conservation of the Greenland White-fronted Goose (Anser albifrons flavirostris) AEWA Technical Series No. 45 (AEWA, 2012).
Google Scholar 
17.Fox, A. D. & Leafloor, J. O. A Global Audit of the Status and Trends of Arctic and Northern Hemisphere Goose Populations. (Conservation of Arctic Flora and Fauna International Secretariat). ISBN 978-9935-431-66-0. (2018).18.Humphries, G., Magness, D. R. & Huettmann, F. Machine Learning for Ecology and Sustainable Natural Resource Management (Springer, 2018).Book 

Google Scholar 
19.Carlson, D. A. Lesson in sharing. Nature 469, 293. https://doi.org/10.1038/469293a (2011).ADS 
CAS 
Article 
PubMed 

Google Scholar 
20.Huettmann, F., Artukhin, Y., Gilg, O. & Humphires, G. Predictions of 27 Arctic pelagic seabird distributions using public environmental variables, assessed with colony data: A first digital IPY and GBIF open access synthesis platform. Mar. Biodivers. 41, 141–179. https://doi.org/10.1007/s12526-011-0083-2 (2011).Article 

Google Scholar 
21.Huettmann, F. & Ickert-Bond, S. On Open Access, data mining and plant conservation in the Circumpolar North with an online data example of the Herbarium. (University of Alaska Museum of the North Arctic Science, 2017). http://www.nrcresearchpress.com/toc/as/0/ja (2017).22.Ickert-Bond, S. et al. New Insights on Beringian Plant Distribution Patterns. Alaska Park Science—Volume 12 Issue 1: Science, History, and Alaska’s Changing Landscapes. (U.S. National Park Service, 2018). https://www.nps.gov/articles/aps-v12-i1-c-10.htm.23.Huettmann F (2015) On the Relevance and Moral Impediment of Digital Data Management Data Sharing and Public Open Access and Open Source Code in (Tropical) Research: The Rio Convention Revisited Towards Mega Science and Best Professional Research Practices. In: Huettmann F (eds) Central American Biodiversity: Conservation Ecology and a Sustainable Future. Springer, New York, pp. 391–41824.Zoeckler, C. Chapter 9: Status, Threat, and protection of Arctic Waterbirds. In Protection of the Three Poles (ed. Huettrmann, F.) 203–216 (Springer, 2012).Chapter 

Google Scholar 
25.Jiao, S., Huettmann, F., Guo, Y., Li, X. & Ouyang, Y. Advanced long-term bird banding and climate data mining in spring confirm passerine population declines for the Northeast Chinese-Russian flyway. Glob. Planet. Change 144C, 17–33. https://doi.org/10.1016/j.gloplacha.2016.06.015(2016).26.Zoeckler, C. et al. The winter distribution of the Spoon-billed Sandpiper Calidris pygmaeus. Bird Conserv. Int. 26, 476–489 (2016).Article 

Google Scholar 
27.Huettmann, F., Mi, C. & Guo, Yu. ‘Batteries’ in machine learning: A first experimental assessment of inference for Siberian Crane Breeding Grounds in the Russian High Arctic Based on ‘Shaving’ 74 predictors. In Machine Learning for Ecology and Sustainable Natural Resource Management (eds Humphries, G. et al.) 163–184 (Springer, 2018).Chapter 

Google Scholar 
28.Rogacheva, E. V. The Birds of Central Siberia (Husum Druck-Verlag, 1992).
Google Scholar 
29.Chen, J. et al. Prospects for the sustainability of social-ecological systems (SES) on the Mongolian plateau: five critical issues. Environ. Res. Lett. 13, 123004. https://doi.org/10.1088/1748-9326/aaf27b (2018).ADS 
Article 

