Outstanding Challenges in the Transferability of Ecological Models

A 'How to' guide for interpreting parameters in habitat-selection analyses

Habitat‐selection analyses allow researchers to link animals to their environment via habitat‐selection or step‐selection functions, and are commonly used to address questions related to wildlife management and conservation efforts. Habitat‐selection analyses that incorporate movement characteristics, referred to as integrated step‐selection analyses, are particularly appealing because they allow modelling of both movement and habitat‐selection processes.

Despite their popularity, many users struggle with interpreting parameters in habitat‐selection and step‐selection functions. Integrated step‐selection analyses also require several additional steps to translate model parameters into a full‐fledged movement model, and the mathematics supporting this approach can be challenging for many to understand.

Using simple examples, we demonstrate how weighted distribution theory and the inhomogeneous Poisson point process can facilitate parameter interpretation in habitat‐selection analyses. Furthermore, we provide a ‘how to’ guide illustrating the steps required to implement integrated step‐selection analyses using the amt package

By providing clear examples with open‐source code, we hope to make habitat‐selection analyses more understandable and accessible to end users.

Continent-Wide Tree Species Distribution Models May Mislead Regional Management Decisions: A Case Study in the Transboundary Biosphere Reserve Mura-Drava-Danube

The understanding of spatial distribution patterns of native riparian tree species in Europe lacks accurate species distribution models (SDMs), since riparian forest habitats have a limited spatial extent and are strongly related to the associated watercourses, which needs to be represented in the environmental predictors. However, SDMs are urgently needed for adapting forest management to climate change, as well as for conservation and restoration of riparian forest ecosystems. For such an operative use, standard large-scale bioclimatic models alone are too coarse and frequently exclude relevant predictors. In this study, we compare a bioclimatic continent-wide model and a regional model based on climate, soil, and river data for central to south-eastern Europe, targeting seven riparian foundation species—Alnus glutinosa, Fraxinus angustifolia, F. excelsior, Populus nigra, Quercus robur, Ulmus laevis, and U. minor. The results emphasize the high importance of precise occurrence data and environmental predictors. Soil predictors were more important than bioclimatic variables, and river variables were partly of the same importance. In both models, five of the seven species were found to decrease in terms of future occurrence probability within the study area, whereas the results for two species were ambiguous. Nevertheless, both models predicted a dangerous loss of occurrence probability for economically and ecologically important tree species, likely leading to significant effects on forest composition and structure, as well as on provided ecosystem services.

On the Interpretations of Joint Modeling in Community Ecology

Explaining and modeling species communities is more than ever a central goal of ecology. Recently, joint species distribution models (JSDMs), which extend species distribution models (SDMs) by considering correlations among species, have been proposed to improve species community analyses and rare species predictions while potentially inferring species interactions. Here, we illustrate the mathematical links between SDMs and JSDMs and their ecological implications and demonstrate that JSDMs, just like SDMs, cannot separate environmental effects from biotic interactions. We provide a guide to the conditions under which JSDMs are (or are not) preferable to SDMs for species community modeling. More generally, we call for a better uptake and clarification of novel statistical developments in the field of biodiversity modeling.

Where did they not go? Considerations for generating pseudo-absences for telemetry-based habitat models

BACKGROUND

Habitat suitability models give insight into the ecological drivers of species distributions and are increasingly common in management and conservation planning. Telemetry data can be used in habitat models to describe where animals were present, however this requires the use of presence-only modeling approaches or the generation of 'pseudo-absences' to simulate locations where animals did not go. To highlight considerations for generating pseudo-absences for telemetry-based habitat models, we explored how different methods of pseudo-absence generation affect model performance across species' movement strategies, model types, and environments.

METHODS

We built habitat models for marine and terrestrial case studies, Northeast Pacific blue whales (Balaenoptera musculus) and African elephants (Loxodonta africana). We tested four pseudo-absence generation methods commonly used in telemetry-based habitat models: (1) background sampling; (2) sampling within a buffer zone around presence locations; (3) correlated random walks beginning at the tag release location; (4) reverse correlated random walks beginning at the last tag location. Habitat models were built using generalised linear mixed models, generalised additive mixed models, and boosted regression trees.

RESULTS

We found that the separation in environmental niche space between presences and pseudo-absences was the single most important driver of model explanatory power and predictive skill. This result was consistent across marine and terrestrial habitats, two species with vastly different movement syndromes, and three different model types. The best-performing pseudo-absence method depended on which created the greatest environmental separation: background sampling for blue whales and reverse correlated random walks for elephants. However, despite the fact that models with greater environmental separation performed better according to traditional predictive skill metrics, they did not always produce biologically realistic spatial predictions relative to known distributions.

CONCLUSIONS

Habitat model performance may be positively biased in cases where pseudo-absences are sampled from environments that are dissimilar to presences. This emphasizes the need to carefully consider spatial extent of the sampling domain and environmental heterogeneity of pseudo-absence samples when developing habitat models, and highlights the importance of scrutinizing spatial predictions to ensure that habitat models are biologically realistic and fit for modeling objectives.

