Multiresponse algorithms for community‐level modelling: Review of theory, applications, and comparison to species distribution models

Predicting the responses of European grassland communities to climate and land cover change

European grasslands are among the most species-rich ecosystems on small spatial scales. However, human-induced activities like land use and climate change pose significant threats to this diversity. To explore how climate and land cover change will affect biodiversity and community composition in grassland ecosystems, we conducted joint species distribution models (SDMs) on the extensive vegetation-plot database sPlotOpen to project distributions of 1178 grassland species across Europe under current conditions and three future scenarios. We further compared model accuracy and computational efficiency between joint SDMs (JSDMs) and stacked SDMs, especially for rare species. Our results show that: (i) grassland communities in the mountain ranges are expected to suffer high rates of species loss, while those in western, northern and eastern Europe will experience substantial turnover; (ii) scaling anomalies were observed in the predicted species richness, reflecting regional differences in the dominant drivers of assembly processes; (iii) JSDMs did not outperform stacked SDMs in predictive power but demonstrated superior efficiency in model fitting and predicting; and (iv) incorporating co-occurrence datasets improved the model performance in predicting the distribution of rare species. This article is part of the theme issue 'Ecological novelty and planetary stewardship: biodiversity dynamics in a transforming biosphere'.

Space-for-time substitutions in climate change ecology and evolution

In an epoch of rapid environmental change, understanding and predicting how biodiversity will respond to a changing climate is an urgent challenge. Since we seldom have sufficient long-term biological data to use the past to anticipate the future, spatial climate-biotic relationships are often used as a proxy for predicting biotic responses to climate change over time. These 'space-for-time substitutions' (SFTS) have become near ubiquitous in global change biology, but with different subfields largely developing methods in isolation. We review how climate-focussed SFTS are used in four subfields of ecology and evolution, each focussed on a different type of biotic variable - population phenotypes, population genotypes, species' distributions, and ecological communities. We then examine the similarities and differences between subfields in terms of methods, limitations and opportunities. While SFTS are used for a wide range of applications, two main approaches are applied across the four subfields: spatial in situ gradient methods and transplant experiments. We find that SFTS methods share common limitations relating to (i) the causality of identified spatial climate-biotic relationships and (ii) the transferability of these relationships, i.e. whether climate-biotic relationships observed over space are equivalent to those occurring over time. Moreover, despite widespread application of SFTS in climate change research, key assumptions remain largely untested. We highlight opportunities to enhance the robustness of SFTS by addressing key assumptions and limitations, with a particular emphasis on where approaches could be shared between the four subfields.

A two-species distribution model for parapatric newts, with inferences on their history of spatial replacement

Related species often engage in abutting or overlapping contact zones with various strengths of interspecific competition. Biotic interactions such as these preclude the registration of the full profile of environmental variables that would describe the otherwise larger species ranges. Here, I advocate to forego full range species distribution modelling and instead focus on the ecography of the contact zone, for example with ‘two-species distribution models’ (TSDMs), in which presence data are contrasted against the background of environmental data. The newts Triturus cristatus and Triturus marmoratus meet in the west of France. A countrywide TSDM suggests that the contact zone of the species is located at a climatic gradient, in line with their north-eastern vs. south-western ranges. The species are also ecologically segregated by elevation and forestation, which is in line with a documented movement of the contact zone caused by hedgerow removal in lowland areas. Hindcasts for the Holocene suggest that the species contact zone was positioned at either the same place as at present or more to the south, depending on the amount of forestation. A forecast under climate warming predicts a fast movement to the north, but this scenario is deemed unrealistic. One reason is that recent habitat loss compromises dispersal and range expansion. Other species pairs to which TSDMs have been applied are listed for comparison.

Modeling demersal fish and benthic invertebrate assemblages in support of marine conservation planning

Marine classification schemes based on abiotic surrogates often inform regional marine conservation planning in lieu of detailed biological data. However, these schemes may poorly represent ecologically relevant biological patterns required for effective design and management strategies. We used a community-level modeling approach to characterize and delineate representative mesoscale (tens to thousands of kilometers) assemblages of demersal fish and benthic invertebrates in the Northwest Atlantic. Hierarchical clustering of species occurrence data from four regional annual multispecies trawl surveys revealed three to six groupings (predominant assemblage types) in each survey region, broadly associated with geomorphic and oceanographic features. Indicator analyses identified 3-34 emblematic taxa of each assemblage type. Random forest classifications accurately predicted assemblage distributions from environmental covariates (AUC > 0.95) and identified thermal limits (annual minimum and maximum bottom temperatures) as important predictors of distribution in each region. Using forecasted oceanographic conditions for the year 2075 and a regional classification model, we projected assemblage distributions in the southernmost bioregion (Scotian Shelf-Bay of Fundy) under a high emissions climate scenario (RCP 8.5). Range expansions to the northeast are projected for assemblages associated with warmer and shallower waters of the Western Scotian Shelf over the 21st century as thermal habitat on the relatively cooler Eastern Scotian Shelf becomes more favorable. Community-level modeling provides a biotic-informed approach for identifying broadscale ecological structure required for the design and management of ecologically coherent, representative, well-connected networks of Marine Protected Areas. When combined with oceanographic forecasts, this modeling approach provides a spatial tool for assessing sensitivity and resilience to climate change, which can improve conservation planning, monitoring, and adaptive management.