Google Scholar 
30.Sethi, S., Goyal, S. P. & Choudhary, A. N. Chapter 12: Poaching, illegal wildlife trade, and bushmeat hunting in India and South Asia. In International Wildlife Management: Conservation Challenges in a changing World (John Hopkins University Press, 2019).
Google Scholar 
31.Huettmann, F. Chapter 6: Effective Poyang Lake Conservation? A local ecology view from downstream involving internationally migratory birds when trying to buffer and manage water from HKH with ‘Modern’ concepts. In Hindu Kush-Himalaya Watersheds Downhill: Landscape Ecology and Conservation Perspectives (eds Regmi, G.R. & Huettmann, F.) 99–112 (Springer, 2020).32.Yong, D. L. et al. The State of Migratory Landbirds in the East Asian Flyway: Distributions, Threats, and Conservation Needs. Front. Ecol. Evol. 9, 613172. https://doi.org/10.3389/fevo.2021.613172 (2021).Article 

Google Scholar 
33.Deng, X. Q. et al. Contrasting trends in two East Asian populations of the Greater White-fronted Goose Anser albifrons. Wildlfowl Special Issue 6, 181–205 (2020).
Google Scholar 
34.Li, C. et al. Population trends and migration routes of the East Asian Bean Goose Anser fabalis middendorffii and A. f. serrirostris. Wildfowl 70(6), 124–156 (2020).
Google Scholar 
35.Shimada, T., Mori, A. & Tajiri, H. Regional variation in long-term population trends for the Greater White-fronted Goose Anser albifrons in Japan. Wildfowl 69, 105–117 (2019).
Google Scholar 
36.Wilson, R. E., Ely, C. R. & Talbot, S. L. Flyway structure in the circumpolar greater white-fronted goose. Ecol. Evol. https://doi.org/10.1002/ece3.4345 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
37.Bocharnikov, V. & Huettmann, F. Wilderness Condition as a Status Indicator of Russian Flora and Fauna: Implications for Future Protection Initiatives. Int. J. Wilderness 25, 26–39 (2019).
Google Scholar 
38.Klein, D. R. & Magomedova, M. Industrial development and wildlife in arctic ecosystems: Can learning from the past lead to a brighter future? In Social and Environmental Impacts in the North (eds Rasmussen, R. O. & Koroleva, N. E.) 5–56 (Kluwer Academic Publishers, 2003).39.Klein, D. R. et al. Management and conservation of wildlife in a changing Arctic environment. Arctic Climate Impact Assessment (ACIA) ACIA Overview Report 597–648 (Cambridge University Press, 2005).
Google Scholar 
40.Banerjee, S. Arctic Voices: Resistance at the Tipping Point (Seven Stories Press, 2012).
Google Scholar 
41.Zuckerberg, B., Huettmann, F. & Friar, J. Proper data management as a for reliable species distribution modeling. In Predictive Species and Habitat Modeling in Landscape Ecology (Drew, C. A. et al.) 45–70 (Springer, 2011).42.Elith, J., Graham, C. & NCEAS Working Group. Novel methods improve prediction of species’ distributions from occurrence data. Ecography 29, 129–151 (2006).Article 

Google Scholar 
43.Elith, J. et al. Presence-only and presence-absence data for comparing species distribution modeling methods. J. Biodivers. Inform. 15, 69–80 (2020).Article 

Google Scholar 
44.Tian, H. et al. Combining modern tracking data and historical records improves understanding of the summer habitats of the Eastern Lesser White-fronted Goose Anser erythropus. Ecol. Evol. https://doi.org/10.1002/ece3.7310 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
45.Ohse, B., Huettmann, F., Ickert-Bond, S. & Juday, G. Modeling the distribution of white spruce (Picea glauca) for Alaska with high accuracy: An open access role-model for predicting tree species in last remaining wilderness areas Polar Biol. 32, 1717–1724 (2009).46.Spiridonov, V. et al. Chapter 8 Toward the new role of marine and coastal protected areas in the Arctic: The Russian case. In Protection of the Three Poles (ed. Huettrmann, F.) 171–201 (Springer, 2012).Chapter 

Google Scholar 
47.Nixon, W., Sinclair, P. H., Eckert, C. D. & Hughes, N. Birds of the Yukon Territory (UBC Press, 2003).
Google Scholar 
48.Booms, T., Huettmann, F. & Schempf, P. Gyrfalcon nest distribution in Alaska based on a predictive GIS model. Polar Biol. 33, 1601–1612 (2009).
Google Scholar 
49.Aycrigg, J. et al. Novel approaches to modeling and mapping terrestrial vertebrate occurrence in the Northwest and Alaska: An evaluation. Northwest Sci. 89, 355–381. https://doi.org/10.3955/046.089.0405 (2015).Article 

