Understanding species distribution in dynamic populations: a new approach using spatio‐temporal point process models

Understanding and predicting a species’ distribution across a landscape is of central importance in ecology, biogeography and conservation biology. However, it presents daunting challenges when populations are highly dynamic (i.e. increasing or decreasing their ranges), particularly for small populations where information about ecology and life history traits is lacking. Currently, many modelling approaches fail to distinguish whether a site is unoccupied because the available habitat is unsuitable or because a species expanding its range has not arrived at the site yet. As a result, habitat that is indeed suitable may appear unsuitable. To overcome some of these limitations, we use a statistical modelling approach based on spatio‐temporal log‐Gaussian Cox processes. These model the spatial distribution of the species across available habitat and how this distribution changes over time, relative to covariates. In addition, the model explicitly accounts for spatio‐temporal dynamics that are unaccounted for by covariates through a spatio‐temporal stochastic process. We illustrate the approach by predicting the distribution of a recently established population of Eurasian cranes Grus grus in England, UK, and estimate the effect of a reintroduction in the range expansion of the population. Our models show that wetland extent and perimeter‐to‐area ratio have a positive and negative effect, respectively, in crane colonisation probability. Moreover, we find that cranes are more likely to colonise areas near already occupied wetlands and that the colonisation process is progressing at a low rate. Finally, the reintroduction of cranes in SW England can be considered a human‐assisted long‐distance dispersal event that has increased the dispersal potential of the species along a longitudinal axis in S England. Spatio‐temporal log‐Gaussian Cox process models offer an excellent opportunity for the study of species where information on life history traits is lacking, since these are represented through the spatio‐temporal dynamics reflected in the model.     

Joint species distribution modelling for spatio‐temporal occurrence and ordinal abundance data

Aim

Species distribution models are important tools used to study the distribution and abundance of organisms relative to abiotic variables. Dynamic local interactions among species in a community can affect abundance. The abundance of a single species may not be at equilibrium with the environment for spreading invasive species and species that are range shifting because of climate change. Innovation : We develop methods for incorporating temporal processes into a spatial joint species distribution model for presence/absence and ordinal abundance data. We model non‐equilibrium conditions via a temporal random effect and temporal dynamics with a vector‐autoregressive process allowing for intra‐ and interspecific dependence between co‐occurring species. The autoregressive term captures how the abundance of each species can enhance or inhibit its own subsequent abundance or the subsequent abundance of other species in the community and is well suited for a ‘community modules’ approach of strongly interacting species within a food web. R code is provided for fitting multispecies models within a Bayesian framework for ordinal data with any number of locations, time points, covariates and ordinal categories.

Main conclusions

We model ordinal abundance data of two invasive insects (hemlock woolly adelgid and elongate hemlock scale) that share a host tree and were undergoing northwards range expansion in the eastern U.S.A. during the period 1997–2011. Accounting for range expansion and high inter‐annual variability in abundance led to improved estimation of the species–environment relationships. We would have erroneously concluded that winter temperatures did not affect scale abundance had we not accounted for the range expansion of scale. The autoregressive component revealed weak evidence for commensalism, in which adelgid may have predisposed hemlock stands for subsequent infestation by scale. Residual spatial dependence indicated that an unmeasured variable additionally affected scale abundance. Our robust modelling approach could provide similar insights for other community modules of co‐occurring species.     

SESAM – a new framework integrating macroecological and species distribution models for predicting spatio‐temporal patterns of species assemblages

Two different approaches currently prevail for predicting spatial patterns of species assemblages. The first approach (macroecological modelling, MEM) focuses directly on realized properties of species assemblages, whereas the second approach (stacked species distribution modelling, S‐SDM) starts with constituent species to approximate the properties of assemblages. Here, we propose to unify the two approaches in a single ‘spatially explicit species assemblage modelling’ (SESAM) framework. This framework uses relevant designations of initial species source pools for modelling, macroecological variables, and ecological assembly rules to constrain predictions of the richness and composition of species assemblages obtained by stacking predictions of individual species distributions. We believe that such a framework could prove useful in many theoretical and applied disciplines of ecology and evolution, both for improving our basic understanding of species assembly across spatio‐temporal scales and for anticipating expected consequences of local, regional or global environmental changes. In this paper, we propose such a framework and call for further developments and testing across a broad range of community types in a variety of environments.     

