No species is an island: testing the effects of biotic interactions on models of avian niche occupation

Traditionally, the niche of a species is described as a hypothetical 3D space, constituted by well‐known biotic interactions (e.g. predation, competition, trophic relationships, resource–consumer interactions, etc.) and various abiotic environmental factors. Species distribution models (SDMs), also called “niche models” and often used to predict wildlife distribution at landscape scale, are typically constructed using abiotic factors with biotic interactions generally been ignored. Here, we compared the goodness of fit of SDMs for red‐backed shrike Lanius collurio in farmlands of Western Poland, using both the classical approach (modeled only on environmental variables) and the approach which included also other potentially associated bird species. The potential associations among species were derived from the relevant ecological literature and by a correlation matrix of occurrences. Our findings highlight the importance of including heterospecific interactions in improving our understanding of niche occupation for bird species. We suggest that suite of measures currently used to quantify realized species niches could be improved by also considering the occurrence of certain associated species. Then, an hypothetical “species 1” can use the occurrence of a successfully established individual of “species 2” as indicator or “trace” of the location of available suitable habitat to breed. We hypothesize this kind of biotic interaction as the “heterospecific trace effect” (HTE): an interaction based on the availability and use of “public information” provided by individuals from different species. Finally, we discuss about the incomes of biotic interactions for enhancing the predictive capacities on species distribution models.     

物种分布模型的发展及评价方法

物种分布模型已被广泛地应用于以保护区规划、气候变化对物种分布的影响等为目的的研究。回顾了已经得到广泛应用的多种物种分布模型,总结了评价模型性能的方法。基于物种分布模型的发展和应用以及性能评价中尚存在的问题,本文认为:在物种分布模型中集成样本选择模块能够避免模型预测过程中的过度拟合及欠拟合,增加变量选择模块可评估和降低变量之间自相关性的影响,增加生物因子以及将物种对环境的适应性机制(及扩散行为特征)和潜在分布模型进行结合,是提高模型预测性能的可行方案;在模型性能的评价方面,采用赤池信息量可对模型的预测性能进行客观评价。相关建议可为物种分布建模提供参考。     

Towards novel approaches to modelling biotic interactions in multispecies assemblages at large spatial extents

Aim Biotic interactions – within guilds or across trophic levels – have widely been ignored in species distribution models (SDMs). This synthesis outlines the development of ‘species interaction distribution models’ (SIDMs), which aim to incorporate multispecies interactions at large spatial extents using interaction matrices. Location Local to global. Methods We review recent approaches for extending classical SDMs to incorporate biotic interactions, and identify some methodological and conceptual limitations. To illustrate possible directions for conceptual advancement we explore three principal ways of modelling multispecies interactions using interaction matrices: simple qualitative linkages between species, quantitative interaction coefficients reflecting interaction strengths, and interactions mediated by interaction currencies. We explain methodological advancements for static interaction data and multispecies time series, and outline methods to reduce complexity when modelling multispecies interactions. Results Classical SDMs ignore biotic interactions and recent SDM extensions only include the unidirectional influence of one or a few species. However, novel methods using error matrices in multivariate regression models allow interactions between multiple species to be modelled explicitly with spatial co‐occurrence data. If time series are available, multivariate versions of population dynamic models can be applied that account for the effects and relative importance of species interactions and environmental drivers. These methods need to be extended by incorporating the non‐stationarity in interaction coefficients across space and time, and are challenged by the limited empirical knowledge on spatio‐temporal variation in the existence and strength of species interactions. Model complexity may be reduced by: (1) using prior ecological knowledge to set a subset of interaction coefficients to zero, (2) modelling guilds and functional groups rather than individual species, and (3) modelling interaction currencies and species’ effect and response traits. Main conclusions There is great potential for developing novel approaches that incorporate multispecies interactions into the projection of species distributions and community structure at large spatial extents. Progress can be made by: (1) developing statistical models with interaction matrices for multispecies co‐occurrence datasets across large‐scale environmental gradients, (2) testing the potential and limitations of methods for complexity reduction, and (3) sampling and monitoring comprehensive spatio‐temporal data on biotic interactions in multispecies communities.     