Google Scholar 
50.Carboneras, C. & Kirwan, G. M. Greater White-fronted Goose (Anser albifrons). In Handbook of the Birds of the World Alive (eds del Hoyo, J. et al.) (Lynx Edicions, 2013).
Google Scholar 
51.Carboneras, C. & Kirwan, G. M. Bean Goose (Anser fabalis). In Handbook of the Birds of the World Alive (eds del Hoyo, J. et al.) (Lynx Edicions, 2014).
Google Scholar 
52.Hilborn, R. & Mangel, M. The Ecological Detective: Confronting Models with Data (Princeton University Press, 1997).Book 

Google Scholar 
53.Breiman, L. Statistical modeling: The two cultures (with comments and a rejoinder by the author). Stat. Sci. 16, 199–231 (2002).MATH 

Google Scholar 
54.Betts, M. G. et al. Comments on “Methods to account for spatial autocorrelation in the analysis of species distributional data: A review”. Ecography 32, 374–378 (2009).55.Fox, C. H. F. et al. Predictions from machine learning ensembles: Marine bird distribution and density on Canada’s Pacific coast. Mar. Ecol. Prog. Ser. 566, 199–216 (2017).ADS 
Article 

Google Scholar 
56.Fox, A. D. et al. Twenty-five years of population monitoring: The rise and fall of the Greenland White-fronted Goose Anser albifrons flavirostris. In Waterbirds Around the World (eds Boere, G. et al.) 637–639 (The Stationary Office, 2006).
Google Scholar 
57.Dornelas, M. et al. BioTIME: A database of biodiversity time series for the Anthropocene. Glob. Ecol. Biogeogr. https://doi.org/10.1111/geb.12729 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
58.BirdLife International. Anser albifrons. The IUCN Red List of Threatened Species 2016: e.T22679881A85980652. https://doi.org/10.2305/IUCN.UK.2016-3.RLTS.T22679881A85980652.en. (Accessed 28th March 2021) (2016).59.BirdLife International. Anser fabalis. The IUCN Red List of Threatened Species 2018: e.T22679875A132302864. https://doi.org/10.2305/IUCN.UK.2018-2.RLTS.T22679875A132302864.en. (Accessed 28 March 2021) (2018).60.Wang, X. L. et al. Stochastic simulations reveal few green wave surfing populations among spring migrating herbivorous waterfowl. Nat. Commun. 10, 2187 (2019).ADS 
Article 

Google Scholar 
61.Ruokonen, M., Litvin, K. & Aarvak, T. Taxonomy of the bean goose–pink-footed goose. Mol. Phylogenet. Evol. 48, 554–562 (2008).CAS 
Article 

Google Scholar 
62.Kölzsch, A. et al. Flyway connectivity and exchange primarily driven by moult migration in geese. Mov. Biol. 7, 3 (2019).
Google Scholar 
63.Yen, P., Huettmann, F. & Cooke, F. Modelling abundance and distribution of Marbled Murrelets (Brachyramphus marmoratus) using GIS, marine data and advanced multivariate statistics. Ecol. Model. 171, 395–413 (2004).Article 

Google Scholar 
64.Herrick, K. A., Huettmann, F. & Lindgren, M. A. A global model of avian influenza prediction in wild birds: The importance of northern regions. Vet. Res. 44(1), 42. https://doi.org/10.1186/1297-9716-44-42 (2013).Article 
PubMed 
PubMed Central 

Google Scholar 
65.Gulyaeva, M. et al. Data mining and model-predicting a global disease reservoir for low-pathogenic Avian Influenza (AI) in the wider pacific rim using big data sets. Sci. Rep. 10, 1681. https://doi.org/10.1038/s41598-020-73664-2 (2020).CAS 
Article 

