Showing papers in "Ecography in 2019"

V.PhyloMaker: an R package that can generate very large phylogenies for vascular plants

Abstract: We present V.PhyloMaker, a freely available package for R designed to generate phylogenies for vascular plants. The mega‐tree implemented in V.PhyloMaker (i.e. GBOTB.extended.tre), which was derived from two recently published mega‐trees and includes 74 533 species and all families of extant vascular plants, is the largest dated phylogeny for vascular plants. V.PhyloMaker can generate phylogenies for very large species lists (the largest species list that we tested included 314 686 species). V.PhyloMaker generates phylogenies at a fast speed, much faster than other phylogeny‐generating packages. Our tests of V.PhyloMaker show that generating a phylogeny for 60 000 species requires less than six hours. V.PhyloMaker includes an approach to attach genera or species to their close relatives in a phylogeny. We provide a simple example in this paper to show how to use V.PhyloMaker to generate phylogenies.

landscapemetrics: an open‐source R tool to calculate landscape metrics

Abstract: Quantifying landscape characteristics and linking them to ecological processes is one of the central goals of landscape ecology. Landscape metrics are a widely used tool for the analysis of patch‐based, discrete land‐cover classes. Existing software to calculate landscape metrics has several constraints, such as being limited to a single platform, not being open‐source or involving a complicated integration into large workflows. We present landscapemetrics, an open‐source R package that overcomes many constraints of existing landscape metric software. The package includes an extensive collection of commonly used landscape metrics in a tidy workflow. To facilitate the integration into large workflows, landscapemetrics is based on a well‐established spatial framework in R. This allows pre‐processing of land‐cover maps or further statistical analysis without importing and exporting the data from and to different software environments. Additionally, the package provides many utility functions to visualize, extract, and sample landscape metrics. Lastly, we provide building‐blocks to motivate the development and integration of new metrics in the future. We demonstrate the usage and advantages of landscapemetrics by analysing the influence of different sampling schemes on the estimation of landscape metrics. In so doing, we demonstrate the many advantages of the package, especially its easy integration into large workflows. These new developments should help with the integration of landscape analysis in ecological research, given that ecologists are increasingly using R for the statistical analysis, modelling and visualization of spatial data.

Microclimatic buffering in forests of the future: the role of local water balance

Abstract: Forest canopies buffer climate extremes and promote microclimates that may function as refugia for understory species under changing climate. However, the biophysical conditions that promote and maintain microclimatic buffering and its stability through time are largely unresolved. We posited that forest microclimatic buffering is sensitive to local water balance and canopy cover, and we measured this effect during the growing season across a climate gradient in forests of the northwestern United States (US). We found that forest canopies buffer extremes of maximum temperature and vapor pressure deficit (VPD), with biologically meaningful effect sizes. For example, during the growing season, maximum temperature and VPD under at least 50% forest canopy were 5.3°C and 1.1 kPa lower on average, respectively, compared to areas without canopy cover. Canopy buffering of temperature and vapor pressure deficit was greater at higher levels of canopy cover, and varied with water balance, implying that buffering effects are subject to changes in local hydrology. We project changes in the water balance for the mid‐21st century and predict how such changes may impact the ability of western US forests to buffer climate extremes. Our results suggest that some forests will lose their capacity to buffer climate extremes as sites become increasingly water limited. Changes in water balance combined with accelerating canopy losses due to increases in the frequency and severity of disturbance will create potentially non‐linear changes in the microclimate conditions of western US forests.

Incorporating microclimate into species distribution models

Abstract: Species distribution models (SDMs) have rapidly evolved into one of the most widely used tools to answer a broad range of ecological questions, from the effects of climate change to challenges for species management. Current SDMs and their predictions under anthropogenic climate change are, however, often based on free‐air or synoptic temperature conditions with a coarse resolution, and thus fail to capture apparent temperature (cf. microclimate) experienced by living organisms within their habitats. Yet microclimate operates as soon as a habitat can be characterized by a vertical component (e.g. forests, mountains, or cities) or by horizontal variation in surface cover. The mismatch between how we usually express climate (cf. coarse‐grained free‐air conditions) and the apparent microclimatic conditions that living organisms experience has only recently been acknowledged in SDMs, yet several studies have already made considerable progress in tackling this problem from different angles. In this review, we summarize the currently available methods to obtain meaningful microclimatic data for use in distribution modelling. We discuss the issue of extent and resolution, and propose an integrated framework using a selection of appropriately‐placed sensors in combination with both the detailed measurements of the habitat 3D structure, for example derived from digital elevation models or airborne laser scanning, and the long‐term records of free‐air conditions from weather stations. As such, we can obtain microclimatic data with a relevant spatiotemporal resolution and extent to dynamically model current and future species distributions.