Implications for conservation biocontrol of spatio‐temporal relationships between carabid beetles and coleopterous pests in winter oilseed rape

1 The spatio‐temporal distributions of predatory carabid beetles (Coleoptera: Carabidae) and their potential prey, the larvae of three coleopterous pests, Meligethes aeneus (Fabricius) and Ceutorhynchus spp. [Ceutorhynchus pallidactylus (Marsham), the cabbage stem weevil, and Ceutorhynchus assimilis (Paykull), the cabbage seed weevil], were studied within a crop of winter oilseed rape. The distributions of Collembola were recorded as potential alternative prey. Insect distributions were analysed and compared using Spatial Analysis by Distance Indices. 2 Mature larvae of the pests dropped from the crop canopy to the soil for pupation in temporal succession from May to early July. Their distributions within the crop were irregular and differed with species. 3 Adults of seven species or genera of carabid were abundant and active within the crop during May and June: Nebria brevicollis (Fabricius), Anchomenus dorsalis (Pontoppidan), Loricera pilicornis (Fabricius), Amara similata (Gyllenhal), Asaphidion spp., Pterostichus madidus (Fabricius) and Pterostichus melanarius (Illiger). 4 During May, N. brevicollis was spatially associated with peak numbers of M. aeneus larvae and with Collembola. Anchomenus dorsalis was spatially associated with Ceutorhynchus spp. larvae during two peaks in the abundance of the latter in early and late June. Nebria brevicollis and A. dorsalis coincided in both time and space with larvae of the three coleopterous pests when they were most vulnerable to predation by epigeal predators and are therefore good candidates for conservation biocontrol. 5 The importance of carabid beeetles in the natural enemy complex in winter oilseed rape and their potential for biocontrol of spring and summer pests are discussed in relation to husbandry practices for the crop and its adjacent areas which could be manipulated to promote carabid survival for integrated pest management.     

An integrated,spatio‐temporal modelling framework for analysing biological invasions

Aim

We develop a novel modelling framework for analysing the spatio‐temporal spread of biological invasions. The framework integrates different invasion drivers and disentangles their roles in determining observed invasion patterns by fitting models to historical distribution data. As a case study application, we analyse the spread of common ragweed (Ambrosia artemisiifolia).

Location

Central Europe.

Methods

A lattice system represents actual landscapes with environmental heterogeneity. Modelling covers the spatio‐temporal invasion sequence in this grid and integrates the effects of environmental conditions on local invasion suitability, the role of invaded cells and spatially implicit “background” introductions as propagule sources, within‐cell invasion level bulk‐up and multiple dispersal means. A modular framework design facilitates flexible numerical representation of the modelled invasion processes and customization of the model complexity. We used the framework to build and contrast increasingly complex models, and fitted them using a Bayesian inference approach with parameters estimated by Markov chain Monte Carlo (MCMC).

Results

All modelled invasion drivers codetermined the A. artemisiifolia invasion pattern. Inferences about individual drivers depended on which processes were modelled concurrently, and hence changed both quantitatively and qualitatively between models. Among others, the roles of environmental variables were assessed substantially differently subject to whether models included explicit source‐recipient cell relationships, spatio‐temporal variability in source cell strength and human‐mediated dispersal means. The largest fit improvements were found by integrating filtering effects of the environment and spatio‐temporal availability of propagule sources.