Improved prediction of Canada lynx distribution through regional model transferability and data efficiency

The application of species distribution models (SDMs) to areas outside of where a model was created allows informed decisions across large spatial scales, yet transferability remains a challenge in ecological modeling. We examined how regional variation in animal-environment relationships influenced model transferability for Canada lynx (Lynx canadensis), with an additional conservation aim of modeling lynx habitat across the northwestern United States. Simultaneously, we explored the effect of sample size from GPS data on SDM model performance and transferability. We used data from three geographically distinct Canada lynx populations in Washington (n = 17 individuals), Montana (n = 66), and Wyoming (n = 10) from 1996 to 2015. We assessed regional variation in lynx-environment relationships between these three populations using principal components analysis (PCA). We used ensemble modeling to develop SDMs for each population and all populations combined and assessed model prediction and transferability for each model scenario using withheld data and an extensive independent dataset (n = 650). Finally, we examined GPS data efficiency by testing models created with sample sizes of 5%-100% of the original datasets. PCA results indicated some differences in environmental characteristics between populations; models created from individual populations showed differential transferability based on the populations' similarity in PCA space. Despite population differences, a single model created from all populations performed as well, or better, than each individual population. Model performance was mostly insensitive to GPS sample size, with a plateau in predictive ability reached at ~30% of the total GPS dataset when initial sample size was large. Based on these results, we generated well-validated spatial predictions of Canada lynx distribution across a large portion of the species' southern range, with precipitation and temperature the primary environmental predictors in the model. We also demonstrated substantial redundancy in our large GPS dataset, with predictive performance insensitive to sample sizes above 30% of the original.

Ecological forecasts reveal limitations of common model selection methods: predicting changes in beaver colony densities

Over the past two decades, there have been numerous calls to make ecology a more predictive science through direct empirical assessments of ecological models and predictions. While the widespread use of model selection using information criteria has pushed ecology toward placing a higher emphasis on prediction, few attempts have been made to validate the ability of information criteria to correctly identify the most parsimonious model with the greatest predictive accuracy. Here, we used an ecological forecasting framework to test the ability of information criteria to accurately predict the relative contribution of density dependence and density-independent factors (forage availability, harvest, weather, wolf [Canis lupus] density) on inter-annual fluctuations in beaver (Castor canadensis) colony densities. We modeled changes in colony densities using a discrete-time Gompertz model, and assessed the performance of four models using information criteria values: density-independent models with (1) and without (2) environmental covariates; and density-dependent models with (3) and without (4) environmental covariates. We then evaluated the forecasting accuracy of each model by withholding the final one-third of observations from each population and compared observed vs. predicted densities. Information criteria and our forecasting accuracy metrics both provided strong evidence of compensatory density dependence in the annual dynamics of beaver colony densities. However, despite strong within-sample performance by the most complex model (density-dependent with covariates) as determined using information criteria, hindcasts of colony densities revealed that the much simpler density-dependent model without covariates performed nearly as well predicting out-of-sample colony densities. The hindcast results indicated that the complex model over-fit our data, suggesting that parameters identified by information criteria as important predictor variables are only marginally valuable for predicting landscape-scale beaver colony dynamics. Our study demonstrates the importance of evaluating ecological models and predictions with long-term data and revealed how a known limitation of information criteria (over-fitting of complex models) can affect our interpretation of ecological dynamics. While incorporating knowledge of the factors that influence animal population dynamics can improve population forecasts, we suggest that comparing forecast performance metrics can likewise improve our knowledge of the factors driving population dynamics.

Legacy effects of fish but not elevation influence lake ecosystem response to environmental change

How communities reorganize during climate change depends on the distribution of diversity within ecosystems and across landscapes. Understanding how environmental and evolutionary history constrain community resilience is critical to predicting shifts in future ecosystem function. The goal of our study was to understand how communities with different histories respond to environmental change with regard to shifts in elevation (temperature, nutrients) and introduced predators. We hypothesized that community responses to the environment would differ in ways consistent with local adaptation and initial trait structure. We transplanted plankton communities from lakes at different elevations with and without fish in the Sierra Nevada Mountains in California to mesocosms at different elevations with and without fish. We examined the relative importance of the historical and experimental environment on functional (size structure, effects on lower trophic levels), community (zooplankton composition, abundance and biomass) and population (individual species abundance and biomass) responses. Communities originating from different elevations produced similar biomass at each elevation despite differences in species composition; that is, the experimental elevation, but not the elevation of origin, had a strong effect on biomass. Conversely, we detected a legacy effect of predators on plankton in the fishless environment. Daphnia pulicaria that historically coexisted with fish reached greater biomass under fishless conditions than those from fishless lakes, resulting in greater zooplankton community biomass and larger average size. Therefore, trait variation among lake populations determined the top-down effects of fish predators. In contrast, phenotypic plasticity and local diversity were sufficient to maintain food web structure in response to changing environmental conditions associated with elevation.