Finding answers in the dark: caves as models in ecology fifty years after Poulson and White

Abstract: The use of semi‐isolated habitats such as oceanic islands, lakes and mountain summits as model systems has played a crucial role in the development of evolutionary and ecological theory. Soon after the discovery of life in caves, different pioneering authors similarly recognized the great potential of these peculiar habitats as biological model systems. In their 1969 paper in Science, ‘The cave environment’, Poulson and White discussed how caves can be used as natural laboratories in which to study the underlying principles governing the dynamics of more complex environments. Together with other seminal syntheses published at the time, this work contributed to establishing the conceptual foundation for expanding the scope and relevance of cave‐based studies. Fifty years after, the aim of this review is to show why and how caves and other subterranean habitats can be used as eco‐evolutionary laboratories. Recent advances and directions in different areas are provided, encompassing community ecology, trophic‐webs and ecological networks, conservation biology, macroecology and climate change biology. Special emphasis is given to discuss how caves are only part of the extended network of fissures and cracks that permeate most substrates and, thus, their ecological role as habitat islands is critically discussed. Numerous studies have quantified the relative contribution of abiotic, biotic and historical factors in driving species distributions and community turnovers in space and time, from local to regional scales. Conversely, knowledge of macroecological patterns of subterranean organisms at a global scale remains largely elusive, due to major geographical and taxonomical biases. Also, knowledge regarding subterranean trophic webs and the effect of anthropogenic climate change on deep subterranean ecosystems is still limited. In these research fields, the extensive use of novel molecular and statistical tools may hold promise for quickly producing relevant information not accessible hitherto.

Eco‐evolution on the edge during climate change

Abstract: We urgently need to predict species responses to climate change to minimize future biodiversity loss and ensure we do not waste limited resources on ineffective conservation strategies. Currently, most predictions of species responses to climate change ignore the potential for evolution. However, evolution can alter species ecological responses, and different aspects of evolution and ecology can interact to produce complex eco‐evolutionary dynamics under climate change. Here we review how evolution could alter ecological responses to climate change on species warm and cool range margins, where evolution could be especially important. We discuss different aspects of evolution in isolation, and then synthesize results to consider how multiple evolutionary processes might interact and affect conservation strategies. On species cool range margins, the evolution of dispersal could increase range expansion rates and allow species to adapt to novel conditions in their new range. However, low genetic variation and genetic drift in small range‐front populations could also slow or halt range expansions. Together, these eco‐evolutionary effects could cause a three‐step, stop‐and‐go expansion pattern for many species. On warm range margins, isolation among populations could maintain high genetic variation that facilitates evolution to novel climates and allows species to persist longer than expected without evolution. This ‘evolutionary extinction debt’ could then prevent other species from shifting their ranges. However, as climate change increases isolation among populations, increasing dispersal mortality could select for decreased dispersal and cause rapid range contractions. Some of these eco‐evolutionary dynamics could explain why many species are not responding to climate change as predicted. We conclude by suggesting that resurveying historical studies that measured trait frequencies, the strength of selection, or heritabilities could be an efficient way to increase our eco‐evolutionary knowledge in climate change biology.

An evaluation of transferability of ecological niche models

Abstract: Ecological niche modeling (ENM) is used widely to study species’ geographic distributions. ENM applications frequently involve transferring models calibrated with environmental data from one region to other regions or times that may include novel environmental conditions. When novel conditions are present, transferability implies extrapolation, whereas, in absence of such conditions, transferability is an interpolation step only. We evaluated transferability of models produced using 11 ENM algorithms from the perspective of interpolation and extrapolation in a virtual species framework. We defined fundamental niches and potential distributions of 16 virtual species distributed across Eurasia. To simulate real situations of incomplete understanding of species’ distribution or existing fundamental niche (environmental conditions suitable for the species contained in the study area; N*F), we divided Eurasia into six regions and used 1–5 regions for model calibration and the rest for model evaluation. The models produced with the 11 ENM algorithms were evaluated in environmental space, to complement the traditional geographic evaluation of models. None of the algorithms accurately estimated the existing fundamental niche (N*F) given one region in calibration, and model evaluation scores decreased as the novelty of the environments in the evaluation regions increased. Thus, we recommend quantifying environmental similarity between calibration and transfer regions prior to model transfer, providing an avenue for assessing uncertainty of model transferability. Different algorithms had different sensitivity to completeness of knowledge of N*F, with implications for algorithm selection. If the goal is to reconstruct fundamental niches, users should choose algorithms with limited extrapolation when N*F is well known, or choose algorithms with increased extrapolation when N*F is poorly known. Our assessment can inform applications of ecological niche modeling transference to anticipate species invasions into novel areas, disease emergence in new regions, and forecasts of species distributions under future climate conditions.