Main conclusions

Our modelling framework provides a straightforward means to build integrated invasion models and address hypotheses about the roles and mutual relationships of different putative invasion drivers. Its statistical nature and generic design make it suitable for studying many observed invasions. For efficient invasion modelling, it is important to represent changes in spatio‐temporal propagule supply by explicitly tracking the species’ colonization sequence and establishment of new populations.

     

Community versus single‐species distribution models for British plants

Aim Species distribution models are increasingly used to predict the impacts of global change on whole ecological communities by modelling the individualistic niche responses of large numbers of species. However, it is not clear whether this single‐species ensemble approach is preferable to community‐wide strategies that represent interspecific associations or shared responses to environmental gradients. Here, we test the performance of two multi‐species modelling approaches against equivalent single‐species models. Location Great Britain. Methods Single‐ and multi‐species distribution models were fitted for 701 native British plant species at a 10‐km grid scale. Two machine learning methods were used – classification and regression trees (CARTs) and artificial neural networks (ANNs). The single‐species versions are widely used in ecology but their multivariate extensions are less well known and have not previously been evaluated against one another. We compared their abilities to predict species distributions, community compositions and species richness in an independent geographical region reserved from model‐fitting. Results The single‐ and multi‐species models performed similarly, although the community models gave slightly poorer predictive accuracy by all measures. However, from the point of view of the whole community they were much simpler than the array of single‐species models, involving orders of magnitude fewer parameters. Multi‐species approaches also left greater residual spatial autocorrelation than the individualistic models and, contrary to expectation, were relatively less accurate for rarer species. However, the fitted multi‐species response curves had lower tendency for pronounced discontinuities that are unlikely to be a feature of realized niche responses. Main conclusions Although community distribution models were slightly less accurate than single‐species models, they offered a highly simplified way of modelling spatial patterns in British plant diversity. Moreover, an advantage of the multi‐species approach was that the modelling of shared environmental responses resolved more realistic response curves. However, there was a slight tendency for community models to predict rare species less accurately, which is potentially disadvantageous for conservation applications. We conclude that multi‐species distribution models may have potential for understanding and predicting the structure of ecological communities, but were slightly inferior to single‐species ensembles for our data.     

Spatio‐temporal variation of biotic factors underpins contemporary range dynamics of congeners

Species’ ranges are complex often exhibiting multidirectional shifts over space and time. Despite the strong fingerprint of recent historical climate change on species’ distributions, biotic factors such as loss of vegetative habitat and the presence of potential competitors constitute important yet often overlooked drivers of range dynamics. Furthermore, short‐term changes in environmental conditions can influence the underlying processes of local extinction and local colonization that drive range shifts, yet are rarely considered at broad scales. We used dynamic state‐space occupancy models to test multiple hypotheses of the relative importance of major drivers of range shifts of Golden‐winged Warblers (Vermivora chrysoptera) and Blue‐winged Warblers (V. cyanoptera) between 1983 and 2012 across North America: warming temperatures; habitat changes; and occurrence of congeneric species, used here as proxy for biotic interactions. Dynamic occupancies for both species were most influenced by spatial relative to temporal variation in temperature and habitat. However, temporal variation in temperature anomalies and biotic interactions remained important. The two biotic factors considered, habitat change and biotic interactions, had the largest relative effect on estimated extinction rates followed by abiotic temperature anomalies. For the Golden‐winged Warbler, the predicted presence of the Blue‐winged Warbler, a hypothesized competitor, most influenced extinction probabilities, contributing to evidence supporting its role in site‐level species replacement. Given the overall importance of biotic factors on range‐wide dynamic occupancies, their consideration alongside abiotic factors should not be overlooked. Our results suggest that warming compounds the negative effect of habitat loss emphasizing species’ need for habitat to adapt to a changing climate. Notably, even closely related species exhibited individual responses to abiotic and biotic factors considered.     

