Hierarchical models facilitate spatial analysis of large data sets: a case study on invasive plant species in the northeastern United States

Abundance-mediated species interactions

Species interactions shape biodiversity patterns, community assemblage, and the dynamics of wildlife populations. Ecological theory posits that the strength of interspecific interactions is fundamentally underpinned by the population sizes of the involved species. Nonetheless, prevalent approaches for modeling species interactions predominantly center around occupancy states. Here, we use simulations to illuminate the inadequacies of modeling species interactions solely as a function of occupancy, as is common practice in ecology. We demonstrate erroneous inference into species interactions due to error in parameter estimates when considering species occupancy alone. To address this critical issue, we propose, develop, and demonstrate an abundance-mediated interaction framework designed explicitly for modeling species interactions involving two or more species from detection/non-detection data. We present Markov chain Monte Carlo (MCMC) samplers tailored for diverse ecological scenarios, including intraguild predation, disease- or predator-mediated competition, and trophic cascades. Illustrating the practical implications of our approach, we compare inference from modeling the interactions in a three-species network involving coyotes (Canis latrans), fishers (Pekania pennanti), and American marten (Martes americana) in North America as a function of occupancy states and as a function of abundance. When modeling interactions as a function of abundance rather than occupancy, we uncover previously unidentified interactions. Our study emphasizes that accounting for abundance-mediated interactions rather than simple co-occurrence patterns can fundamentally alter our comprehension of system dynamics. Through an empirical case study and comprehensive simulations, we demonstrate the importance of accounting for abundance when modeling species interactions, and we present a statistical framework equipped with MCMC samplers to achieve this paradigm shift in ecological research.

Joint species distribution models with imperfect detection for high-dimensional spatial data

Determining the spatial distributions of species and communities is a key task in ecology and conservation efforts. Joint species distribution models are a fundamental tool in community ecology that use multi-species detection-nondetection data to estimate species distributions and biodiversity metrics. The analysis of such data is complicated by residual correlations between species, imperfect detection, and spatial autocorrelation. While many methods exist to accommodate each of these complexities, there are few examples in the literature that address and explore all three complexities simultaneously. Here we developed a spatial factor multi-species occupancy model to explicitly account for species correlations, imperfect detection, and spatial autocorrelation. The proposed model uses a spatial factor dimension reduction approach and Nearest Neighbor Gaussian Processes to ensure computational efficiency for data sets with both a large number of species (e.g., >100) and spatial locations (e.g., 100,000). We compared the proposed model performance to five alternative models, each addressing a subset of the three complexities. We implemented the proposed and alternative models in the spOccupancy software, designed to facilitate application via an accessible, well documented, and open-source R package. Using simulations, we found that ignoring the three complexities when present leads to inferior model predictive performance, and the impacts of failing to account for one or more complexities will depend on the objectives of a given study. Using a case study on 98 bird species across the continental US, the spatial factor multi-species occupancy model had the highest predictive performance among the alternative models. Our proposed framework, together with its implementation in spOccupancy, serves as a user-friendly tool to understand spatial variation in species distributions and biodiversity while addressing common complexities in multi-species detection-nondetection data.

The shadow model: how and why small choices in spatially explicit species distribution models affect predictions

The use of species distribution models (SDMs) has rapidly increased over the last decade, driven largely by increasing observational evidence of distributional shifts of terrestrial and aquatic populations. These models permit, for example, the quantification of range shifts, the estimation of species co-occurrence, and the association of habitat to species distribution and abundance. The increasing complexity of contemporary SDMs presents new challenges-as the choices among modeling options increase, it is essential to understand how these choices affect model outcomes. Using a combination of original analysis and literature review, we synthesize the effects of three common model choices in semi-parametric predictive process species distribution modeling: model structure, spatial extent of the data, and spatial scale of predictions. To illustrate the effects of these choices, we develop a case study centered around sablefish (Anoplopoma fimbria) distribution on the west coast of the USA. The three modeling choices represent decisions necessary in virtually all ecological applications of these methods, and are important because the consequences of these choices impact derived quantities of interest (e.g., estimates of population size and their management implications). Truncating the spatial extent of data near the observed range edge, or using a model that is misspecified in terms of covariates and spatial and spatiotemporal fields, led to bias in population biomass trends and mean distribution compared to estimates from models using the full dataset and appropriate model structure. In some cases, these suboptimal modeling decisions may be unavoidable, but understanding the tradeoffs of these choices and impacts on predictions is critical. We illustrate how seemingly small model choices, often made out of necessity or simplicity, can affect scientific advice informing management decisions-potentially leading to erroneous conclusions about changes in abundance or distribution and the precision of such estimates. For example, we show how incorrect decisions could cause overestimation of abundance, which could result in management advice resulting in overfishing. Based on these findings and literature gaps, we outline important frontiers in SDM development.