The effect of sample size on the accuracy of species distribution models: considering both presences and pseudo-absences or background sites

Abstract: Most high-performing species distribution modelling techniques require both presences, and either absences or pseudo-absences or background points. In this paper, we explore the effect of sample size, towards developing improved strategies for modelling. We generated 1800 virtual species with three levels of prevalence using ten modelling techniques, while varying the number of training presences (NTP) and the number of random points (NRP representing pseudo-absences or background sites). For five of the ten modelling techniques we built two versions of models: one with an equal total weight (ETW) setting where the total weight for pseudo-absence is equivalent to the total weight for presence, and another with an unequal total weight (UTW) setting where the total weight for pseudo-absence is not required to be equal to the total weight for presence. We compared two strategies for NRP: a small multiplier strategy (i.e. setting NRP at a few times as large as NTP), and a large number strategy (i.e. using numerous random points). We produced ensemble models (by averaging the predictions from 30 models built with the same set of training presences and different sets of random points in equivalent numbers) for three NTP magnitudes and two NRP strategies. We found that model accuracy altered as NRP increased with four distinct patterns of performance: increasing, decreasing, arch-shaped and horizontal. In most cases ETW improved model performance. Ensemble models had higher accuracy than the corresponding single models, and this improvement was pronounced when NTP was low. We conclude that a large NRP is not always an appropriate strategy. The best choice for NRP will depend on the modelling techniques used, species prevalence and NTP. We recommend building ensemble models instead of single models, using the small multiplier strategy for NRP with ETW, especially when only a small number of species presence records are available.

Bringing Elton and Grinnell together: a quantitative framework to represent the biogeography of ecological interaction networks

Abstract: Biogeography has traditionally focused on the spatial distribution and abundance of species. Both are driven by the way species interact with one another, but only recently community ecologists realized the need to document their spatial and temporal variation. Here, we call for an integrated approach, adopting the view that community structure is best represented as a network of ecological interactions, and show how it translates to biogeography questions. We propose that the ecological niche should encompass the effect of the environment on species distribution (the Grinnellian dimension of the niche) and on the ecological interactions among them (the Eltonian dimension). Starting from this concept, we develop a quantitative theory to explain turnover of interactions in space and time – i.e. a novel approach to interaction distribution modeling. We apply this framework to host–parasite interactions across Europe and find that two aspects of the environment (temperature and precipitation) exert a strong imprint on species co-occurrence, but not on species interactions. Even where species co-occur, interaction proves to be stochastic rather than deterministic, adding to variation in realized network structure. We also find that a large majority of host-parasite pairs are never found together, thus precluding any inferences regarding their probability to interact. This first attempt to explain variation of network structure at large spatial scales opens new perspectives at the interface of species distribution modeling and community ecology.

Revealing patterns of nocturnal migration using the European weather radar network

Abstract: Nocturnal avian migration flyways remain an elusive concept, as we have largely lacked methods to map their full extent We used the network of European weather radars to investigate nocturnal bird movements at the scale of the European flyway We mapped the main migration directions and showed the intensity of movement across part of Europe by extracting biological information from 70 weather radar stations from northern Scandinavia to Portugal, during the autumn migration season of 2016 On average, over the 20 nights and all sites, 389 birds passed per 1 km transect per hour The night with highest migration intensity showed an average of 1621 birds km–1 h–1 passing the radar stations, but there was considerable geographical and temporal variation in migration intensity The highest intensity of migration was seen in central France The overall migration directions showed strong southwest components Migration dynamics were strongly related to synoptic wind conditions A wind-related mass migration event occurred immediately after a change in wind conditions, but quickly diminished even when supporting winds continued to prevail This first continental-scale study using the European network of weather radars demonstrates the wealth of information available and its potential for investigating large-scale bird movements, with consequences for ecosystem function, nutrient transfer, human and livestock health, and civil and military aviation

Food-web structure varies along environmental gradients in a high-latitude marine ecosystem

Abstract: 295 –––––––––––––––––––––––––––––––––––––––– © 2018 The Authors. This is an Online Open article This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. Subject Editor: Dominique Gravel Editor-in-Chief: Miguel Araújo Accepted 9 May 2018 42: 295–308, 2019 doi: 10.1111/ecog.03443 doi: 10.1111/ecog.03443 42 295–308

Stability and synchrony across ecological hierarchies in heterogeneous metacommunities: linking theory to data