To Be Or Not to Be? Variable selection can change the projected fate of a threatened species under future climate

Species distribution models (SDMs) are commonly used to project future changes in the geographic ranges of species, to estimate extinction rates and to plan biodiversity conservation. However, these models can produce a range of results depending on how they are parameterized, and over‐reliance on a single model may lead to overconfidence in maps of future distributions. The choice of predictor variable can have a greater influence on projected future habitat than the range of climate models used. We demonstrate this in the case of the Ptunarra Brown Butterfly, a species listed as vulnerable in Tasmania, Australia. We use the Maxent model to develop future projections for this species based on three variable sets; all 35 commonly used so‐called ‘bioclimatic’ variables, a subset of these based on expert knowledge, and a set of monthly climate variables relevant to the species’ primary activity period. We used a dynamically downscaled regional climate model based on three global climate models. Depending on the choice of variable set, the species is projected either to experience very little contraction of habitat or to come close to extinction by the end of the century due to lack of suitable climate. The different conclusions could have important consequences for conservation planning and management, including the perceived viability of habitat restoration. The output of SDMs should therefore be used to define the range of possible trajectories a species may be on, and ongoing monitoring used to inform management as changes occur.     

Delimiting the geographical background in species distribution modelling

Aim The extent of the study area (geographical background, GB) can strongly affect the results of species distribution models (SDMs), but as yet we lack objective and practicable criteria for delimiting the appropriate GB. We propose an approach to this problem using trend surface analysis (TSA) and provide an assessment of the effects of varying GB extent on the performance of SDMs for four species. Location Mainland Spain. Methods Using data for four well known wild ungulate species and different GBs delimited with a TSA, we assessed the effects of GB extent on the predictive performance of SDMs: specifically on model calibration (Miller’s statistic) and discrimination (area under the curve of the receiver operating characteristic plot, AUC; sensitivity and specificity), and on the tendency of the models to predict environmental potential when they are projected beyond their training area. Results In the training area, discrimination significantly increased and calibration decreased as the GB was enlarged. In contrast, as GB was enlarged, both discriminatory power and calibration decreased when assessed in the core area of the species distributions. When models trained using small GBs were projected beyond their training area, they showed a tendency to predict higher environmental potential for the species than those models trained using large GBs. Main conclusions By restricting GB extent using a geographical criterion, model performance in the core area of the species distribution can be significantly improved. Large GBs make models demonstrate high discriminatory power but are barely informative. By delimiting GB using a geographical criterion, the effect of historical events on model parameterization may be reduced. Thus purely environmental models are obtained that, when projected onto a new scenario, depict the potential distribution of the species. We therefore recommend the use of TSA in geographically delimiting the GB for use in SDMs.     

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

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.     

Prediction of plant species distributions across six millennia

The usefulness of species distribution models (SDMs) in predicting impacts of climate change on biodiversity is difficult to assess because changes in species ranges may take decades or centuries to occur. One alternative way to evaluate the predictive ability of SDMs across time is to compare their predictions with data on past species distributions. We use data on plant distributions, fossil pollen and current and mid-Holocene climate to test the ability of SDMs to predict past climate-change impacts. We find that species showing little change in the estimated position of their realized niche, with resulting good model performance, tend to be dominant competitors for light. Different mechanisms appear to be responsible for among-species differences in model performance. Confidence in predictions of the impacts of climate change could be improved by selecting species with characteristics that suggest little change is expected in the relationships between species occurrence and climate patterns.     