A taxonomic-based joint species distribution model for presence-only data

Species distribution models (SDMs) are an important class of model for mapping taxa spatially and are a key tool for tackling biodiversity loss. However, most common SDMs depend on presence-absence data and, despite the accumulation and exponential growth of biological occurrence data across the globe, the available data are predominantly presence-only (i.e. they lack real absences). Although presence-only SDMs do exist, they inevitably require assumptions about absences of the considered taxa and they are specified mostly for single species and, thus, do not exploit fully the information in related taxa. This greatly limits the utility of global biodiversity databases such as GBIF. Here, we present a Bayesian-based SDM for multiple species that operates directly on presence-only data by exploiting the joint distribution between the multiple ecological processes and, crucially, identifies the sampling effort per taxa which allows inference on absences. The model was applied to two case studies. One, focusing on taxonomically diverse taxa over central Mexico and another focusing on the monophyletic family Cactacea over continental Mexico. In both cases, the model was able to identify the ecological and sampling effort processes for each taxon using only the presence observations, environmental and anthropological data.

Admission Hyperglycemia Predicts Long-Term Mortality in Critically Ill Patients With Subarachnoid Hemorrhage: A Retrospective Analysis of the MIMIC-III Database

Subarachnoid hemorrhage (SAH) is a severe subtype of stroke with high mortality. Hyperglycemia is a common phenomenon in critically ill patients and associated with poor clinical outcome. However, the predictive value of admission hyperglycemia for 30 and 90-day all-cause mortality in critically ill patients with SAH remains controversial. All SAH patients between 2001 and 2012 were included based on the MIMIC-III database and were further classified according to the tertiles of blood glucose (BG) measured on intensive care unit (ICU) admission. Clinical information including demographic data, comorbidities, and laboratory indicators were exacted and analyzed. The primary outcomes were 30- and 90-day all-cause mortality. A total of 1,298 SAH patients were included. The 30 and 90-day mortality rates were 19.80% and 22.73%, respectively. Subjects in the high glucose tertile were older, were overweight, had higher sequential organ failure assessment (SOFA) and Simplified Acute Physiology Score II (SAPS II) scores, and presented higher mortality rate. Generalized additive model revealed a U-shaped relationship between BG and 30 and 90-day all-cause mortality. Furthermore, Kaplan-Meier (K-M) survival curve also illustrated that subjects with admission hyperglycemia presented lower survival rate and shorter survival time. In Cox analysis, after adjustment for potential confounders, admission hyperglycemia was related to an increase in 30- and 90-day all-cause mortality in SAH patients. In subgroup analysis, the association between admission hyperglycemia and all-cause mortality was consistent. In conclusion, admission hyperglycemia is associated with significantly increased 30- and 90-day all-cause mortality in critically ill patients with SAH.

Quantifying the demographic vulnerabilities of dry woodlands to climate and competition using rangewide monitoring data

Climate change is expected to alter the distribution and abundance of tree species, impacting ecosystem structure and function. Yet, anticipating where this will occur is often hampered by a lack of understanding of how demographic rates, most notably recruitment, vary in response to climate and competition across a species range. Using large-scale monitoring data on two dry woodland tree species (Pinus edulis and Juniperus osteosperma), we develop an approach to infer recruitment, survival, and growth of both species across their range. In doing so, we account for ecological and statistical dependencies inherent in large-scale monitoring data. We find that drying and warming conditions generally lead to declines in recruitment and survival, but the strength of responses varied between species. These climate conditions point to geographic regions of high vulnerability for particular species, such as Pinus edulis in northern Arizona, where both survival and recruitment are low. Our approach provides a path forward for leveraging emerging large-scale monitoring and remotely sensed data to anticipate the impacts of global change on species distributions.