Abstract: Understanding stability across ecological hierarchies is critical for landscape management in a changing world. Recent studies showed that synchrony among lower‐level components is key to scaling temporal stability across two hierarchical levels, whether spatial or organizational. But an extended framework that integrates both spatial scale and organizational level simultaneously is required to clarify the sources of ecosystem stability at large scales. However, such an extension is far from trivial when taking into account the spatial heterogeneities in real‐world ecosystems. In this paper, we develop a partitioning framework that bridges variability and synchrony measures across spatial scales and organizational levels in heterogeneous metacommunities. In this framework, metacommunity variability is expressed as the product of local‐scale population variability and two synchrony indices that capture the temporal coherence across species and space, respectively. We develop an R function ‘var.partition’ and apply it to five types of desert plant communities to illustrate our framework and test how diversity shapes synchrony and variability at different hierarchical levels. As the observation scale increased from local populations to metacommunities, the temporal variability of plant productivity was reduced mainly by factors that decreased species synchrony. Species synchrony decreased from local to regional scales, and spatial synchrony decreased from species to community levels. Local and regional species diversity were key factors that reduced species synchrony at the two scales. Moreover, beta diversity contributed to decreasing spatial synchrony among communities. We conclude that our new framework offers a valuable toolbox for future empirical studies to disentangle the mechanisms and pathways by which ecological factors influence stability at large scales.

What we (don't) know about global plant diversity

Abstract: The era of big biodiversity data has led to rapid, exciting advances in the theoretical and applied biological, ecological and conservation sciences. While large genetic, geographic and trait databases are available, these are neither complete nor random samples of the globe. Gaps and biases in these databases reduce our inferential and predictive power, and this incompleteness is even more worrisome because we are ignorant of both its kind and magnitude. We performed a comprehensive examination of the taxonomic and spatial sampling in the most complete current databases for plant genes, locations and functional traits. To do this, we downloaded data from The Plant List (taxonomy), the Global Biodiversity Information Facility (locations), TRY (traits) and GenBank (genes). Only 17.7% of the world's described and accepted land plant species feature in all three databases, meaning that more than 82% of known plant biodiversity lacks representation in at least one database. Species coverage is highest for location data and lowest for genetic data. Bryophytes and orchids stand out taxonomically and the equatorial region stands out spatially as poorly represented in all databases. We have highlighted a number of clades and regions about which we know little functionally, spatially and genetically, on which we should set research targets. The scientific community should recognize and reward the significant value, both for biodiversity science and conservation, of filling in these gaps in our knowledge of the plant tree of life.

On population abundance and niche structure

Abstract: Recent published evidence indicates a negative correlation between density of populations and the distance of their environments to a suitably defined ‘niche centroid’. This empirical observation lacks theoretical grounds. We provide a theoretical underpinning for the empirical relationship between population density and position in niche space, and use this framework to understand the circumstances under which the relationship will fail. We propose a metapopulation model for the area of distribution, as a system of ordinary differential equations coupled with a dispersal kernel. We present an analytical approximation to the solution of the system as well as R code to solve the full model numerically. We use this tool to analyze various scenarios and assumptions. General and realistic demographic assumptions imply a good correlation between position in niche space and population abundance. Factors that modify this correlation are: transitory states, a heterogeneous spatial structure of suitability, and Allee effects. We also explain why the raw output of the niche modeling algorithm MaxEnt is not a good predictor of environmental suitability. Our results elucidate the empirical results for spatial patterns of population size in niche terms, and provide a theoretical basis for a structured theory of the niche.

Vertical stratification influences global patterns of biodiversity

Abstract: Species distributions in terrestrial ecosystems are three-dimensional, spanning both the horizontal landscape and the vertical space provided by the physical environment. Classical hypotheses suggest that communities become more vertically stratified with increasing species richness, owing to reduced competition or finer niche subdivision. However, this assertion remains untested in the context of the broader realm of biogeography. Here, integrating traits and distribution data for amphibians globally, we show how vertical strategies interact with the physical and climatic environments to govern global patterns of species richness and community composition. Our results reveal a marked latitudinal shift in strategies of vertical habitat use, from highly arboreal assemblages in the tropics to highly fossorial assemblages in sub-tropical and temperate regions. Arboreality is strongly associated with precipitation, vegetation structure and climatic stability, whereas fossoriality is more common in dry environments with high diurnal temperature range and low vegetation structure. These analyses shed light on the importance of vertical stratification for species coexistence in species-rich regions. As certain tropical habitats become drier from climate change, the rich biological diversity that is emblematic of the tropics may transition from vertically stratified to ‘flattened’, with future communities living mostly on or beneath the ground.