Re-Visiting the Incidence of Environmental Factors on a Pre-Imaginal Population of the Red Gum Lerp Psyllid, Glycaspis brimblecombei Moore

The red gum lerp psyllid, Glycaspis brimblecombei Moore (Hemiptera: Aphalaridae), is an invasive pest of Eucalyptus trees worldwide, responsible for serious damage, including the death of plants. Knowledge about the incidence of climatic factors on the insect development are essential to define useful strategies for controlling this pest. To this aim, G. brimblecombei has been sampled by two different methods from April 2012 to February 2013 in eastern Sicily on Eucalyptus camaldulensis in nine different sites, where the main climatic data (air temperature, relative humidity, and precipitation) have been also registered. The Glycaspis brimblecombei population showed a similar trend in all nine sites, positively correlated only with air temperature, but a negative correlation has emerged with precipitation and relative humidity. The results show the need for a deeper understanding of the role played by other abiotic (such as different concentrations of CO2) and biotic (e.g., the antagonistic action of natural enemies, competition with other pests, etc.) factors. The greater sensitivity, even at low densities of psyllid, of sampling methods based on the random collection of a fixed number of leaves compared to methods based on the collection of infested leaves in a fixed time interval has been also outlined.

Shade trees preserve avian insectivore biodiversity on coffee farms in a warming climate

AIM

Coffee is an important export for many developing countries, with a global annual trade value of $100 billion, but it is threatened by a warming climate. Shade trees may mitigate the effects of climate change through temperature regulation that can aid in coffee growth, slow pest reproduction, and sustain avian insectivore diversity. The impact of shade on bird diversity and microclimate on coffee farms has been studied extensively in the Neotropics, but there is a dearth of research in the Paleotropics.

LOCATION

East Africa.

METHODS

We created current and future regional Maxent models for avian insectivores in East Africa using Worldclim temperature data and observations from the Global Biodiversity Information Database. We then adjusted current and future bioclimatic layers based on mean differences in temperature between shade and sun coffee farms and projected the models using these adjusted layers to predict the impact of shade tree removal on climatic suitability for avian insectivores.

RESULTS

Existing Worldclim temperature layers more closely matched temperatures under shade trees than temperatures in the open. Removal of shade trees, through warmer temperatures alone, would result in reduction of avian insectivore species by over 25%, a loss equivalent to 50 years of climate change under the most optimistic emissions scenario. Under the most extreme climate scenario and removal of shade trees, insectivore richness is projected to decline from a mean of 38 to fewer than 8 avian insectivore species.

MAIN CONCLUSIONS

We found that shade trees on coffee farms already provide important cooler microclimates for avian insectivores. Future temperatures will become a regionally limiting factor for bird distribution in East Africa, which could negatively impact control of coffee pests, but the effect of climate change can be potentially mediated through planting and maintaining shade trees on coffee farms.

Habitat selection patterns are density dependent under the ideal free distribution

Despite being widely used, habitat selection models are rarely reliable and informative when applied across different ecosystems or over time. One possible explanation is that habitat selection is context-dependent due to variation in consumer density and/or resource availability. The goal of this paper is to provide a general theoretical perspective on the contributory mechanisms of consumer and resource density-dependent habitat selection, as well as on our capacity to account for their effects. Towards this goal we revisit the ideal free distribution (IFD), where consumers are assumed to be omniscient, equally competitive and freely moving, and are hence expected to instantaneously distribute themselves across a heterogeneous landscape such that fitness is equalised across the population. Although these assumptions are clearly unrealistic to some degree, the simplicity of the structure in IFD provides a useful theoretical vantage point to help clarify our understanding of more complex spatial processes. Of equal importance, IFD assumptions are compatible with the assumptions underlying common habitat selection models. Here we show how a fitness-maximising space use model, based on IFD, gives rise to resource and consumer density-dependent shifts in consumer distribution, providing a mechanistic explanation for the context-dependent outcomes often reported in habitat selection analysis. Our model suggests that adaptive shifts in consumer distribution patterns would be expected to lead to nonlinear and often non-monotonic patterns of habitat selection. These results indicate that even under the simplest of assumptions about adaptive organismal behaviour, habitat selection strength should critically depend on system-wide characteristics. Clarifying the impact of adaptive behavioural responses may be pivotal in making meaningful ecological inferences about observed patterns of habitat selection and allow reliable transferability of habitat selection predictions across time and space.

Racing against change: understanding dispersal and persistence to improve species' conservation prospects

Climate change is contributing to the widespread redistribution, and increasingly the loss, of species. Geographical range shifts among many species were detected rapidly after predictions of the potential importance of climate change were specified 35 years ago: species are shifting their ranges towards the poles and often to higher elevations in mountainous areas. Early tests of these predictions were largely qualitative, though extraordinarily rapid and broadly based, and statistical tests distinguishing between climate change and other global change drivers provided quantitative evidence that climate change had already begun to cause species’ geographical ranges to shift. I review two mechanisms enabling this process, namely development of approaches for accounting for dispersal that contributes to range expansion, and identification of factors that alter persistence and lead to range loss. Dispersal in the context of range expansion depends on an array of processes, like population growth rates in novel environments, rates of individual species movements to new locations, and how quickly areas of climatically tolerable habitat shift. These factors can be tied together in well-understood mathematical frameworks or modelled statistically, leading to better prediction of extinction risk as climate changes. Yet, species' increasing exposures to novel climate conditions can exceed their tolerances and raise the likelihood of local extinction and consequent range losses. Such losses are the consequence of processes acting on individuals, driven by factors, such as the growing frequency and severity of extreme weather, that contribute local extinction risks for populations and species. Many mechanisms can govern how species respond to climate change, and rapid progress in global change research creates many opportunities to inform policy and improve conservation outcomes in the early stages of the sixth mass extinction.