Google Scholar 
66.Robold, R. & Huettmann, F. High-Resolution Prediction of American Red Squirrel in Interior Alaska: A role model for conservation using open access data, machine learning, GIS and LIDAR. PEERJ. https://peerj.com/articles/11830/ (2021). More

1.Nathan, R. et al. A movement ecology paradigm for unifying organismal movement research. Proc. Natl. Acad. Sci. 105, 19052–19059 (2008).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
2.Sutherland, W. J. et al. Identification of 100 fundamental ecological questions. J. Ecol. 101, 58–67 (2013).
Google Scholar 
3.Bernstein, R. A. & Gobbel, M. Partitioning of space in communities of ants. J. Anim. Ecol. 48, 931–942 (1979).
Google Scholar 
4.Jenkins, S. H. A size-distance relation in food selection by beavers. Ecology 61, 740–746 (2018).
Google Scholar 
5.Grémillet, D. et al. Offshore diplomacy, or how seabirds mitigate intra-specific competition: A case study based on GPS tracking of Cape gannets from neighbouring colonies. Mar. Ecol. Prog. Ser. 268, 265–279 (2004).ADS 

Google Scholar 
6.Orians, G. H. & Pearson, N. E. On the theory of central place foraging. In Analysis of Ecological Systems (eds Horn, D. J. et al.) 154–177 (The Ohio State University Press, 1979).7.Schoener, T. W. Generality of the size-distance relation in models of optimal feeding. Am. Nat. 114, 902–914 (1979).MathSciNet 

Google Scholar 
8.Brown, M. J. F. & Gordon, D. M. How resources and encounters affect the distribution of foraging activity in a seed-harvesting ant. Behav. Ecol. Sociobiol. 47, 195–203 (2000).
Google Scholar 
9.Dawo, B., Kalko, E. K. V. & Dietz, M. Spatial organization reflects the social organization in Bechstein’s bats. Ann. Zool. Fennici 50, 356–370 (2013).
Google Scholar 
10.Nordstrom, C. A., Battaile, B. C., Cotté, C. & Trites, A. W. Foraging habitats of lactating northern fur seals are structured by thermocline depths and submesoscale fronts in the eastern Bering Sea. Deep Res. Part II Top. Stud. Oceanogr. 88–89, 78–96 (2013).ADS 

Google Scholar 
11.Bolton, M., Conolly, G., Carroll, M., Wakefield, E. D. & Caldow, R. A review of the occurrence of inter-colony segregation of seabird foraging areas and the implications for marine environmental impact assessment. Ibis (London 1859) 161, 241–259 (2019).
Google Scholar 
12.Cairns, D. K. The regulation of seabird colony size: A hinterland model. Am. Nat. 134, 141–146 (1989).
Google Scholar 
13.Wakefield, E. D. et al. Space partitioning without territoriality in gannets. Science (80-.) 341, 68–70 (2013).ADS 
CAS 

Google Scholar 
14.Masello, J. F. et al. Diving seabirds share foraging space and time within and among species. Ecosphere 1, art19 (2010).
Google Scholar 
15.Cecere, J. G. et al. Spatial segregation of home ranges between neighbouring colonies in a diurnal raptor. Sci. Rep. 8, 11762 (2018).CAS 

Google Scholar 
16.Ainley, D. G., Ford, R. G., Brown, E. D., Suryan, R. M. & Irons, D. B. Prey resources, competition, and geographic structure of Kittiwake colonies in Prince William Sound. Ecology 84, 709–723 (2003).
Google Scholar 
17.Ramos, R. et al. Meta-population feeding grounds of cory’s shearwater in the subtropical Atlantic ocean: Implications for the definition of marine protected areas based on tracking studies. Divers. Distrib. 19, 1284–1298 (2013).
Google Scholar 
18.Dean, B. et al. Simultaneous multi-colony tracking of a pelagic seabird reveals cross-colony utilization of a shared foraging area. Mar. Ecol. Prog. Ser. 538, 239–248 (2015).ADS 
CAS 