Understanding extinction debts: spatio–temporal scales, mechanisms and a roadmap for future research

Abstract: Extinction debt refers to delayed species extinctions expected as a consequence of ecosystem perturbation. Quantifying such extinctions and investigating long‐term consequences of perturbations has proven challenging, because perturbations are not isolated and occur across various spatial and temporal scales, from local habitat losses to global warming. Additionally, the relative importance of eco‐evolutionary processes varies across scales, because levels of ecological organization, i.e. individuals, (meta)populations and (meta)communities, respond hierarchically to perturbations. To summarize our current knowledge of the scales and mechanisms influencing extinction debts, we reviewed recent empirical, theoretical and methodological studies addressing either the spatio–temporal scales of extinction debts or the eco‐evolutionary mechanisms delaying extinctions. Extinction debts were detected across a range of ecosystems and taxonomic groups, with estimates ranging from 9 to 90% of current species richness. The duration over which debts have been sustained varies from 5 to 570 yr, and projections of the total period required to settle a debt can extend to 1000 yr. Reported causes of delayed extinctions are 1) life‐history traits that prolong individual survival, and 2) population and metapopulation dynamics that maintain populations under deteriorated conditions. Other potential factors that may extend survival time such as microevolutionary dynamics, or delayed extinctions of interaction partners, have rarely been analyzed. Therefore, we propose a roadmap for future research with three key avenues: 1) the microevolutionary dynamics of extinction processes, 2) the disjunctive loss of interacting species and 3) the impact of multiple regimes of perturbation on the payment of debts. For their ability to integrate processes occurring at different levels of ecological organization, we highlight mechanistic simulation models as tools to address these knowledge gaps and to deepen our understanding of extinction dynamics.

Addressing common pitfalls does not provide more support to geographical and ecological abundant‐centre hypotheses

Abstract: 696 –––––––––––––––––––––––––––––––––––––––– © 2018 The Authors. This is an Online Open article This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. Subject Editor: Carsten Dormann Editor-in-Chief: Miguel Araújo Accepted 5 September 2018 42: 696–705, 2019 doi: 10.1111/ecog.04027 doi: 10.1111/ecog.04027 42 696–705

Unifying functional trait approaches to understand the assemblage of ecological communities: synthesizing taxonomic divides

Abstract: Functional traits have long been considered the ‘holy grail’ in community ecology due to their potential to link phenotypic variation with ecological processes. Advancements across taxonomic disciplines continue to support functional ecology's objective to approach generality in community assembly. However, a divergence of definitions, aims and methods across taxa has created discord, limiting the field's predictive capacity. Here, we provide a guide to support functional ecological comparisons across taxa. We describe advances in cross‐taxa functional research, identify gaps in approaches, synthesize definitions and unify methodological considerations. When deciding which traits to compare, particularly response traits, we advocate selecting functionally analogous traits that relate to community assembly processes. Finally, we describe at what scale and for which questions functional comparisons across taxa are useful and when other approaches may be more constructive. Our approach promotes standardized methods for integrative research across taxa to identify broad trends in community assembly.

Are island‐like systems biologically similar to islands? A review of the evidence

Abstract: Islands are geographically defined as land masses completely surrounded by water, and island systems have been used as models for many biogeographic, ecological, and evolutionary theories ever since Darwin's pioneering efforts. However, their biological definition is complex. Over the past few decades these theories have been applied to many study systems that only share some geographic features with island systems. These features include spatial fragmentation, limited area, spatial and temporal isolation from adjacent parts of the system, and low connectivity between different parts within the system, to mention just a few. These systems vary in their form, the matrix that surrounds them, the factors defining their borders, the extent of insularity they impose on the different taxa, and their geological similarity to different types of actual islands. Here, I seek to understand whether such island‐like systems (ILS) function biologically as true islands. In the first part, I describe the wide diversity of ILS suggested in the literature and the variation in the features that define their insularity. In the second part, I review the extent to which the main theories of island biology are applicable to these systems: species–area and species–isolation relationships, community composition, evolutionary radiations, and the extent of endemism and genetic diversity. In the third and final part, I suggest a new conceptual framework within which to classify and study the biology of ILS, as well as practical future research directions. I conclude that the term ‘biological island’ is a multi‐faceted concept, loosely related to its geographical definition. As ILS are often less isolated than true islands, and their biological patterns are only partly similar to those of true islands (and even this is true only for some ILS) the use of the term ‘island’ to describe any isolated habitat is therefore inappropriate.

oSCR: a spatial capture–recapture R package for inference about spatial ecological processes

Abstract: Spatial capture–recapture (SCR) methods have become widely applied in ecology. The immediate adoption of SCR is due to the fact that it resolves some major criticisms of traditional capture–recapture methods related to heterogeneity in detectabililty, and the emergence of new technologies (e.g. camera traps, non‐invasive genetics) that have vastly improved our ability to collection spatially explicit observation data on individuals. However, the utility of SCR methods reaches far beyond simply convenience and data availability. SCR presents a formal statistical framework that can be used to test explicit hypotheses about core elements of population and landscape ecology, and has profound implications for how we study animal populations. In this software note, we describe the technical basis and analytical workflow of oSCR, an R package for analyzing spatial encounter history data using a multi‐session sex‐structured likelihood. The impetus for developing oSCR was to create an accessible and transparent analysis tool that allows users to conveniently and intuitively formulate statistical models that map directly to fundamental processes of interest in spatial population ecology (e.g. space use, resource selection, density and connectivity). We have placed an emphasis on creating a transparent and accessible code base that is coupled with a logical workflow that we hope stimulates active participation in further technical developments.