Bryophytes are predicted to lag behind future climate change despite their high dispersal capacities

The extent to which species can balance out the loss of suitable habitats due to climate warming by shifting their ranges is an area of controversy. Here, we assess whether highly efficient wind-dispersed organisms like bryophytes can keep-up with projected shifts in their areas of suitable climate. Using a hybrid statistical-mechanistic approach accounting for spatial and temporal variations in both climatic and wind conditions, we simulate future migrations across Europe for 40 bryophyte species until 2050. The median ratios between predicted range loss vs expansion by 2050 across species and climate change scenarios range from 1.6 to 3.3 when only shifts in climatic suitability were considered, but increase to 34.7–96.8 when species dispersal abilities are added to our models. This highlights the importance of accounting for dispersal restrictions when projecting future distribution ranges and suggests that even highly dispersive organisms like bryophytes are not equipped to fully track the rates of ongoing climate change in the course of the next decades.

Bryophytes tend to be sensitive to warming, but their high dispersal ability could help them track climate change. Here the authors combine correlative niche models and mechanistic dispersal models for 40 European bryophyte species under RCP4.5 and RCP8.5, finding that most of these species are unlikely to track climate change over the coming decades.

Eco-Epidemiological Uncertainties of Emerging Plant Diseases: The Challenge of Predicting Xylella fastidiosa Dynamics in Novel Environments

In order to prevent and control the emergence of biosecurity threats such as vector-borne diseases of plants, it is vital to understand drivers of entry, establishment, and spatiotemporal spread, as well as the form, timing, and effectiveness of disease management strategies. An inherent challenge for policy in combatting emerging disease is the uncertainty associated with intervention planning in areas not yet affected, based on models and data from current outbreaks. Following the recent high-profile emergence of the bacterium Xylella fastidiosa in a number of European countries, we review the most pertinent epidemiological uncertainties concerning the dynamics of this bacterium in novel environments. To reduce the considerable ecological and socio-economic impacts of these outbreaks, eco-epidemiological research in a broader range of environmental conditions needs to be conducted and used to inform policy to enhance disease risk assessment, and support successful policy-making decisions. By characterizing infection pathways, we can highlight the uncertainties that surround our knowledge of this disease, drawing attention to how these are amplified when trying to predict and manage outbreaks in currently unaffected locations. To help guide future research and decision-making processes, we invited experts in different fields of plant pathology to identify data to prioritize when developing pest risk assessments. Our analysis revealed that epidemiological uncertainty is mainly driven by the large variety of hosts, vectors, and bacterial strains, leading to a range of different epidemiological characteristics further magnified by novel environmental conditions. These results offer new insights on how eco-epidemiological analyses can enhance understanding of plant disease spread and support management recommendations.[Formula: see text] Copyright © 2020 The Author(s). This is an open access article distributed under the CC BY 4.0 International license.

Identifying island safe havens to prevent the extinction of the World's largest lizard from global warming

The Komodo dragon (Varanus komodoensis) is an endangered, island‐endemic species with a naturally restricted distribution. Despite this, no previous studies have attempted to predict the effects of climate change on this iconic species. We used extensive Komodo dragon monitoring data, climate, and sea‐level change projections to build spatially explicit demographic models for the Komodo dragon. These models project the species’ future range and abundance under multiple climate change scenarios. We ran over one million model simulations with varying model parameters, enabling us to incorporate uncertainty introduced from three main sources: (a) structure of global climate models, (b) choice of greenhouse gas emission trajectories, and (c) estimates of Komodo dragon demographic parameters. Our models predict a reduction in range‐wide Komodo dragon habitat of 8%–87% by 2050, leading to a decrease in habitat patch occupancy of 25%–97% and declines of 27%–99% in abundance across the species' range. We show that the risk of extirpation on the two largest protected islands in Komodo National Park (Rinca and Komodo) was lower than other island populations, providing important safe havens for Komodo dragons under global warming. Given the severity and rate of the predicted changes to Komodo dragon habitat patch occupancy (a proxy for area of occupancy) and abundance, urgent conservation actions are required to avoid risk of extinction. These should, as a priority, be focused on managing habitat on the islands of Komodo and Rinca, reflecting these islands’ status as important refuges for the species in a warming world. Variability in our model projections highlights the importance of accounting for uncertainties in demographic and environmental parameters, structural assumptions of global climate models, and greenhouse gas emission scenarios when simulating species metapopulation dynamics under climate change.