Modelling spatial patterns in host-associated microbial communities

Microbial communities exhibit spatial structure at different scales, due to constant interactions with their environment and dispersal limitation. While this spatial structure is often considered in studies focusing on free-living environmental communities, it has received less attention in the context of host-associated microbial communities or microbiota. The wider adoption of methods accounting for spatial variation in these communities will help to address open questions in basic microbial ecology as well as realize the full potential of microbiome-aided medicine. Here, we first overview known factors affecting the composition of microbiota across diverse host types and at different scales, with a focus on the human gut as one of the most actively studied microbiota. We outline a number of topical open questions in the field related to spatial variation and patterns. We then review the existing methodology for the spatial modelling of microbiota. We suggest that methodology from related fields, such as systems biology and macro-organismal ecology, could be adapted to obtain more accurate models of spatial structure. We further posit that methodological developments in the spatial modelling and analysis of microbiota could in turn broadly benefit theoretical and applied ecology and contribute to the development of novel industrial and clinical applications.

Ensuring identifiability in hierarchical mixed effects Bayesian models

Ecologists are increasingly familiar with Bayesian statistical modeling and its associated Markov chain Monte Carlo (MCMC) methodology to infer about or to discover interesting effects in data. The complexity of ecological data often suggests implementation of (statistical) models with a commensurately rich structure of effects, including crossed or nested (i.e., hierarchical or multi-level) structures of fixed and/or random effects. Yet, our experience suggests that most ecologists are not familiar with subtle but important problems that often arise with such models and with their implementation in popular software. Of foremost consideration for us is the notion of effect identifiability, which generally concerns how well data, models, or implementation approaches inform about, i.e., identify, quantities of interest. In this paper, we focus on implementation pitfalls that potentially misinform subsequent inference, despite otherwise informative data and models. We illustrate the aforementioned issues using random effects regressions on synthetic data. We show how to diagnose identifiability issues and how to remediate these issues with model reparameterization and computational and/or coding practices in popular software, with a focus on JAGS, OpenBUGS, and Stan. We also show how these solutions can be extended to more complex models involving multiple groups of nested, crossed, additive, or multiplicative effects, for models involving random and/or fixed effects. Finally, we provide example code (JAGS/OpenBUGS and Stan) that practitioners can modify and use for their own applications.

Empirical orthogonal function regression: Linking population biology to spatial varying environmental conditions using climate projections

Ecologists and oceanographers inform population and ecosystem management by identifying the physical drivers of ecological dynamics. However, different research communities use different analytical tools where, for example, physical oceanographers often apply rank-reduction techniques (a.k.a. empirical orthogonal functions [EOF]) to identify indicators that represent dominant modes of physical variability, whereas population ecologists use dynamical models that incorporate physical indicators as covariates. Simultaneously modeling physical and biological processes would have several benefits, including improved communication across sub-fields; more efficient use of limited data; and the ability to compare importance of physical and biological drivers for population dynamics. Here, we develop a new statistical technique, EOF regression, which jointly models population-scale dynamics and spatially distributed physical dynamics. EOF regression is fitted using maximum-likelihood techniques and applies a generalized EOF analysis to environmental measurements, estimates one or more time series representing modes of environmental variability, and simultaneously estimates the association of this time series with biological measurements. By doing so, it identifies a spatial map of environmental conditions that are best correlated with annual variability in the biological process. We demonstrate this method using a linear (Ricker) model for early-life survival ("recruitment") of three groundfish species in the eastern Bering Sea from 1982 to 2016, combined with measurements and end-of-century projections for bottom and sea surface temperature. Results suggest that (a) we can forecast biological dynamics while applying delta-correction and statistical downscaling to calibrate measurements and projected physical variables, (b) physical drivers are statistically significant for Pacific cod and walleye pollock recruitment, (c) separately analyzing physical and biological variables fails to identify the significant association for walleye pollock, and (d) cod and pollock will likely have reduced recruitment given forecasted temperatures over future decades.

A Generalized Linear Mixed Model Approach to Assess Emerald Ash Borer Diffusion

The Asian Emerald Ash Borer beetle (EAB, Agrilus planipennis Fairmaire) can cause damage to all species of Ash trees (Fraxinus), and rampant, unchecked infestations of this insect can cause significant damage to forests. It is thus critical to assess and model the spread of the EAB in a manner that allows authorities to anticipate likely areas of future tree infestation. In this study, a generalized linear mixed model (GLMM), combining the features of the commonly used generalized linear model (GLM) and a random effects model, was developed to predict future EAB spread patterns in Southern Ontario, Canada. The GLMM was designed to deal with autocorrelation in the data. Two random effects were established based on the geographic information provided with the EAB data, and a method based on statistical inference was proposed to identify the most significant factors associated with the distribution of the EAB. The results of the model showed that 95% of the testing data were correctly classified. The predictive performance of the GLMM was substantially enhanced in comparison with that obtained by the GLM. The influence of climatic factors, such as wind speed and anthropogenic activities, had the most significant influence on the spread of the EAB.