sars: an R package for fitting, evaluating and comparing species–area relationship models

Abstract: The species–area relationship (SAR) constitutes one of the most general ecological patterns globally. A number of different SAR models have been proposed. Recent work has shown that no single model universally provides the best fit to empirical SAR datasets: multiple models may be of practical and theoretical interest. However, there are no software packages available that a) allow users to fit the full range of published SAR models, or b) provide functions to undertake a range of additional SAR-related analyses. To address these needs, we have developed the R package ‘sars’ that provides a wide variety of SAR-related functionality. The package provides functions to: a) fit 20 SAR models using non-linear and linear regression, b) calculate multi-model averaged curves using various information criteria, and c) generate confidence intervals using bootstrapping. Plotting functions allow users to depict and scrutinize the fits of individual models and multi-model averaged curves. The package also provides additional SAR functionality, including functions to fit, plot and evaluate the random placement model using a species–sites abundance matrix, and to fit the general dynamic model of oceanic island biogeography. The ‘sars’ R package will aid future SAR research by providing a comprehensive set of simple to use tools that enable in-depth exploration of SARs and SAR-related patterns. The package has been designed to allow other researchers to add new functions and models in the future and thus the package represents a resource for future SAR work that can be built on and expanded by workers in the field.

Testing methods in species distribution modelling using virtual species: what have we learnt and what are we missing?

Abstract: Species distribution models (SDMs) have become one of the major predictive tools in ecology. However, multiple methodological choices are required during the modelling process, some of which may have a large impact on forecasting results. In this context, virtual species, i.e. the use of simulations involving a fictitious species for which we have perfect knowledge of its occurrence-environment relationships and other relevant characteristics, have become increasingly popular to test SDMs. This approach provides for a simple virtual ecologist framework under which to test model properties, as well as the effects of the different methodological choices, and allows teasing out the effects of targeted factors with great certainty. This simplification is therefore very useful in setting up modelling standards and best practice principles. As a result, numerous virtual species studies have been published over the last decade. The topics covered include differences in performance between statistical models, effects of sample size, choice of threshold values, methods to generate pseudo-absences for presence-only data, among many others. These simulations have therefore already made a great contribution to setting best modelling practices in SDMs. Recent software developments have greatly facilitated the simulation of virtual species, with at least three different packages published to that effect. However, the simulation procedure has not been homogeneous, which introduces some subtleties in the interpretation of results, as well as differences across simulation packages. Here we 1) review the main contributions of the virtual species approach in the SDM literature; 2) compare the major virtual species simulation approaches and software packages; and 3) propose a set of recommendations for best simulation practices in future virtual species studies in the context of SDMs.

Where the wild birds go: explaining the differences in migratory destinations across terrestrial bird species

Abstract: Given their large movement capacities, migratory birds have in principle a wide range of possible geographical locations for their breeding and non‐breeding destinations, yet each species migrates between consistent breeding and non‐breeding ranges. In this study, we use a macroecological approach to search for the general factors explaining the location of the seasonal ranges of migratory bird species across the globe. We develop a null model to test the hypotheses that access to resources, geographical distance, tracking of temperature, and habitat conditions (separately as well as considered together) have a major influence in the location of species’ migratory destinations, once each species’ geographical constraints are taken into account. Our results provide evidence for a trade‐off between costs associated with distance travelled and gains in terms of better access to resources. We also provide strong support to the hypotheses that all factors tested, with the exception of habitat, have a strong and additive effect on the global geography of bird migration. Indeed, our results indicate that species’ contemporary migratory destinations (i.e. the combination of their breeding and non‐breeding ranges) are such that they allow species to track a temperature regime throughout the year, to escape local competition and reach areas with better access to resources, and to minimise the spatial distance travelled, within the limitations imposed by the geographical location of each species. Our study thus sheds light on the mechanisms underpinning bird migration and provides a strong basis for predicting how migratory species will respond to future change.