Google Scholar 
19.Hunt Jr., G. L. et al. Physical processes, prey abundance, and the foraging ecology of seabirds. In Proceedings of the 22nd International Ornithological Congress, Durban (eds Adams, N. J. & Slotow, R. H.) 2040–2056 (BirdLife South Africa, 1999).20.Weimerskirch, H. Are seabirds foraging for unpredictable resources? Deep Res. Part II Top. Stud. Oceanogr. 54, 211–223 (2007).ADS 

Google Scholar 
21.Benoit-Bird, K. J. et al. Prey patch patterns predict habitat use by top marine predators with diverse foraging strategies. PLoS ONE 8, e53348 (2013).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
22.Lydersen, C. et al. The importance of tidewater glaciers for marine mammals and seabirds in Svalbard, Norway. J. Mar. Syst. 129, 452–471 (2014).
Google Scholar 
23.Hamilton, C. D., Lydersen, C., Ims, R. A. & Kovacs, K. M. Coastal habitat use by ringed seals Pusa hispida following a regional sea-ice collapse: Importance of glacial refugia in a changing Arctic. Mar. Ecol. Prog. Ser. 545, 261–277 (2016).ADS 

Google Scholar 
24.Nishizawa, B. et al. Contrasting assemblages of seabirds in the subglacial meltwater plume and oceanic water of Bowdoin Fjord, northwestern Greenland. ICES J. Mar. Sci. 77, 711–720 (2020).
Google Scholar 
25.Grémillet, D. et al. Arctic warming: Nonlinear impacts of sea-ice and glacier melt on seabird foraging. Glob. Change Biol. 21, 1116–1123 (2015).ADS 

Google Scholar 
26.Hamilton, C. D. et al. Contrasting changes in space use induced by climate change in two Arctic marine mammal species. Biol. Lett. 15, 20180834 (2019).PubMed 
PubMed Central 

Google Scholar 
27.Hartley, C. H. & Fisher, J. The marine foods of birds in an inland fjord region in West Spitsbergen: Part 2. Birds. J. Anim. Ecol. 5, 370–389 (1936).
Google Scholar 
28.Everett, A. et al. Subglacial discharge plume behaviour revealed by CTD-instrumented ringed seals. Sci. Rep. 8, 13467 (2018).ADS 
PubMed 
PubMed Central 

Google Scholar 
29.Carroll, D. et al. Modeling turbulent subglacial meltwater plumes: Implications for fjord-scale buoyancy-driven circulation. J. Phys. Oceanogr. 45, 2169–2185 (2015).ADS 

Google Scholar 
30.Urbański, J. A. et al. Subglacial discharges create fluctuating foraging hotspots for seabirds in tidewater glacier bays. Sci. Rep. 7, 43999 (2017).ADS 
PubMed 
PubMed Central 

Google Scholar 
31.Stempniewicz, L. et al. Marine birds and mammals foraging in the rapidly deglaciating Arctic fjord—Numbers, distribution and habitat preferences. Clim. Change 140, 533–548 (2017).ADS 
Article 

Google Scholar 
32.Stempniewicz, L. et al. Advection of Atlantic water masses influences seabird community foraging in a high-Arctic fjord. Prog. Oceanogr. 193, 102549 (2021).
Google Scholar 
33.Dragańska-Deja, K., Błaszczyk, M., Deja, K., Wesławski, J. M. & Rodak, J. Tidewater glaciers as feeding spots for the Black-legged kittiwake (Rissa tridactyla): A citizen science approach. Polish Polar Res. 41, 69–93 (2020).
Google Scholar 
34.Bertrand, P. et al. Feeding at the front line: interannual variation in the use of glacier fronts by foraging black-legged kittiwakes. Mar. Ecol. Prog. Ser. 677, 197–208 (2021).35.Walsh, P. M. et al. Seabird Monitoring Handbook for Britain and Ireland. A Compilation of Methods for Survey and Monitoring of Breeding Seabirds (JNCC/RSPB/ITE/Seabird Group, 1995).
Google Scholar 
36.Anker-Nilssen, T. et al. Key-Site Monitoring in Norway 2018, Including Svalbard and Jan Mayen (SEAPOP, 2020).
Google Scholar 
37.Harris, S. M. et al. Personality predicts foraging site fidelity and trip repeatability in a marine predator. J. Anim. Ecol. 89, 68–79 (2020).Article 
PubMed 