Computationally efficient joint species distribution modeling of big spatial data

The ongoing global change and the increased interest in macroecological processes call for the analysis of spatially extensive data on species communities to understand and forecast distributional changes of biodiversity. Recently developed joint species distribution models can deal with numerous species efficiently, while explicitly accounting for spatial structure in the data. However, their applicability is generally limited to relatively small spatial data sets because of their severe computational scaling as the number of spatial locations increases. In this work, we propose a practical alleviation of this scalability constraint for joint species modeling by exploiting two spatial-statistics techniques that facilitate the analysis of large spatial data sets: Gaussian predictive process and nearest-neighbor Gaussian process. We devised an efficient Gibbs posterior sampling algorithm for Bayesian model fitting that allows us to analyze community data sets consisting of hundreds of species sampled from up to hundreds of thousands of spatial units. The performance of these methods is demonstrated using an extensive plant data set of 30,955 spatial units as a case study. We provide an implementation of the presented methods as an extension to the hierarchical modeling of species communities framework.

Neural hierarchical models of ecological populations

Neural networks are increasingly being used in science to infer hidden dynamics of natural systems from noisy observations, a task typically handled by hierarchical models in ecology. This article describes a class of hierarchical models parameterised by neural networks - neural hierarchical models. The derivation of such models analogises the relationship between regression and neural networks. A case study is developed for a neural dynamic occupancy model of North American bird populations, trained on millions of detection/non-detection time series for hundreds of species, providing insights into colonisation and extinction at a continental scale. Flexible models are increasingly needed that scale to large data and represent ecological processes. Neural hierarchical models satisfy this need, providing a bridge between deep learning and ecological modelling that combines the function representation power of neural networks with the inferential capacity of hierarchical models.

A checklist for maximizing reproducibility of ecological niche models

Reporting specific modelling methods and metadata is essential to the reproducibility of ecological studies, yet guidelines rarely exist regarding what information should be noted. Here, we address this issue for ecological niche modelling or species distribution modelling, a rapidly developing toolset in ecology used across many aspects of biodiversity science. Our quantitative review of the recent literature reveals a general lack of sufficient information to fully reproduce the work. Over two-thirds of the examined studies neglected to report the version or access date of the underlying data, and only half reported model parameters. To address this problem, we propose adopting a checklist to guide studies in reporting at least the minimum information necessary for ecological niche modelling reproducibility, offering a straightforward way to balance efficiency and accuracy. We encourage the ecological niche modelling community, as well as journal reviewers and editors, to utilize and further develop this framework to facilitate and improve the reproducibility of future work. The proposed checklist framework is generalizable to other areas of ecology, especially those utilizing biodiversity data, environmental data and statistical modelling, and could also be adopted by a broader array of disciplines.

Integrating experimental and distribution data to predict future species patterns

Predictive species distribution models are mostly based on statistical dependence between environmental and distributional data and therefore may fail to account for physiological limits and biological interactions that are fundamental when modelling species distributions under future climate conditions. Here, we developed a state-of-the-art method integrating biological theory with survey and experimental data in a way that allows us to explicitly model both physical tolerance limits of species and inherent natural variability in regional conditions and thereby improve the reliability of species distribution predictions under future climate conditions. By using a macroalga-herbivore association (Fucus vesiculosus - Idotea balthica) as a case study, we illustrated how salinity reduction and temperature increase under future climate conditions may significantly reduce the occurrence and biomass of these important coastal species. Moreover, we showed that the reduction of herbivore occurrence is linked to reduction of their host macroalgae. Spatial predictive modelling and experimental biology have been traditionally seen as separate fields but stronger interlinkages between these disciplines can improve species distribution projections under climate change. Experiments enable qualitative prior knowledge to be defined and identify cause-effect relationships, and thereby better foresee alterations in ecosystem structure and functioning under future climate conditions that are not necessarily seen in projections based on non-causal statistical relationships alone.