The grand challenges of migration ecology that radar aeroecology can help answer

Abstract: Many migratory species have experienced substantial declines that resulted from rapid and massive expansions of human structures and activities, habitat alterations and climate change. Migrants are also recognized as an integral component of biodiversity and provide a multitude of services and disservices that are relevant to human agriculture, economy and health. The plethora of recently published studies reflects the need for better fundamental knowledge on migrations and for better management of their ecological and human‐relevant effects. Yet, where are we in providing answers to fundamental questions and societal challenges? Engaging a broad network of researchers worldwide, we used a horizon‐scan approach to identify the most important challenges which need to be overcome in order to gain a fuller understanding of migration ecology, and which could be addressed using radar aeroecological and macroecological approaches. The top challenges include both long‐standing and novel topics, ranging from fundamental information on migration routes and phenology, orientation and navigation strategies, and the multitude of effects migrants may have on resident communities, to societal challenges, such as protecting or preventing migrant services and disservices, and the conservation of migrants in the face of environmental changes. We outline these challenges, identify the urgency of addressing them and the primary stakeholders – researchers, policy makers and practitioners, or funders of research.=

Recalculating route: dispersal constraints will drive the redistribution of Amazon primates in the Anthropocene

Abstract: Climate change will redistribute the global biodiversity in the Anthropocene. As climates change, species might move from one place to another, due to local extinctions and colonization of new environments. However, the existence of permeable migratory routes precedes faunal migrations in fragmented landscapes. Here, we investigate how dispersal will affect the outcome of climate change on the distribution of Amazon's primate species. We modeled the distribution of 80 Amazon primate species, using ecological niche models, and projected their potential distribution on scenarios of climate change. Then, we imposed landscape restrictions to primate dispersal, derived from a natural biogeographical barrier to primates (the main tributaries of the Amazon river) and an anthropogenic constraint to the migration of many canopy‐dependent animals (deforested areas). We also highlighted potential conflict zones, i.e. regions of high migration potential but predicted to be deforested. Species response to climate change varied across dispersal limitation scenarios. If species could occupy all newly suitable climate, almost 70% of species could expand ranges. Including dispersal barriers (natural and anthropogenic), however, led to range expansion in only less than 20% of the studied species. When species were not allowed to migrate, all of them lost an average of 90% of the suitable area, suggesting that climate may become unsuitable within their present distributions. All Amazon primate species may need to move as climate changes to avoid deleterious effects of exposure to non‐analog climates. The effect of climate change on the distribution of Amazon primates will ultimately depend on whether landscape permeability will allow climate‐driven faunal migrations. The network of protected areas in the Amazon could work as ‘stepping stones’ but most are outside important migratory routes. Therefore, protecting important dispersal corridors is foremost to allow effective migrations of the Amazon fauna in face of climate change and deforestation.

Bunching up the background betters bias in species distribution models

Abstract: Sets of presence records used to model species’ distributions typically consist of observations collected opportunistically rather than systematically. As a result, sampling probability is geographically uneven, which may confound the model's characterization of the species’ distribution. Modelers frequently address sampling bias by manipulating training data: either subsampling presence data or creating a similar spatial bias in non‐presence background data. We tested a new method, which we call ‘background thickening’, in the latter category. Background thickening entails concentrating background locations around presence locations in proportion to presence location density. We compared background thickening to two established sampling bias correction methods – target group background selection and presence thinning – using simulated data and data from a case study. In the case study, background thickening and presence thinning performed similarly well, both producing better model discrimination than target group background selection, and better model calibration than models without correction. In the simulation, background thickening performed better than presence thinning when the number of simulated presence locations was low, and vice versa. We discuss drawbacks to target group background selection, why background thickening and presence thinning are conservative but robust sampling bias correction methods, and why background thickening is better than presence thinning for small sample sizes. Particularly, background thickening is advantageous for treating sampling bias when data are scarce because it avoids discarding presence records.

Accessibility maps as a tool to predict sampling bias in historical biodiversity occurrence records

Abstract: Historical biodiversity occurrence records are often discarded in spatial modeling analyses because of a lack of a method to quantify their sampling bias. Here we propose a new approach for predicting sampling bias in historical written records of occurrence, using a South African example as proof of concept. We modelled and mapped accessibility of the study area as the mean of proximity to freshwater and European settlements. We tested the model’s ability to predict the location of historical biodiversity records from a dataset of 2612 large mammal occurrence records collected from historical written sources in South Africa in the period 1497–1920. We investigated temporal, spatial and environmental biases in these historical records and examined if the model prediction and occurrence dataset share similar environmental bias. We find a good agreement between the accessibility map and the distribution of sampling effort in the early historical period in South Africa. Environmental biases in the empirical data are identified, showing a preference for lower maximum temperature of the warmest month, higher mean monthly precipitation, higher net primary productivity and less arid biomes than expected by a uniform use of the study area. We find that the model prediction shares similar environmental bias as the empirical data. Accessibility maps, built with very simple statistical rules and in the absence of empirical data, can thus predict the spatial and environmental biases observed in historical biodiversity occurrence records. We recommend that this approach be used as a tool to estimate sampling bias in small datasets of occurrence and to improve the use of these data in spatial analyses in ecological and conservation studies.