Towards transferability in fish migration models: A generic operational tool for predicting silver eel migration in rivers

In the global context of river fragmentation, predicting fish migration is urgent to implement management actions aimed at protecting and promoting the free movement of diadromous fish. However, large-scale applicability of conservation measures requires transferable models that enable prediction of migration even in data-poor regions. Here, we surveyed 12 contrasted European river sites to predict the activity peaks of silver eels (Anguilla anguilla) during river migration towards spawning areas through an ensemble modelling approach. Site-specific Boosted Regression Tree (BRT) models were adjusted using standardized hydrological variables to predict migration probability, which were aggregated in consensus predictions. Results of independent cross-validations demonstrated that silver eel migration runs were accurately predicted in response to changes in river discharge. Transferability and predictive performance were improved by considering catchment-size dissimilarity between river sites (85 to 109,930 km²) when combining the site-specific predictions. Nevertheless, we provided two examples for which the effects of human actions on flow conditions were so high that they prevented reliable predictions of migration runs. Further contributions should thus take advantage of the flexibility of our approach for updating model collection with new sites to extend the predictive performance under a larger range of ecological conditions. Our transferable hydrological-based modelling framework offers an opportunity to implement large-scale management strategies for eel conservation, even in rivers where eel monitoring data lack. The BRT models and prediction functions were compiled in an R package named 'silvRpeak' to facilitate operational implementation by end-user managers, which can determine when mitigation measures should be implemented to improve river continuity (e.g. turbine shutdown and sluice gate opening) and balance their economic activity towards eel conservation. The only input required is discharge records that are widely available across European hydrological stations.

A guide to ecosystem models and their environmental applications

Applied ecology has traditionally approached management problems through a simplified, single-species lens. Repeated failures of single-species management have led us to a new paradigm - managing at the ecosystem level. Ecosystem management involves a complex array of interacting organisms, processes and scientific disciplines. Accounting for interactions, feedback loops and dependencies between ecosystem components is therefore fundamental to understanding and managing ecosystems. We provide an overview of the main types of ecosystem models and their uses, and discuss challenges related to modelling complex ecological systems. Existing modelling approaches typically attempt to do one or more of the following: describe and disentangle ecosystem components and interactions; make predictions about future ecosystem states; and inform decision making by comparing alternative strategies and identifying important uncertainties. Modelling ecosystems is challenging, particularly when balancing the desire to represent many components of an ecosystem with the limitations of available data and the modelling objective. Explicitly considering different forms of uncertainty is therefore a primary concern. We provide some recommended strategies (such as ensemble ecosystem models and multi-model approaches) to aid the explicit consideration of uncertainty while also meeting the challenges of modelling ecosystems.

Species distribution models have limited spatial transferability for invasive species

The reliability of transferring species distribution models (SDMs) to new ranges and future climates has been widely debated. Biological invasions offer the unique opportunity to evaluate model transferability, as distribution data between species' native and introduced ranges are geographically independent of each other. Here, we performed the first global quantitative synthesis of the spatial transferability of SDMs for 235 invasive species and assessed the association of model transferability with the focal invader, model choice and parameterisation. We found that SDMs had limited spatial transferability overall. However, model transferability was higher for terrestrial endotherms, species introduced from or to the Southern Hemisphere, and species introduced more recently. Model transferability was also positively associated with the number of presences for model calibration and evaluation, respectively, but negatively with the number of predictors. These findings highlight the importance of considering the characteristics of the focal invader, environment and modelling in the application and assessment of SDMs.

Climate oscillation and the invasion of alien species influence the oceanic distribution of seabirds

Spatial and temporal distribution of seabird transiting and foraging at sea is an important consideration for marine conservation planning. Using at-sea observations of seabirds (n = 317), collected during the breeding season from 2012 to 2016, we built boosted regression tree (BRT) models to identify relationships between numerically dominant seabird species (red-footed booby, brown noddy, white tern, and wedge-tailed shearwater), geomorphology, oceanographic variability, and climate oscillation in the Chagos Archipelago. We documented positive relationships between red-footed booby and wedge-tailed shearwater abundance with the strength in the Indian Ocean Dipole, as represented by the Dipole Mode Index (6.7% and 23.7% contribution, respectively). The abundance of red-footed boobies, brown noddies, and white terns declined abruptly with greater distance to island (17.6%, 34.1%, and 41.1% contribution, respectively). We further quantified the effects of proximity to rat-free and rat-invaded islands on seabird distribution at sea and identified breaking point distribution thresholds. We detected areas of increased abundance at sea and habitat use-age under a scenario where rats are eradicated from invaded nearby islands and recolonized by seabirds. Following rat eradication, abundance at sea of red-footed booby, brown noddy, and white terns increased by 14%, 17%, and 3%, respectively, with no important increase detected for shearwaters. Our results have implication for seabird conservation and island restoration. Climate oscillations may cause shifts in seabird distribution, possibly through changes in regional productivity and prey distribution. Invasive species eradications and subsequent island recolonization can lead to greater access for seabirds to areas at sea, due to increased foraging or transiting through, potentially leading to distribution gains and increased competition. Our approach predicting distribution after successful eradications enables anticipatory threat mitigation in these areas, minimizing competition between colonies and thereby maximizing the risk of success and the conservation impact of eradication programs.