Quantifying spatio-temporal variation of invasion spread

- The spread of invasive species can have far-reaching environmental and ecological consequences. Understanding invasion spread patterns and the underlying process driving invasions are key to predicting and managing invasions. - We combine a set of statistical methods in a novel way to characterize local spread properties and demonstrate their application using simulated and historical data on invasive insects. Our method uses a Gaussian process fit to the surface of waiting times to invasion in order to characterize the vector field of spread. - Using this method, we estimate with statistical uncertainties the speed and direction of spread at each location. Simulations from a stratified diffusion model verify the accuracy of our method. - We show how we may link local rates of spread to environmental covariates for two case studies: the spread of the gypsy moth ( Lymantria dispar), and hemlock woolly adelgid ( Adelges tsugae) in North America. We provide an R-package that automates the calculations for any spatially referenced waiting time data.

A local evaluation of the individual state-space to scale up Bayesian spatial capture-recapture

Spatial capture-recapture models (SCR) are used to estimate animal density and to investigate a range of problems in spatial ecology that cannot be addressed with traditional nonspatial methods. Bayesian approaches in particular offer tremendous flexibility for SCR modeling. Increasingly, SCR data are being collected over very large spatial extents making analysis computational intensive, sometimes prohibitively so. To mitigate the computational burden of large-scale SCR models, we developed an improved formulation of the Bayesian SCR model that uses local evaluation of the individual state-space (LESS). Based on prior knowledge about a species' home range size, we created square evaluation windows that restrict the spatial domain in which an individual's detection probability (detector window) and activity center location (AC window) are estimated. We used simulations and empirical data analyses to assess the performance and bias of SCR with LESS. LESS produced unbiased estimates of SCR parameters when the AC window width was ≥5σ (σ: the scale parameter of the half-normal detection function), and when the detector window extended beyond the edge of the AC window by 2σ. Importantly, LESS considerably decreased the computation time needed for fitting SCR models. In our simulations, LESS increased the computation speed of SCR models up to 57-fold. We demonstrate the power of this new approach by mapping the density of an elusive large carnivore-the wolverine (Gulo gulo)-with an unprecedented resolution and across the species' entire range in Norway (> 200,000 km2). Our approach helps overcome a major computational obstacle to population and landscape-level SCR analyses. The LESS implementation in a Bayesian framework makes the customization and fitting of SCR accessible for practitioners working at scales that are relevant for conservation and management.

Spatial Joint Species Distribution Modeling using Dirichlet Processes

Species distribution models usually attempt to explain presence-absence or abundance of a species at a site in terms of the environmental features (so-called abiotic features) present at the site. Historically, such models have considered species individually. However, it is well-established that species interact to influence presence-absence and abundance (envisioned as biotic factors). As a result, there has been substantial recent interest in joint species distribution models with various types of response, e.g., presence-absence, continuous and ordinal data. Such models incorporate dependence between species response as a surrogate for interaction. The challenge we address here is how to accommodate such modeling in the context of a large number of species (e.g., order 102) across sites numbering on the order of 102 or 103 when, in practice, only a few species are found at any observed site. Again, there is some recent literature to address this; we adopt a dimension reduction approach. The novel wrinkle we add here is spatial dependence. That is, we have a collection of sites over a relatively small spatial region so it is anticipated that species distribution at a given site would be similar to that at a nearby site. Specifically, we handle dimension reduction through Dirichlet processes, enabling clustering of species, joined with spatial dependence across sites through Gaussian processes. We use both simulated data and a plant communities dataset for the Cape Floristic Region (CFR) of South Africa to demonstrate our approach. The latter consists of presence-absence measurements for 639 tree species at 662 locations. Through both data examples we are able to demonstrate improved predictive performance using the foregoing specification.

Black swans in space: modeling spatiotemporal processes with extremes

In ecological systems, extremes can happen in time, such as population crashes, or in space, such as rapid range contractions. However, current methods for joint inference about temporal and spatial dynamics (e.g., spatiotemporal modeling with Gaussian random fields) may perform poorly when underlying processes include extreme events. Here we introduce a model that allows for extremes to occur simultaneously in time and space. Our model is a Bayesian predictive-process GLMM (generalized linear mixed-effects model) that uses a multivariate-t distribution to describe spatial random effects. The approach is easily implemented with our flexible R package glmmfields. First, using simulated data, we demonstrate the ability to recapture spatiotemporal extremes, and explore the consequences of fitting models that ignore such extremes. Second, we predict tree mortality from mountain pine beetle (Dendroctonus ponderosae) outbreaks in the U.S. Pacific Northwest over the last 16 yr. We show that our approach provides more accurate and precise predictions compared to traditional spatiotemporal models when extremes are present. Our R package makes these models accessible to a wide range of ecologists and scientists in other disciplines interested in fitting spatiotemporal GLMMs, with and without extremes.