Projected impacts of climate change on spatio‐temporal patterns of freshwater fish beta diversity: a deconstructing approach

Aim To assess the potential impacts of future climate change on spatio‐temporal patterns of freshwater fish beta diversity. Location Adour–Garonne River Basin (France). Methods We first applied an ensemble modelling approach to project annually the future distribution of 18 fish species for the 2010–2100 period on 50 sites. We then explored the spatial and temporal patterns of beta diversity by distinguishing between its two additive components, namely species turnover and nestedness. Results Taxonomic homogenization of fish assemblages was projected to increase linearly over the 21st century, especially in the downstream parts of the river gradient. This homogenization process was almost entirely caused by a decrease in spatial species turnover. When considering the temporal dimension of beta diversity, our results reveal an overall pattern of decreasing beta diversity along the upstream–downstream river gradient. In contrast, when considering the turnover and nestedness components of temporal beta diversity we found significant U‐shaped and hump‐shaped relationships, respectively. Main conclusions Future climate change is projected to modify the taxonomic composition of freshwater fish assemblages by increasing their overall similarity over the Adour–Garonne River Basin. Our findings suggest that the distinction between the nestedness and turnover components of beta diversity is not only crucial for understanding the processes shaping spatial beta‐diversity patterns but also for identifying localities where the rates of species replacement are projected to be greatest. Specifically we recommend that future conservation studies should not only consider the spatial component of beta diversity but also its dynamic caused by climate warming.     

Integrating dynamic environmental predictors and species occurrences: Toward true dynamic species distribution models

While biological distributions are not static and change/evolve through space and time, nonstationarity of climatic and land‐use conditions is frequently neglected in species distribution models. Even recent techniques accounting for spatiotemporal variation of species occurrence basically consider the environmental predictors as static; specifically, in most studies using species distribution models, predictor values are averaged over a 50‐ or 30‐year time period. This could lead to a strong bias due to monthly/annual variation between the climatic conditions in which species' locations were recorded and those used to develop species distribution models or even a complete mismatch if locations have been recorded more recently. Moreover, the impact of land‐use change has only recently begun to be fully explored in species distribution models, but again without considering year‐specific values. Excluding dynamic climate and land‐use predictors could provide misleading estimation of species distribution. In recent years, however, open‐access spatially explicit databases that provide high‐resolution monthly and annual variation in climate (for the period 1901–2016) and land‐use (for the period 1992–2015) conditions at a global scale have become available. Combining species locations collected in a given month of a given year with the relative climatic and land‐use predictors derived from these datasets would thus lead to the development of true dynamic species distribution models (D‐SDMs), improving predictive accuracy and avoiding mismatch between species locations and predictor variables. Thus, we strongly encourage modelers to develop D‐SDMs using month‐ and year‐specific climatic data as well as year‐specific land‐use data that match the period in which species data were collected.     

Correlation and process in species distribution models: bridging a dichotomy

Within the field of species distribution modelling an apparent dichotomy exists between process‐based and correlative approaches, where the processes are explicit in the former and implicit in the latter. However, these intuitive distinctions can become blurred when comparing species distribution modelling approaches in more detail. In this review article, we contrast the extremes of the correlative–process spectrum of species distribution models with respect to core assumptions, model building and selection strategies, validation, uncertainties, common errors and the questions they are most suited to answer. The extremes of such approaches differ clearly in many aspects, such as model building approaches, parameter estimation strategies and transferability. However, they also share strengths and weaknesses. We show that claims of one approach being intrinsically superior to the other are misguided and that they ignore the process–correlation continuum as well as the domains of questions that each approach is addressing. Nonetheless, the application of process‐based approaches to species distribution modelling lags far behind more correlative (process‐implicit) methods and more research is required to explore their potential benefits. Critical issues for the employment of species distribution modelling approaches are given, together with a guideline for appropriate usage. We close with challenges for future development of process‐explicit species distribution models and how they may complement current approaches to study species distributions.     