Best environmental predictors of breeding phenology differ with elevation in a common woodland bird species

Temperatures in mountain areas are increasing at a higher rate than the Northern Hemisphere land average, but how fauna may respond, in particular in terms of phenology, remains poorly understood. The aim of this study was to assess how elevation could modify the relationships between climate variability (air temperature and snow melt-out date), the timing of plant phenology and egg-laying date of the coal tit (Periparus ater). We collected 9 years (2011-2019) of data on egg-laying date, spring air temperature, snow melt-out date, and larch budburst date at two elevations (~1,300 m and ~1,900 m asl) on a slope located in the Mont-Blanc Massif in the French Alps. We found that at low elevation, larch budburst date had a direct influence on egg-laying date, while at high-altitude snow melt-out date was the limiting factor. At both elevations, air temperature had a similar effect on egg-laying date, but was a poorer predictor than larch budburst or snowmelt date. Our results shed light on proximate drivers of breeding phenology responses to interannual climate variability in mountain areas and suggest that factors directly influencing species phenology vary at different elevations. Predicting the future responses of species in a climate change context will require testing the transferability of models and accounting for nonstationary relationships between environmental predictors and the timing of phenological events.

Model-Assisted Bird Monitoring Based on Remotely Sensed Ecosystem Functioning and Atlas Data

Urgent action needs to be taken to halt global biodiversity crisis. To be effective in the implementation of such action, managers and policy-makers need updated information on the status and trends of biodiversity. Here, we test the ability of remotely sensed ecosystem functioning attributes (EFAs) to predict the distribution of 73 bird species with different life-history traits. We run ensemble species distribution models (SDMs) trained with bird atlas data and 12 EFAs describing different dimensions of carbon cycle and surface energy balance. Our ensemble SDMs—exclusively based on EFAs—hold a high predictive capacity across 71 target species (up to 0.94 and 0.79 of Area Under the ROC curve and true skill statistic (TSS)). Our results showed the life-history traits did not significantly affect SDM performance. Overall, minimum Enhanced Vegetation Index (EVI) and maximum Albedo values (descriptors of primary productivity and energy balance) were the most important predictors across our bird community. Our approach leverages the existing atlas data and provides an alternative method to monitor inter-annual bird habitat dynamics from space in the absence of long-term biodiversity monitoring schemes. This study illustrates the great potential that satellite remote sensing can contribute to the Aichi Biodiversity Targets and to the Essential Biodiversity Variables framework (EBV class “Species distribution”).

Origin of climatic data can determine the transferability of species distribution models

Methodological research on species distribution modelling (SDM) has so far largely focused on the choice of appropriate modelling algorithms and variable selection approaches, but the consequences of choosing amongst different sources of environmental data has scarcely been investigated. Bioclimatic variables are commonly used as predictors in SDMs. Currently, several online databases offer the same sets of bioclimatic variables, but they differ in underlying source of raw data and method of data processing (extrapolation and downscaling). In this paper, we asked whether predictive performance and spatial transferability of SDMs are affected by the choice of two different bioclimatic databases viz. WorldClim 2 and Chelsa 1.2. We used presence-absence data of the invasive plant Ageratina adenophora from the Western Himalaya for training SDMs and a set of independently-collected presence-only datasets from the Central and Eastern Himalaya to evaluate the transferability of the SDMs beyond the training range. We found that the performance of SDMs was, to a large degree, affected by the choice of the climatic dataset. Models calibrated on Chelsa 1.2 outperformed WorldClim 2 in terms of internal evaluation on the calibration dataset. However, when the model was transferred beyond the calibration range to the Central and Eastern Himalaya, models based on WorldClim 2 performed substantially better. We recommend that, in addition to the choice of predictor variables, the choice of predictor datasets with these variables should not be based merely on subjective decision whenever several options are available. Instead, such decisions should be based on robust evaluation of the most appropriate dataset for a given geographic region and species being modelled. Moreover, decisions could also depend on the objective of the study, i.e. projecting within the calibration range or beyond. Therefore, a quantitative evaluation of predictor datasets from alternative sources should be routinely performed as an integral part of the modelling procedure.

Improving prediction of rare species' distribution from community data

Species distribution models (SDMs) have been increasingly used to predict the geographic distribution of a wide range of organisms; however, relatively fewer research efforts have concentrated on rare species despite their critical roles in biological conservation. The present study tested whether community data may improve modelling rare species by sharing information among common and rare ones. We chose six SDMs that treat community data in different ways, including two traditional single-species models (random forest and artificial neural network) and four joint species distribution models that incorporate species associations implicitly (multivariate random forest and multi-response artificial neural network) or explicitly (hierarchical modelling of species communities and generalized joint attribute model). In addition, we evaluated two approaches of data arrangement, species filtering and conditional prediction, to enhance the selected models. The model predictions were tested using cross validation based on empirical data collected from marine fisheries surveys, and the effects of community data were evaluated by comparing models for six selected rare species. The results demonstrated that the community data improved the predictions of rare species' distributions to certain extent but might also be unhelpful in some cases. The rare species could be appropriately predicted in terms of occurrence, whereas their abundance tended to be underestimated by most models. Species filtering and conditional predictions substantially benefited the predictive performances of multiple- and single-species models, respectively. We conclude that both the modelling algorithms and community data need to be carefully selected in order to deliver improvement in modelling rare species. The study highlights the opportunity and challenges to improve prediction of rare species' distribution by making the most of community data.