Applying spatiotemporal models to monitoring data to quantify fish population responses to the Deepwater Horizon oil spill in the Gulf of Mexico

Quantifying the impacts of disturbances such as oil spills on marine species can be challenging. Natural environmental variability, human responses to the disturbance (e.g., fisheries closures), the complex life histories of the species being monitored, and limited pre-spill data can make detection of effects of oil spills difficult. Using long-term monitoring data from the state of Louisiana (USA), we applied novel spatiotemporal approaches to identify anomalies in species occurrence and catch rates. We included covariates (salinity, temperature, turbidity) to help isolate unusual events. While some species showed evidence of unlikely temporal anomalies in occurrence or catch rates, we found that the majority of the observed anomalies were also before the Deepwater Horizon event. Several species-gear combinations suggested upticks in the spatial variability immediately following the spill, but most species indicated no trend. Across species-gear combinations, there was no clear evidence for synchronous or asynchronous responses in occurrence or catch rates across sites following the spill. Our results are in general agreement to other analyses of monitoring data that detected small impacts, but in contrast to recent results from ecological modeling that showed much larger effects of the oil spill on fish and shellfish.

Empirically-based modeling and mapping to consider the co-occurrence of ecological receptors and stressors

Part of the ecological risk assessment process involves examining the potential for environmental stressors and ecological receptors to co-occur across a landscape. In this study, we introduce a Bayesian joint modeling framework for use in evaluating and mapping the co-occurrence of stressors and receptors using empirical data, open-source statistical software, and Geographic Information Systems tools and data. To illustrate the approach, we apply the framework to bioassessment data on stream fishes and nutrients collected from a watershed in southwestern Ohio. The results highlighted the joint model's ability to parse and exploit statistical dependencies in order to provide empirical insight into the potential environmental and ecotoxicological interactions influencing co-occurrence. We also demonstrate how probabilistic predictions can be generated and mapped to visualize spatial patterns in co-occurrences. For practitioners, we believe that this data-driven approach to modeling and mapping co-occurrence can lead to more quantitatively transparent and robust assessments of ecological risk.

Enhanced effects of biotic interactions on predicting multispecies spatial distribution of submerged macrophytes after eutrophication

Water eutrophication creates unfavorable environmental conditions for submerged macrophytes. In these situations, biotic interactions may be particularly important for explaining and predicting the submerged macrophytes occurrence. Here, we evaluate the roles of biotic interactions in predicting spatial occurrence of submerged macrophytes in 1959 and 2009 for Dianshan Lake in eastern China, which became eutrophic since the 1980s. For the four common species occurred in 1959 and 2009, null species distribution models based on abiotic variables and full models based on both abiotic and biotic variables were developed using generalized linear model (GLM) and boosted regression trees (BRT) to determine whether the biotic variables improved the model performance. Hierarchical Bayesian-based joint species distribution models capable of detecting paired biotic interactions were established for each species in both periods to evaluate the changes in the biotic interactions. In most of the GLM and BRT models, the full models showed better performance than the null models in predicting the species presence/absence, and the relative importance of the biotic variables in the full models increased from less than 50% in 1959 to more than 50% in 2009 for each species. Moreover, co-occurrence correlation of each paired species interaction was higher in 2009 than that in 1959. The findings suggest biotic interactions that tend to be positive play more important roles in the spatial distribution of multispecies assemblages of macrophytes and should be included in prediction models to improve prediction accuracy when forecasting macrophytes' distribution under eutrophication stress.

Climate change both facilitates and inhibits invasive plant ranges in New England