Google Scholar 
38.Coulson, J. C. Sexing black-legged kittiwakes by measurement. Ringing Migr. 24, 233–239 (2009).ADS 

Google Scholar 
39.Paredes, R. et al. Proximity to multiple foraging habitats enhances seabirds’ resilience to local food shortages. Mar. Ecol. Prog. Ser. 471, 253–269 (2012).ADS 

Google Scholar 
40.Christensen-Dalsgaard, S., May, R. & Lorentsen, S. H. Taking a trip to the shelf: Behavioral decisions are mediated by the proximity to foraging habitats in the black-legged kittiwake. Ecol. Evol. 8, 866–878 (2018).PubMed 

Google Scholar 
41.Coulson, J. C. & Macdonald, A. Recent changes in the habits of the Kittiwake. Br. Birds 55, 171–177 (1962).
Google Scholar 
42.R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2020). https://www.R-project.org/.43.Fleming, C. H. & Calabrese, J. M. A new kernel density estimator for accurate home-range and species-range area estimation. Methods Ecol. Evol. 8, 571–579 (2017).
Google Scholar 
44.Fleming, C. H. et al. Rigorous home range estimation with movement data: A new autocorrelated kernel density estimator. Ecology 96, 1182–1188 (2015).CAS 
PubMed 

Google Scholar 
45.Fleming, C. H. & Calabrese, J. M. ctmm: Continuous-time movement modeling. R package version 0.5.10. https://cran.r-project.org/package=ctmm (2020).46.Dong, X., Fleming, C. H., Noonan, M. J. & Calabrese, J. M. ctmmweb: A Shiny web app for the ctmm movement analysis package. https://github.com/ctmm-initiative/ctmmweb. (2018).47.Fleming, C. H., Noonan, M. J., Medici, E. P. & Calabrese, J. M. Overcoming the challenge of small effective sample sizes in home-range estimation. Methods Ecol. Evol. 10, 1679–1689 (2019).
Google Scholar 
48.Noonan, M. J. et al. A comprehensive analysis of autocorrelation and bias in home range estimation. Ecol. Monogr. 89, 1–21 (2019).
Google Scholar 
49.Fleming, C. H. et al. From fine-scale foraging to home ranges: A semivariance approach to identifying movement modes across spatiotemporal scales. Am. Nat. 183, E154–E167 (2014).PubMed 

Google Scholar 
50.Calabrese, J. M., Fleming, C. H. & Gurarie, E. Ctmm: An R package for analyzing animal relocation data as a continuous-time stochastic process. Methods Ecol. Evol. 7, 1124–1132 (2016).
Google Scholar 
51.Burnham, K. P. & Anderson, D. R. Model selection and multimodel inference: A practical information-theoretic approach (Springer, 2002).MATH 

Google Scholar 
52.Lascelles, B. G. et al. Applying global criteria to tracking data to define important areas for marine conservation. Divers. Distrib. 22, 422–431 (2016).
Google Scholar 
53.Beal, M. et al. BirdLifeInternational/track2kba: First Release (Version 0.5.0). Zenodo. https://doi.org/10.5281/zenodo.3823902 (2020).54.Winner, K. et al. Statistical inference for home range overlap. Methods Ecol. Evol. 9, 1679–1691 (2018).
Google Scholar 
55.Fieberg, J. & Kochanny, C. O. Quantifying home-range overlap: The importance of the utilization distribution. J. Wildl. Manag. 69, 1346–1359 (2005).
Google Scholar 
56.Bhattacharyya, A. On a measure of divergence between two multinomial populations. Indian J. Stat. 7, 401–406 (1946).MathSciNet 
MATH 

Google Scholar 
57.Bhattacharyya, A. On a measure of divergence between two statistical populations defined by their probability distribution. Bull. Calcutta Math. Soc. 35, 99–109 (1943).MathSciNet 
MATH 