Hurricanes overcome migration lag and shape intraspecific genetic variation beyond a poleward mangrove range limit

Expansion of many tree species lags behind climate change projections. Extreme storms can rapidly overcome this lag, especially for coastal species, but how will storm-driven expansion shape intraspecific genetic variation? Do storms provide recruits only from the nearest sources, or from more distant sources? Answers to these questions have ecological and evolutionary implications, but empirical evidence is absent from the literature. In 2017, Hurricane Irma provided an opportunity to address this knowledge gap at the northern range limit of the neotropical black mangrove (Avicennia germinans) on the Atlantic coast of Florida, USA. We observed massive post-hurricane increases in beach-stranded A. germinans propagules at, and past, this species' present day range margin when compared to a previously surveyed nonhurricane year. Yet, propagule dispersal does not guarantee subsequent establishment and reproductive success (i.e., effective dispersal). We also evaluated prior effective dispersal along this coastline with isolated A. germinans trees identified beyond the most northern established population. We used 12 nuclear microsatellite loci to genotype 896 hurricane-driven drift propagules from nine sites and 10 isolated trees from four sites, determined their sources of origin, and estimated dispersal distances. Almost all drift propagules and all isolated trees came from the nearest sources. This research suggests that hurricanes are a prerequisite for poleward range expansion of a coastal tree species and that storms can shape the expanding gene pool by providing almost exclusively range-margin genotypes. These insights and empirical estimates of hurricane-driven dispersal distances should improve our ability to forecast distributional shifts of coastal species.

Including Indigenous and local knowledge in climate research. An assessment of the opinion of Spanish climate change researchers

Researchers have documented that observations of climate change impacts reported by Indigenous Peoples and Local Communities coincide with scientific measurements of such impacts. However, insights from Indigenous and Local Knowledge are not yet completely included in international climate change research and policy fora. In this article, we compare observations of climate change impacts detected by Indigenous Peoples and Local Communities from around the world and collected through a literature review (n=198 case studies), with climate scientists' opinions on the relevance of such information for climate change research. Scientists' opinions were collected through a web survey among climate change researchers from universities and research centres in Spain (n=191). In the survey, we asked about the need to collect local level data regarding 68 different groups of indicators of climate change impacts to improve the current knowledge, and about the feasibility of using Indigenous and local knowledge in climate change studies. Results show consensus on the need to continue collecting local level data from all groups of indicators to get a better understanding of climate change impacts, particularly on impacts on the biological system. However, while scientists of our study considered that Indigenous and local knowledge could mostly contribute to detect climate change impacts on the biological and socioeconomic systems, the literature review shows that information on impacts on these systems is rarely collected; researchers instead have mostly documented the impacts on the climatic and physical systems reported by Indigenous and local knowledge.

Predicting population change from models based on habitat availability and utilization

The need to understand the impacts of land management for conservation, agriculture and disease prevention are driving demand for new predictive ecology approaches that can reliably forecast future changes in population size. Currently, although the link between habitat composition and animal population dynamics is undisputed, its function has not been quantified in a way that enables accurate prediction of population change in nature. Here, using 12 house sparrow colonies as a proof-of-concept, we apply recent theoretical advances to predict population growth or decline from detailed data on habitat composition and habitat selection. We show, for the first time, that statistical population models using derived covariates constructed from parametric descriptions of habitat composition and habitat selection can explain an impressive 92% of observed population variation. More importantly, they provide excellent predictive power under cross-validation, anticipating 81% of variability in population change. These models may be embedded in readily available generalized linear modelling frameworks, allowing their rapid application to field systems. Furthermore, we use optimization on our sample of sparrow colonies to demonstrate how such models, linking populations to their habitats, permit the design of practical and environmentally sound habitat manipulations for managing populations.

Population response modeling and habitat suitability of Cobitis choii fish species in South Korea for climate change adaptation

Endangered species ecosystems require appropriate monitoring for assessing population growth related to the emerging pollutants in their habitat conditions. The response of population growth of Cobitis choii, an endangered fish species, under the exposure to emerging pollutants present in the Geum River Basin of South Korea was studied. Toxicity models of concentration addition (CA), independent action (IA), and concentration addition-independent action (CAIA) were implemented utilizing the concentration of a set of 25 chemicals recorded in the study area. Thus, a population-level response analysis was developed based on the abundance of Cobitis choii for period 2011-2015. The results were compared showing that the CA and IA models were the most conservative approaches for the prediction of growth rate. Further, a standard abnormality index (SAI) and habitat suitability (HS) indicators based on the climate, habitat, and abundance data were presented to completely analyze the population growth of the species. Suitability of the species growth was most probable for year 2015 for the variables of air temperature and land surface temperature. A spatial analysis was complementarily presented to visualize the correlation of variables for the best suitability of the species growth. This study presents a methodology for the analysis of the ecosystem's suitability for Cobitis choii growth and its assessment of the chemicals present in Geum River stream.

Relationships between population densities and niche-centroid distances in North American birds

Correlational ecological niche models have seen intensive use and exploration as a means of estimating the limits of actual and potential geographic distributions of species, yet their application to explaining geographic abundance patterns has been debated. We developed a detailed test of this latter possibility based on the North American Breeding Bird Survey. Correlations between abundances and niche-centroid distances were mostly negative, as per expectations of niche theory and the abundant niche-centre relationship. The negative relationships were not distributed randomly among species: terrestrial, non-migratory, small-bodied, small-niche-breadth and restricted-range species had the strongest negative associations. Distances to niche centroids as estimated from correlational analyses of presence-only data thus offer a unique means by which to infer geographic abundance patterns, which otherwise are enormously difficult to characterise.