Forecasting ecological responses to climate change, invasion, and their interaction must rely on understanding underlying mechanisms. However, such forecasts require extrapolation into new locations and environments. We linked demography and environment using experimental biogeography to forecast invasive and native species' potential ranges under present and future climate in New England, United States to overcome issues of extrapolation in novel environments. We studied two potentially nonequilibrium invasive plants' distributions, Alliaria petiolata (garlic mustard) and Berberis thunbergii (Japanese barberry), each paired with their native ecological analogs to better understand demographic drivers of invasions. Our models predict that climate change will considerably reduce establishment of a currently prolific invader (A. petiolata) throughout New England driven by poor demographic performance in warmer climates. In contrast, invasion of B. thunbergii will be facilitated because of higher growth and germination in warmer climates, with higher likelihood to establish farther north and in closed canopy habitats in the south. Invasion success is in high fecundity for both invasive species and demographic compensation for Apetiolata relative to native analogs. For A. petiolata, simulations suggest that eradication efforts would require unrealistic efficiency; hence, management should focus on inhibiting spread into colder, currently unoccupied areas, understanding source-sink dynamics, and understanding community dynamics should A. petiolata (which is allelopathic) decline. Our results-based on considerable differences with correlative occurrence models typically used for such biogeographic forecasts-suggest the urgency of incorporating mechanism into range forecasting and invasion management to understand how climate change may alter current invasion patterns.

Space-time investigation of the effects of fishing on fish populations

Species distribution models (SDMs) are important statistical tools for obtaining ecological insight into species-habitat relationships and providing advice for natural resource management. Many SDMs have been developed over the past decades, with a focus on space- and more recently, time-dependence. However, most of these studies have been on terrestrial species and applications to marine species have been limited. In this study, we used three large spatio-temporal data sources (habitat maps, survey-based fish density estimates, and fishery catch data) and a novel space-time model to study how the distribution of fishing may affect the seasonal dynamics of a commercially important fish species (Pacific Dover sole, Microstomus pacificus) off the west coast of the USA. Dover sole showed a large scale change in seasonal and annual distribution of biomass, and its distribution shifted from mid-depth zones to inshore or deeper waters during late summer/early fall. In many cases, the scale of fishery removal was small compared to these broader changes in biomass, suggesting that seasonal dynamics were primarily driven by movement and not by fishing. The increasing availability of appropriate data and space-time modeling software should facilitate extending this work to many other species, particularly those in marine ecosystems, and help tease apart the role of growth, natural mortality, recruitment, movement, and fishing on spatial patterns of species distribution in marine systems.

Using spatiotemporal species distribution models to identify temporally evolving hotspots of species co-occurrence

Identifying spatiotemporal hotspots is important for understanding basic ecological processes, but is particularly important for species at risk. A number of terrestrial and aquatic species are indirectly affected by anthropogenic impacts, simply because they tend to be associated with species that are targeted for removals. Using newly developed statistical models that allow for the inclusion of time-varying spatial effects, we examine how the co-occurrence of a targeted and nontargeted species can be modeled as a function of environmental covariates (temperature, depth) and interannual variability. The nontarget species in our case study (eulachon) is listed under the U.S. Endangered Species Act, and is encountered by fisheries off the U.S. West Coast that target pink shrimp. Results from our spatiotemporal model indicated that eulachon bycatch risk decreases with depth and has a convex relationship with sea surface temperature. Additionally, we found that over the 2007-2012 period, there was support for an increase in eulachon density from both a fishery data set (+40%) and a fishery-independent data set (+55%). Eulachon bycatch has increased in recent years, but the agreement between these two data sets implies that increases in bycatch are not due to an increase in incidental targeting of eulachon by fishing vessels, but because of an increasing population size of eulachon. Based on our results, the application of spatiotemporal models to species that are of conservation concern appears promising in identifying the spatial distribution of environmental and anthropogenic risks to the population.

Terrestrial orchids in a tropical forest: best sites for abundance differ from those for reproduction

Suitable habitat for a species is often modeled by linking its distribution patterns with landscape characteristics. However, modeling the relationship between fitness and landscape characteristics is less common. In this study we take a novel approach towards species distribution modeling (SDM) by investigating factors important not only for species occurrence, but also abundance and physical size, as well as fitness measures. We used the Neotropical terrestrial orchid Prescottia stachyodes as our focal species, and compiled geospatial information on habitat and neighboring plants for use in a two-part conditional SDM that accounted for zero inflation and reduced spatial autocorrelation bias. First, we modeled orchid occurrence, and then within suitable sites we contrasted habitat characteristics important for orchid abundance as compared to plant size. We then tested possible fitness implications, informed by analyses of allometric scaling of reproductive effort and lamina area, as well as size-density relationships in areas of P. stachyodes co-occurrence. We determined that orchid presence was based on a combination of biotic and abiotic factors (indicator species, diffuse solar radiation). Within these sites, P. stachyodes abundance was higher on flat terrain, with fine, moderately well-drained soil, and areas without other native orchids, whereas plant size was greater in less rocky areas. In turn, plant size determined reproductive effort, with floral display height proportionate to lamina area (more photosynthates); however, allometric scaling of flower quantity suggests a higher energy cost for production, or maintenance, of flowers. Overall, habitat factors most important for abundance differed from those for size (and thus reproductive effort), suggesting that sites optimal for either recruitment or survival may not be the primary source of seeds. For plots with multiple P. stachyodes plants, size-density relationships differed depending on the size class examined, which may reflect context-dependent population dynamics. Thus, ecological resolution provided by SDM can be enhanced by incorporating abundance and fitness measures.