Niche shift can impair the ability to predict invasion risk in the marine realm: an illustration using Mediterranean fish invaders

Climatic niche conservatism, the tendency of species‐climate associations to remain unchanged across space and time, is pivotal for forecasting the spread of invasive species and biodiversity changes. Indeed, it represents one of the key assumptions underlying species distribution models (SDMs), the main tool currently available for predicting range shifts of species. However, to date, no comprehensive assessment of niche conservatism is available for the marine realm. We use the invasion by Indo‐Pacific tropical fishes into the Mediterranean Sea, the world's most invaded marine basin, to examine the conservatism of the climatic niche. We show that tropical invaders may spread far beyond their native niches and that SDMs do not predict their new distributions better than null models. Our results suggest that SDMs may underestimate the potential spread of invasive species and call for prudence in employing these models in order to forecast species invasion and their response to environmental change.     

Creating individual accessible area hypotheses improves stacked species distribution model performance

Aim

Stacked species distribution models (SDMs) are an important step towards estimating species richness, but frequently overpredict this metric and therefore erroneously predict which species comprise a given community. We test the idea that developing hypotheses about accessible area a priori can greatly improve model performance. By integrating dispersal ability via accessible area into SDM creation, we address an often‐overlooked facet of ecological niche modelling.

Innovation

By limiting the training and transference areas to theoretically accessible areas, we are creating more accurate SDMs on the basis of a taxon's explorable environments. This limitation of space and environment is a more accurate reflection of a taxon's true dispersal properties and more accurately reflects the geographical and environmental space to which a taxon is exposed. Here, we compare the predictive performance of stacked SDMs derived from spatially constrained and unconstrained training areas.

Main conclusions

Restricting a species’ training and transference areas to a theoretically accessible area greatly improves model performance. Stacked SDMs drawn from spatially restricted training areas predicted species richness and community composition more accurately than non‐restricted stacked SDMs. These accessible area‐based restrictions mimic true dispersal barriers to species and limit training areas to the suite of environments to those which a species is exposed to in nature. Furthermore, these restrictions serve to ‘clip’ predictions in geographical space, thus removing overpredictions in adjacent geographical regions where the species is known to be absent.     

Disentangling scale dependencies in species environmental niches and distributions

Understanding species’ responses to environmental conditions, and how these ­species–environment associations shape spatial distributions, are longstanding goals in ecology and biogeography. However, an essential component of species–environment relationships – the spatial unit, or grain, at which they operate – remains unresolved. We identify three components of scale‐dependence in analyses of species–environment associations: 1) response grain, the grain at which species respond most strongly to their environment; 2) environment spatial structure, the pattern of spatial autocorrelation intrinsic to an environmental factor; and 3) analysis grain, the grain at which analyses are conducted and ecological inferences are made. We introduce a novel conceptual framework that defines these scale components in the context of analyzing species–environment relationships, and provide theoretical examples of their interactions for species with various ecological attributes. We then use a virtual species approach to investigate the impacts of each component on common methods of measuring and predicting species–environment relationships. We find that environment spatial structure has a substantial impact on the ability of even simple, univariate species distribution models (SDMs) to recover known species–­environment associations at coarse analysis grains. For simulated environments with ‘fine’ and ‘intermediate’ spatial structure, model explanatory power, and the frequency with which simple SDMs correctly estimated a virtual species’ response to the simulated environment, dramatically declined as analysis grain increased. Informed by these results, we use a scaling analysis to identify maximum analysis grains for individual environmental factors, and a scale optimization procedure to determine the grain of maximum predictive accuracy. Implementing these analysis grain thresholds and model performance standards in an example east African study system yields more accurate distribution predictions, compared to SDMs independently constructed at arbitrary analysis grains. Finally, we integrate our conceptual framework with virtual and empirical results to provide practical recommendations for researchers asking common questions about species–environment relationships.     