Google Scholar 
58.Anderson, M. J. & Walsh, D. C. I. PERMANOVA, ANOSIM, and the Mantel test in the face of heterogeneous dispersions: What null hypothesis are you testing? Ecol. Monogr. 83, 557–574 (2013).
Google Scholar 
59.Carneiro, A. P. B. et al. Consistency in migration strategies and habitat preferences of brown skuas over two winters, a decade apart. Mar. Ecol. Prog. Ser. 553, 267–281 (2016).ADS 

Google Scholar 
60.Dehnhard, N. et al. High inter-and intraspecific niche overlap among three sympatrically breeding, closely related seabird species: Generalist foraging as an adaptation to a highly variable environment? J. Anim. Ecol. 89, 104–119 (2020).PubMed 

Google Scholar 
61.Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: A practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B 57, 289–300 (1995).MathSciNet 
MATH 

Google Scholar 
62.Anderson, M. J. Distance-based tests for homogeneity of multivariate dispersions. Biometrics 62, 245–253 (2006).MathSciNet 
MATH 

Google Scholar 
63.Oksanen, J. et al. vegan: Community Ecology Package (2019). R package version 2.5-7. https://CRAN.R-project.org/package=vegan64.Bivand, R. S., Pebesma, E. & Gomez-Rubio, V. Applied Spatial Data Analysis with R 2nd edn. (Springer, 2013).MATH 

Google Scholar 
65.Pebesma, E. & Bivand, R. S. Classes and methods for spatial data in R. R News 5 (2). https://cran.r-project.org/doc/Rnews/ (2005).66.Paredes, R. et al. Foraging responses of black-legged kittiwakes to prolonged food-shortages around colonies on the Bering Sea shelf. PLoS ONE 9, e92520 (2014).ADS 
PubMed 
PubMed Central 

Google Scholar 
67.Hartig, F. Residual diagnostics for hierarchical (multi-level/mixed) regression models. R package version 0.3.3.0. https://CRAN.R-project.org/package=DHARMa (2020).68.Legendre, P., Lapointe, F.-J. & Casgrain, P. Modeling brain evolution from behavior: A permutational regression approach. Evolution 48, 1487–1499 (1994).
Google Scholar 
69.Goslee, S. C. & Urban, D. L. The ecodist package for dissimilarity-based analysis of ecological data. J. Stat. Softw. 22, 1–19 (2007).
Google Scholar 
70.Bolnick, D. I., Yang, L. H., Fordyce, J. A., Davis, J. M. & Svanbäck, R. Measuring individual-level resource specialization. Ecology 83, 2936–2941 (2002).
Google Scholar 
71.Zaccarelli, N., Mancinelli, G. & Bolnick, D. I. RInSp: An R package for the analysis of individual specialisation in resource use. Methods Ecol. Evol. 4, 1018–1023 (2013).
Google Scholar 
72.Lewis, S., Sherratt, T. N., Hamer, K. C. & Wanless, S. Evidence of intra-specific competition for food in a pelagic seabird. Nature 412, 816–819 (2001).ADS 
CAS 
PubMed 

Google Scholar 
73.Bertrand, A. et al. Broad impacts of fine-scale dynamics on seascape structure from zooplankton to seabirds. Nat. Commun. 5, 5239 (2014).ADS 

Google Scholar 
74.Fauchald, P. Spatial interaction between seabirds and prey: Review and synthesis. Mar. Ecol. Prog. Ser. 391, 139–151 (2009).ADS 

Google Scholar 
75.MacArthur, R. H. & Pianka, E. R. On optimal use of a patchy environment. Am. Nat. 100, 603–609 (1966).
Google Scholar 
76.Schoener, T. W. Theory of feeding strategies. Annu. Rev. Ecol. Syst. 2, 369–404 (1971).
Google Scholar 
77.Carroll, D. et al. The impact of glacier geometry on meltwater plume structure and submarine melt in Greenland fjords. Geophys. Res. Lett. 43, 9739–9748 (2016).ADS 

Google Scholar 
78.Halbach, L. et al. Tidewater glaciers and bedrock characteristics control the phytoplankton growth environment in a fjord in the Arctic. Front. Mar. Sci. 6, 254 (2019).
Google Scholar 
79.Pramanik, A. et al. Hydrology and runoff routing of glacierized drainage basins in the Kongsfjord area, northwest Svalbard. Cryosph. Discuss. [preprint]. https://doi.org/10.5194/tc-2020-197.Article 