More than the sum of the parts: forest climate response from joint species distribution models

The perceived threat of climate change is often evaluated from species distribution models that are fitted to many species independently and then added together. This approach ignores the fact that species are jointly distributed and limit one another. Species respond to the same underlying climatic variables, and the abundance of any one species can be constrained by competition; a large increase in one is inevitably linked to declines of others. Omitting this basic relationship explains why responses modeled independently do not agree with the species richness or basal areas of actual forests. We introduce a joint species distribution modeling approach (JSDM), which is unique in three ways, and apply it to forests of eastern North America. First, it accommodates the joint distribution of species. Second, this joint distribution includes both abundance and presence-absence data. We solve the common issue of large numbers of zeros in abundance data by accommodating zeros in both stem counts and basal area data, i.e., a new approach to zero inflation. Finally, inverse prediction can be applied to the joint distribution of predictions to integrate the role of climate risks across all species and identify geographic areas where communities will change most (in terms of changes in abundance) with climate change. Application to forests in the eastern United States shows that climate can have greatest impact in the Northeast, due to temperature, and in the Upper Midwest, due to temperature and precipitation. Thus, these are the regions experiencing the fastest warming and are also identified as most responsive at this scale.

The choice of spatial scales and spatial smoothness priors for various spatial patterns

Given the drawbacks for using geo-political areas in mapping outcomes unrelated to geo-politics, a compromise is to aggregate and analyse data at the grid level. This has the advantage of allowing spatial smoothing and modelling at a biologically or physically relevant scale. This article addresses two consequent issues: the choice of the spatial smoothness prior and the scale of the grid. Firstly, we describe several spatial smoothness priors applicable for grid data and discuss the contexts in which these priors can be employed based on different aims. Two such aims are considered, i.e., to identify regions with clustering and to model spatial dependence in the data. Secondly, the choice of the grid size is shown to depend largely on the spatial patterns. We present a guide on the selection of spatial scales and smoothness priors for various point patterns based on the two aims for spatial smoothing.

The role of biotic interactions in shaping distributions and realised assemblages of species: implications for species distribution modelling

Predicting which species will occur together in the future, and where, remains one of the greatest challenges in ecology, and requires a sound understanding of how the abiotic and biotic environments interact with dispersal processes and history across scales. Biotic interactions and their dynamics influence species' relationships to climate, and this also has important implications for predicting future distributions of species. It is already well accepted that biotic interactions shape species' spatial distributions at local spatial extents, but the role of these interactions beyond local extents (e.g. 10 km(2) to global extents) are usually dismissed as unimportant. In this review we consolidate evidence for how biotic interactions shape species distributions beyond local extents and review methods for integrating biotic interactions into species distribution modelling tools. Drawing upon evidence from contemporary and palaeoecological studies of individual species ranges, functional groups, and species richness patterns, we show that biotic interactions have clearly left their mark on species distributions and realised assemblages of species across all spatial extents. We demonstrate this with examples from within and across trophic groups. A range of species distribution modelling tools is available to quantify species environmental relationships and predict species occurrence, such as: (i) integrating pairwise dependencies, (ii) using integrative predictors, and (iii) hybridising species distribution models (SDMs) with dynamic models. These methods have typically only been applied to interacting pairs of species at a single time, require a priori ecological knowledge about which species interact, and due to data paucity must assume that biotic interactions are constant in space and time. To better inform the future development of these models across spatial scales, we call for accelerated collection of spatially and temporally explicit species data. Ideally, these data should be sampled to reflect variation in the underlying environment across large spatial extents, and at fine spatial resolution. Simplified ecosystems where there are relatively few interacting species and sometimes a wealth of existing ecosystem monitoring data (e.g. arctic, alpine or island habitats) offer settings where the development of modelling tools that account for biotic interactions may be less difficult than elsewhere.