The effect of positional error on fine scale species distribution models increases for specialist species

Species occurrences inherently include positional error. Such error can be problematic for species distribution models (SDMs), especially those based on fine-resolution environmental data. It has been suggested that there could be a link between the influence of positional error and the width of the species ecological niche. Although positional errors in species occurrence data may imply serious limitations, especially for modelling species with narrow ecological niche, it has never been thoroughly explored. We used a virtual species approach to assess the effects of the positional error on fine-scale SDMs for species with environmental niches of different widths. We simulated three virtual species with varying niche breadth, from specialist to generalist. The true distribution of these virtual species was then altered by introducing different levels of positional error (from 5 to 500 m). We built generalized linear models and MaxEnt models using the distribution of the three virtual species (unaltered and altered) and a combination of environmental data at 5 m resolution. The models’ performance and niche overlap were compared to assess the effect of positional error with varying niche breadth in the geographical and environmental space. The positional error negatively impacted performance and niche overlap metrics. The amplitude of the influence of positional error depended on the species niche, with models for specialist species being more affected than those for generalist species. The positional error had the same effect on both modelling techniques. Finally, increasing sample size did not mitigate the negative influence of positional error. We showed that fine-scale SDMs are considerably affected by positional error, even when such error is low. Therefore, where new surveys are undertaken, we recommend paying attention to data collection techniques to minimize the positional error in occurrence data and thus to avoid its negative effect on SDMs, especially when studying specialist species.     

Controlled comparison of species- and community-level models across novel climates and communities

Species distribution models (SDMs) assume species exist in isolation and do not influence one another''s distributions, thus potentially limiting their ability to predict biodiversity patterns. Community-level models (CLMs) capitalize on species co-occurrences to fit shared environmental responses of species and communities, and therefore may result in more robust and transferable models. Here, we conduct a controlled comparison of five paired SDMs and CLMs across changing climates, using palaeoclimatic simulations and fossil-pollen records of eastern North America for the past 21 000 years. Both SDMs and CLMs performed poorly when projected to time periods that are temporally distant and climatically dissimilar from those in which they were fit; however, CLMs generally outperformed SDMs in these instances, especially when models were fit with sparse calibration datasets. Additionally, CLMs did not over-fit training data, unlike SDMs. The expected emergence of novel climates presents a major forecasting challenge for all models, but CLMs may better rise to this challenge by borrowing information from co-occurring taxa.     

Predicting species distribution: offering more than simple habitat models

In the last two decades, interest in species distribution models (SDMs) of plants and animals has grown dramatically. Recent advances in SDMs allow us to potentially forecast anthropogenic effects on patterns of biodiversity at different spatial scales. However, some limitations still preclude the use of SDMs in many theoretical and practical applications. Here, we provide an overview of recent advances in this field, discuss the ecological principles and assumptions underpinning SDMs, and highlight critical limitations and decisions inherent in the construction and evaluation of SDMs. Particular emphasis is given to the use of SDMs for the assessment of climate change impacts and conservation management issues. We suggest new avenues for incorporating species migration, population dynamics, biotic interactions and community ecology into SDMs at multiple spatial scales. Addressing all these issues requires a better integration of SDMs with ecological theory.     

Projecting climate change impacts on species distributions in megadiverse South African Cape and Southwest Australian Floristic Regions: Opportunities and challenges

Increasing evidence shows that anthropogenic climate change is affecting biodiversity. Reducing or stabilizing greenhouse gas emissions may slow global warming, but past emissions will continue to contribute to further unavoidable warming for more than a century. With obvious signs of difficulties in achieving effective mitigation worldwide in the short term at least, sound scientific predictions of future impacts on biodiversity will be required to guide conservation planning and adaptation. This is especially true in Mediterranean type ecosystems that are projected to be among the most significantly affected by anthropogenic climate change, and show the highest levels of confidence in rainfall projections. Multiple methods are available for projecting the consequences of climate change on the main unit of interest – the species – with each method having strengths and weaknesses. Species distribution models (SDMs) are increasingly applied for forecasting climate change impacts on species geographic ranges. Aggregation of models for different species allows inferences of impacts on biodiversity, though excluding the effects of species interactions. The modelling approach is based on several further assumptions and projections and should be treated cautiously. In the absence of comparable approaches that address large numbers of species, SDMs remain valuable in estimating the vulnerability of species. In this review we discuss the application of SDMs in predicting the impacts of climate change on biodiversity with special reference to the species‐rich South West Australian Floristic Region and South African Cape Floristic Region. We discuss the advantages and challenges in applying SDMs in biodiverse regions with high levels of endemicity, and how a similar biogeographical history in both regions may assist us in understanding their vulnerability to climate change. We suggest how the process of predicting the impacts of climate change on biodiversity with SDMs can be improved and emphasize the role of field monitoring and experiments in validating the predictions of SDMs.     