The Nutritional Ecology of Marine Apex Predators

Apex predators play pivotal roles in marine ecosystems, mediated principally through diet and nutrition. Yet, compared with terrestrial animals, the nutritional ecology of marine predators is poorly understood. One reason is that the field has adhered to an approach that evaluates diet principally in terms of energy gain. Studies in terrestrial systems, by contrast, increasingly adopt a multidimensional approach, the nutritional geometry framework, that distinguishes specific nutrients and calories. We provide evidence that a nutritional approach is likewise relevant to marine apex predators, then demonstrate how nutritional geometry can characterize the nutrient and energy content of marine prey. Next, we show how this framework can be used to reconceptualize ecological interactions via the ecological niche concept, and close with a consideration of its application to problems in marine predator research.

Ensemble modeling of the potential distribution of the whale shark in the Atlantic Ocean

The whale shark (Rhincodon typus) is an endangered marine fish species which can be adversely affected by the fishing activities of the industrial purse seine fleet targeting tropical tuna. Tuna tend to aggregate around all types of floating objects, including whale sharks. We analyzed and modeled the spatial distribution and environmental preferences of whale sharks based on the presence and absence data from fishing observations in the Atlantic Ocean. We used a thorough multialgorithm analysis, based on a new presence-absence dataset, and endeavored to follow the most recent recommendations on best practices in species distribution modeling. First, we selected a subset of relevant variables using a generalized linear model that addressed multicollinearity, statistical errors, and information criteria. We then used the selected variables to build a model ensemble including 19 different algorithms. After eliminating models with insufficient performance, we assessed the potential distribution of whale sharks using the mean of the predictions of the selected models. We also assessed the variance among the predictions of different algorithms, in order to identify areas with the highest model consensus. The results show that several coastal regions and warm shallow currents, such as the Gulf Stream and the Canary and Benguela currents, are the most suitable areas for whale sharks under current environmental conditions. Future environmental projections for the Atlantic Ocean suggest that some of the suitable regions will shift northward, but current concentration areas will continue to be suitable for whale shark, although with less productivity, which could have negative consequences for conservation of the species. We discuss the implications of these predictions for the conservation and management of this charismatic marine species.

Assessment of a large number of empirical plant species niche models by elicitation of knowledge from two national experts

Quantitative models play an increasing role in exploring the impact of global change on biodiversity. To win credibility and trust, they need validating. We show how expert knowledge can be used to assess a large number of empirical species niche models constructed for the British vascular plant and bryophyte flora. Key outcomes were (a) scored assessments of each modeled species and niche axis combination, (b) guidance on models needing further development, (c) exploration of the trade-off between presenting more complex model summaries, which could lead to more thorough validation, versus the longer time these take to evaluate, (d) quantification of the internal consistency of expert opinion based on comparison of assessment scores made on a random subset of models evaluated by both experts. Overall, the experts assessed 39% of species and niche axis combinations to be "poor" and 61% to show a degree of reliability split between "moderate" (30%), "good" (25%), and "excellent" (6%). The two experts agreed in only 43% of cases, reaching greater consensus about poorer models and disagreeing most about models rated as better by either expert. This low agreement rate suggests that a greater number of experts is required to produce reliable assessments and to more fully understand the reasons underlying lack of consensus. While area under curve (AUC) statistics showed generally very good ability of the models to predict random hold-out samples of the data, there was no correspondence between these and the scores given by the experts and no apparent correlation between AUC and species prevalence. Crowd-sourcing further assessments by allowing web-based access to model fits is an obvious next step. To this end, we developed an online application for inspecting and evaluating the fit of each niche surface to its training data.

Spatial resilience of the Great Barrier Reef under cumulative disturbance impacts

In the face of increasing cumulative effects from human and natural disturbances, sustaining coral reefs will require a deeper understanding of the drivers of coral resilience in space and time. Here we develop a high-resolution, spatially explicit model of coral dynamics on Australia's Great Barrier Reef (GBR). Our model accounts for biological, ecological and environmental processes, as well as spatial variation in water quality and the cumulative effects of coral diseases, bleaching, outbreaks of crown-of-thorns starfish (Acanthaster cf. solaris), and tropical cyclones. Our projections reconstruct coral cover trajectories between 1996 and 2017 over a total reef area of 14,780 km² , predicting a mean annual coral loss of -0.67%/year mostly due to the impact of cyclones, followed by starfish outbreaks and coral bleaching. Coral growth rate was the highest for outer shelf coral communities characterized by digitate and tabulate Acropora spp. and exposed to low seasonal variations in salinity and sea surface temperature, and the lowest for inner-shelf communities exposed to reduced water quality. We show that coral resilience (defined as the net effect of resistance and recovery following disturbance) was negatively related to the frequency of river plume conditions, and to reef accessibility to a lesser extent. Surprisingly, reef resilience was substantially lower within no-take marine protected areas, however this difference was mostly driven by the effect of water quality. Our model provides a new validated, spatially explicit platform for identifying the reefs that face the greatest risk of biodiversity loss, and those that have the highest chances to persist under increasing disturbance regimes.