Models for assessing local‐scale co‐abundance of animal species while accounting for differential detectability and varied responses to the environment

We developed a new modeling framework to assess how the local abundance of one species influences the local abundance of a potential competitor while explicitly accounting for differential responses to environmental conditions. Our models also incorporate imperfect detection as well as abundance estimation error for both species. As a case study, we applied the model to four pairs of mammal species in Borneo, surveyed by extensive and spatially widespread camera trapping. We detected different responses to elevation gradients within civet, macaque, and muntjac deer species pairs. Muntjac and porcupine species varied in their response to terrain ruggedness, and the two muntjac responded different to river proximity. Bornean endemic species of civet and muntjac were more sensitive than their widespread counterparts to habitat disturbance (selective logging). Local abundance within several species pairs was positively correlated, but this is likely due to the species having similar responses to (unmodeled) environmental conditions or resources rather than representing facilitation. After accounting for environment and correcting for false absences in detection, negative correlations in local abundance appear rare in tropical mammals. Direct competition may be weak in these species, possibly because the ‘ghost of competition past’ or habitat filtering have already driven separation of the species in niche space. The analytical framework presented here could increase basic understanding of how ecological interactions shape patterns of abundance across the landscape for a range of taxa, and also provide a powerful tool for forecasting the impacts of global change.     

Predicting coral community recovery using multi‐species population dynamics models

Predicting whether, how, and to what degree communities recover from disturbance remain major challenges in ecology. To predict recovery of coral communities we applied field survey data of early recovery dynamics to a multi‐species integral projection model that captured key demographic processes driving coral population trajectories, notably density‐dependent larval recruitment. After testing model predictions against field observations, we updated the model to generate projections of future coral communities. Our results indicated that communities distributed across an island landscape followed different recovery trajectories but would reassemble to pre‐disturbed levels of coral abundance, composition, and size, thus demonstrating persistence in the provision of reef habitat and other ecosystem services. Our study indicates that coral community dynamics are predictable when accounting for the interplay between species life‐history, environmental conditions, and density‐dependence. We provide a quantitative framework for evaluating the ecological processes underlying community trajectory and characteristics important to ecosystem functioning.     

Patterns of spatio‐temporal distribution,abundance, and diversity in a mosquito community from the eastern Smoky Hills of Kansas

Nearly 30% of emerging infectious disease events are caused by vector‐borne pathogens with wildlife origins. Their transmission involves a complex interplay among pathogens, arthropod vectors, the environment and host species, and they pose a risk for public health, livestock and wildlife species. Examining habitat associations of vector species known to transmit infectious diseases, and quantifying spatio‐temporal dynamics of mosquito vector communities is one aspect of the holistic One Health approach that is necessary to develop effective control measures. A survey was conducted from May to August, 2010 of the abundance and diversity of mosquito species occurring in the mixed‐grass prairie habitat of the Smoky Hills of Kansas. This region is an important breeding ground for North America's grassland nesting birds and, as such, it could represent an important habitat for the enzootic amplification cycle of avian malaria and infectious encephalitides, as well as spill‐over events to humans and livestock. A total of 11 species, belonging to the three genera Aedes, Anopheles, and Culex, was collected during this study. Aedes nigromaculis, Ae. sollicitans, Ae. taeniorhynchus, Culex salinarius, and Cx. tarsalis accounted for 98% of the collected species. Multiple linear regression models suggested that mosquito abundances in the grasslands of the central Great Plains were explained by meteorological and environmental variables. Temporal dynamics in mosquito abundances were well supported by models that included maximum and minimum temperature indices (adjusted R²= 0.73). Spatial dynamics of mosquito abundances were best explained by a model containing the following environmental variables (adjusted R²=0.37): ground curvature, topographic wetness index, distance to woodland, and distance to road. The mosquito species we detected are known vectors for infectious encephalitides, including West Nile virus. Understanding the microhabitat characteristics of these mosquito species in a grassland ecosystem will aid in the control and management of these disease vectors.