揭示群落结构及其环境响应的联合物种分布模型的研究进展

物种分布模型通常用于基础生态和应用生态研究,用来确定影响生物分布和物种丰富度的因素,量化物种与非生物条件的关系,预测物种对土地利用和气候变化的反应,并确定潜在的保护区.在传统的物种分布模型中,生物的相互作用很少被纳入,而联合物种分布模型(JSDMs)作为近年提出的一种新的可行方法,可以同时考虑环境因素和生物交互作用,因而成为分析生物群落结构和种间相互作用过程的有力工具.JSDMs以物种分布模型(SDMs)为基础,通常采用广义线性回归模型建立物种对环境变量的多变量响应,以随机效应的形式获取物种间的关联,同时结合隐变量模型(LVMs),并基于Laplace近似和马尔科夫蒙脱卡罗模拟的最大似然估计或贝叶斯方法来估算模型参数.本文对JSDMs的产生及理论基础进行归纳总结,重点介绍了不同类型JSDMs的特点及其在现代生态学中的应用,阐述了JSDMs的应用前景、使用过程中存在的问题及发展方向.随着对环境因素与多物种种间关系研究的深入,JSDMs将是今后物种分布模型研究的重点.     

Incorporating intraspecific variation into species distribution models improves distribution predictions,but cannot predict species traits for a wide-spread plant species

The most common approach to predicting how species ranges and ecological functions will shift with climate change is to construct correlative species distribution models (SDMs). These models use a species’ climatic distribution to determine currently suitable areas for the species and project its potential distribution under future climate scenarios. A core, rarely tested, assumption of SDMs is that all populations will respond equivalently to climate. Few studies have examined this assumption, and those that have rarely dissect the reasons for intraspecific differences. Focusing on the arctic-alpine cushion plant Silene acaulis, we compared predictive accuracy from SDMs constructed using the species’ full global distribution with composite predictions from separate SDMs constructed using subpopulations defined either by genetic or habitat differences. This is one of the first studies to compare multiple ways of constructing intraspecific-level SDMs with a species-level SDM. We also examine the contested relationship between relative probability of occurrence and species performance or ecological function, testing if SDM output can predict individual performance (plant size) and biotic interactions (facilitation). We found that both genetic- and habitat-informed SDMs are considerably more accurate than a species-level SDM, and that the genetic model substantially differs from and outperforms the habitat model. While SDMs have been used to infer population performance and possibly even biotic interactions, in our system these relationships were extremely weak. Our results indicate that individual subpopulations may respond differently to climate, although we discuss and explore several alternative explanations for the superior performance of intraspecific-level SDMs. We emphasize the need to carefully examine how to best define intraspecific-level SDMs as well as how potential genetic, environmental, or sampling variation within species ranges can critically affect SDM predictions. We urge caution in inferring population performance or biotic interactions from SDM predictions, as these often-assumed relationships are not supported in our study.     

Combining physiological threshold knowledge to species distribution models is key to improving forecasts of the future niche for macroalgae

Species distribution models (SDM) are a useful tool for predicting species range shifts in response to global warming. However, they do not explore the mechanisms underlying biological processes, making it difficult to predict shifts outside the environmental gradient where the model was trained. In this study, we combine correlative SDMs and knowledge on physiological limits to provide more robust predictions. The thermal thresholds obtained in growth and survival experiments were used as proxies of the fundamental niches of two foundational marine macrophytes. The geographic projections of these species’ distributions obtained using these thresholds and existing SDMs were similar in areas where the species are either absent‐rare or frequent and where their potential and realized niches match, reaching consensus predictions. The cold‐temperate foundational seaweed Himanthalia elongata was predicted to become extinct at its southern limit in northern Spain in response to global warming, whereas the occupancy of southern‐lusitanic Bifurcaria bifurcata was expected to increase. Combined approaches such as this one may also highlight geographic areas where models disagree potentially due to biotic factors. Physiological thresholds alone tended to over‐predict species prevalence, as they cannot identify absences in climatic conditions within the species’ range of physiological tolerance or at the optima. Although SDMs tended to have higher sensitivity than threshold models, they may include regressions that do not reflect causal mechanisms, constraining their predictive power. We present a simple example of how combining correlative and mechanistic knowledge provides a rapid way to gain insight into a species’ niche resulting in consistent predictions and highlighting potential sources of uncertainty in forecasted responses to climate change.     