Co-declining mammal–dung beetle faunas throughout the Atlantic Forest biome of South America

Abstract: The millennial–scale evolutionary relationships between mammals and dung beetles have been eroded due to several drivers of contemporary biodiversity loss. Although some evidence of co-decline has been shown for mammals and dung beetles at some Neotropical sites, a biome-scale analysis for the entire Atlantic Forest of South America would strengthen our understanding of how relictual sets of mammal species can affect dung beetle co-occurrences and co-declines. We therefore collated hundreds of assemblages of both dung beetles and medium- to large-bodied mammals throughout the world's longest tropical forest latitudinal gradient to examine to what extent mammal assemblages may exert a positive influence on dung beetle species composition and functional assembly, and whether this relationship is scale dependent. We also collated several climatic and other environmental variables to examine the degree to which they shape mammal–dung beetle relationships. The relationships between local mammal and dung beetle faunas were examined using regression models, variation partitioning, dissimilarity indices and ecological networks. We found a clear positive relationship between mammal and dung beetle species richness across this forest biome, indicating an ongoing process of mammal–dung beetle niche-mediated co-decline. We found a strong relationship between the species composition of both taxa, in which dung beetle species dissimilarity apparently track changes in mammalian dissimilarity, typically in 80% of all cases. Co-variables such as phytomass and climatic variables also influenced mammal–dung beetle patterns of co-decline along the Atlantic Forest. We conclude that dung beetle diversity and community assembly are shaped by the remaining co-occurring mammal assemblages and their functional traits, and both groups were governed by environmental features. We emphasize that ecosystem-wide effects of mammal population declines remain poorly understood both quantitatively and qualitatively, and curbing large vertebrate defaunation will ensure the persistence of co-dependent species.

A comprehensive examination of the network position hypothesis across multiple river metacommunities

Abstract: The hierarchical branching nature of river networks can have a strong influence on the assembly of freshwater communities. This unique structure has spurred the development of the network position hypothesis (NPH), which states that the strength of different assembly processes depends on the community position in the river network. Specifically, it predicts that 1) headwater communities should be exclusively controlled by the local environment given that they are more isolated and environmentally heterogeneous relative to downstream reaches. In contrast, 2) downstream communities should be regulated by both environmental and dispersal processes due to increased connectivity given their central position in the riverscape. Although intuitive, the NPH has only been evaluated on a few catchments and it is not yet clear whether its predictions are generalizable. To fill this gap, we tested the NPH on river dwelling fishes using an extensive dataset from 28 French catchments. Stream and climatic variables were assembled to characterize environmental conditions and graph theory was applied on river networks to create spatial variables. We tested both predictions using variation partitioning analyses separately for headwater and downstream sites in each catchment. Only 10 catchments supported both predictions, 11 failed to support at least one of them, while in 7 the NPH was partially supported given that spatial variables were also significant for headwater communities. We then assembled a dataset at the catchment scale (e.g. topography, environmental heterogeneity, network connectivity) and applied a classification tree analysis (CTA) to determine which regional property could explain these results. The CTA showed that the NPH was not supported in catchments with high heterogeneity in connectivity among sites. In more homogeneously connected catchments, the NPH was only supported when headwaters were more environmentally heterogeneous than downstream sites. We conclude that the NPH is context dependent even for taxa dispersing exclusively within streams.

What can observational data reveal about metacommunity processes

Abstract: A key challenge for community ecology is to understand to what extent observational data can be used to infer the underlying community assembly processes. As different processes can lead to similar or even identical patterns, statistical analyses of non‐manipulative observational data never yield undisputable causal inference on the underlying processes. Still, most empirical studies in community ecology are based on observational data, and hence understanding under which circumstances such data can shed light on assembly processes is a central concern for community ecologists. We simulated a spatial agent‐based model that generates variation in metacommunity dynamics across multiple axes, including the four classic metacommunity paradigms as special cases. We further simulated a virtual ecologist who analysed snapshot data sampled from the simulations using eighteen output metrics derived from beta‐diversity and habitat variation indices, variation partitioning and joint species distribution modelling. Our results indicated two main axes of variation in the output metrics. The first axis of variation described whether the landscape has patchy or continuous variation, and thus was essentially independent of the properties of the species community. The second axis of variation related to the level of predictability of the metacommunity. The most predictable communities were niche‐based metacommunities inhabiting static landscapes with marked environmental heterogeneity, such as metacommunities following the species sorting paradigm or the mass effects paradigm. The most unpredictable communities were neutral‐based metacommunities inhabiting dynamics landscapes with little spatial heterogeneity, such as metacommunities following the neutral or patch sorting paradigms. The output metrics from joint species distribution modelling yielded generally the highest resolution to disentangle among the simulated scenarios. Yet, the different types of statistical approaches utilized in this study carried complementary information, and thus our results suggest that the most comprehensive evaluation of metacommunity structure can be obtained by combining them.