Google Scholar 
80.Hopwood, M. J. et al. Non-linear response of summertime marine productivity to increased meltwater discharge around Greenland. Nat. Commun. 9, 3256 (2018).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
81.Arimitsu, M. L., Piatt, J. F. & Mueter, F. Influence of glacier runoff on ecosystem structure in Gulf of Alaska fjords. Mar. Ecol. Prog. Ser. 560, 19–40 (2016).ADS 

Google Scholar 
82.Haney, J. Seabird segregation at Gulf Stream frontal eddies. Mar. Ecol. Prog. Ser. 28, 279–285 (1986).ADS 

Google Scholar 
83.Hyrenbach, K. D., Veit, R. R., Weimerskirch, H. & Hunt, G. L. Jr. Seabird associations with mesoscale eddies: The subtropical Indian Ocean. Mar. Ecol. Prog. Ser. 324, 271–279 (2006).ADS 

Google Scholar 
84.Cox, S. L. et al. Seabird diving behaviour reveals the functional significance of shelf-sea fronts as foraging hotspots. R. Soc. Open Sci. 3, 160317 (2016).ADS 
MathSciNet 
CAS 
PubMed 
PubMed Central 

Google Scholar 
85.Schneider, D. C. Seabirds and fronts: A brief overview. Polar Res. 8, 17–21 (1990).CAS 

Google Scholar 
86.Bost, C. A. et al. The importance of oceanographic fronts to marine birds and mammals of the southern oceans. J. Mar. Syst. 78, 363–376 (2009).
Google Scholar 
87.Durazo, R., Harrison, N. M. & Hill, A. E. Seabird observations at a tidal mixing front in the Irish Sea. Estuar. Coast. Shelf Sci. 47, 153–164 (1998).ADS 

Google Scholar 
88.Wakefield, E. D., Phillips, R. A. & Matthiopoulos, J. Quantifying habitat use and preferences of pelagic seabirds using individual movement data: A review. Mar. Ecol. Prog. Ser. 391, 165–182 (2009).ADS 

Google Scholar 
89.How, P. et al. Rapidly changing subglacial hydrological pathways at a tidewater glacier revealed through simultaneous observations of water pressure, supraglacial lakes, meltwater plumes and surface velocities. Cryosphere 11, 2691–2710 (2017).ADS 

Google Scholar 
90.Navarro, J. & González-Solís, J. Environmental determinants of foraging strategies in Cory’s shearwaters Calonectris diomedea. Mar. Ecol. Prog. Ser. 378, 259–267 (2009).ADS 
CAS 

Google Scholar 
91.Irons, D. B. Foraging area fidelity of individual seabirds in relation to tidal cycles and flock feeding. Ecology 79, 647–655 (1998).
Google Scholar 
92.Piatt, J. F. et al. Predictable hotspots and foraging habitat of the endangered short-tailed albatross (Phoebastria albatrus) in the North Pacific: Implications for conservation. Deep Res. Part II Top. Stud. Oceanogr. 53, 387–398 (2006).ADS 

Google Scholar 
93.Ward, P. & Zahavi, A. The importance of certain assemblages of birds as “information-centres” for food-finding. Ibis (London 1859) 115, 517–534 (1973).
Google Scholar 
94.Weimerskirch, H., Bertrand, S., Silva, J., Marques, J. C. & Goya, E. Use of social information in seabirds: Compass rafts indicate the heading of food patches. PLoS ONE 5, e9928 (2010).ADS 
PubMed 
PubMed Central 

Google Scholar 
95.Ainley, D. G. et al. Geographic structure of Adélie penguin populations: Overlap in colony-specific foraging areas. Ecol. Monogr. 74, 159–178 (2004).
Google Scholar 
96.Fretwell, S. D. & Lucas, H. L. J. On territorial behavior and other factors influencing habitat distribution in birds. Acta Biotheor. 19, 16–36 (1970).
Google Scholar  More