种间作用对物种分布模拟的影响及建模方法

物种分布模型(SDMs)通过量化物种分布和环境变量之间的关系,并将其外推到未知的景观单元,模拟、预测地理空间中生物的潜在分布,是生态学、生物地理学、保护生物学等研究领域的重要工具.然而,目前物种分布模型主要采用非生物因素作为预测变量,由于数据量化和建模表达困难,生物因素特别是种间作用在物种分布模型中常被忽略,将种间作用...     

Equilibrium or not? Modelling potential distribution of invasive species in different stages of invasion

Aim The assumption of equilibrium between organisms and their environment is a standard working postulate in species distribution models (SDMs). However, this assumption is typically violated in models of biological invasions where range expansions are highly constrained by dispersal and colonization processes. Here, we examined how stage of invasion affects the extent to which occurrence data represent the ecological niche of organisms and, in turn, influences spatial prediction of species’ potential distributions. Location Six ecoregions in western Oregon, USA. Methods We compiled occurrence data from 697 field plots collected over a 9‐year period (2001–09) of monitoring the spread of invasive forest pathogen Phytophthora ramorum. Using these data, we applied ecological‐niche factor analysis to calibrate models of potential distribution across different years of colonization. We accounted for natural variation and uncertainties in model evaluation by further investigating three hypothetical scenarios of varying equilibrium in a simulated virtual species, for which the ‘true’ potential distribution was known. Results We confirm our hypothesis that SDMs calibrated in early stages of invasion are less accurate than models calibrated under scenarios closer to equilibrium. SDMs that are developed in early stages of invasion tend to underpredict the potential range compared to models that are built in later stages of invasion. Main conclusions A full environmental niche of invasive species cannot be effectively captured with data from a realized distribution that is restricted by processes preventing full occupancy of suitable habitats. If SDMs are to be used effectively in conservation and management, stage of invasion needs to be considered to avoid underestimation of habitats at risk of invasion.     

Do traits of plant species predict the efficacy of species distribution models for finding new occurrences?

Species distribution models (SDMs) are used to test ecological theory and to direct targeted surveys for species of conservation concern. Several studies have tested for an influence of species traits on the predictive accuracy of SDMs. However, most used the same set of environmental predictors for all species and/or did not use truly independent data to test SDM accuracy. We built eight SDMs for each of 24 plant species of conservation concern, varying the environmental predictors included in each SDM version. We then measured the accuracy of each SDM using independent presence and absence data to calculate area under the receiver operating characteristic curve (AUC) and true positive rate (TPR). We used generalized linear mixed models to test for a relationship between species traits and SDM accuracy, while accounting for variation in SDM performance that might be introduced by different predictor sets. All traits affected one or both SDM accuracy measures. Species with lighter seeds, animal‐dispersed seeds, and a higher density of occurrences had higher AUC and TPR than other species, all else being equal. Long‐lived woody species had higher AUC than herbaceous species, but lower TPR. These results support the hypothesis that the strength of species–environment correlations is affected by characteristics of species or their geographic distributions. However, because each species has multiple traits, and because AUC and TPR can be affected differently, there is no straightforward way to determine a priori which species will yield useful SDMs based on their traits. Most species yielded at least one useful SDM. Therefore, it is worthwhile to build and test SDMs for the purpose of finding new populations of plant species of conservation concern, regardless of these species’ traits.