Landscape analyses using eDNA metabarcoding and Earth observation predict community biodiversity in California

Ecosystems globally are under threat from ongoing anthropogenic environmental change. Effective conservation management requires more thorough biodiversity surveys that can reveal system-level patterns and that can be applied rapidly across space and time. Using modern ecological models and community science, we integrate environmental DNA and Earth observations to produce a time snapshot of regional biodiversity patterns and provide multi-scalar community-level characterization. We collected 278 samples in spring 2017 from coastal, shrub, and lowland forest sites in California, a complex ecosystem and biodiversity hotspot. We recovered 16,118 taxonomic entries from eDNA analyses and compiled associated traditional observations and environmental data to assess how well they predicted alpha, beta, and zeta diversity. We found that local habitat classification was diagnostic of community composition and distinct communities and organisms in different kingdoms are predicted by different environmental variables. Nonetheless, gradient forest models of 915 families recovered by eDNA analysis and using BIOCLIM variables, Sentinel-2 satellite data, human impact, and topographical features as predictors, explained 35% of the variance in community turnover. Elevation, sand percentage, and photosynthetic activities (NDVI32) were the top predictors. In addition to this signal of environmental filtering, we found a positive relationship between environmentally predicted families and their numbers of biotic interactions, suggesting environmental change could have a disproportionate effect on community networks. Together, these analyses show that coupling eDNA with environmental predictors including remote sensing data has capacity to test proposed Essential Biodiversity Variables and create new landscape biodiversity baselines that span the tree of life.

On the Interpretations of Joint Modeling in Community Ecology

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

Improving prediction of rare species' distribution from community data

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

Trait-Based Assessments of Climate-Change Impacts on Interacting Species

Plant-animal interactions are fundamentally important in ecosystems, but have often been ignored by studies of climate-change impacts on biodiversity. Here, we present a trait-based framework for predicting the responses of interacting plants and animals to climate change. We distinguish three pathways along which climate change can impact interacting species in ecological communities: (i) spatial and temporal mismatches in the occurrence and abundance of species, (ii) the formation of novel interactions and secondary extinctions, and (iii) alterations of the dispersal ability of plants. These pathways are mediated by three kinds of functional traits: response traits, matching traits, and dispersal traits. We propose that incorporating these traits into predictive models will improve assessments of the responses of interacting species to climate change.

A pathway for multivariate analysis of ecological communities using copulas

We describe a new pathway for multivariate analysis of data consisting of counts of species abundances that includes two key components: copulas, to provide a flexible joint model of individual species, and dissimilarity-based methods, to integrate information across species and provide a holistic view of the community. Individual species are characterized using suitable (marginal) statistical distributions, with the mean, the degree of over-dispersion, and/or zero-inflation being allowed to vary among a priori groups of sampling units. Associations among species are then modeled using copulas, which allow any pair of disparate types of variables to be coupled through their cumulative distribution function, while maintaining entirely the separate individual marginal distributions appropriate for each species. A Gaussian copula smoothly captures changes in an index of association that excludes joint absences in the space of the original species variables. A permutation-based filter with exact family-wise error can optionally be used a priori to reduce the dimensionality of the copula estimation problem. We describe in detail a Monte Carlo expectation maximization algorithm for efficient estimation of the copula correlation matrix with discrete marginal distributions (counts). The resulting fully parameterized copula models can be used to simulate realistic ecological community data under fully specified null or alternative hypotheses. Distributions of community centroids derived from simulated data can then be visualized in ordinations of ecologically meaningful dissimilarity spaces. Multinomial mixtures of data drawn from copula models also yield smooth power curves in dissimilarity-based settings. Our proposed analysis pathway provides new opportunities to combine model-based approaches with dissimilarity-based methods to enhance understanding of ecological systems. We demonstrate implementation of the pathway through an ecological example, where associations among fish species were found to increase after the establishment of a marine reserve.

Detection and Control of Invasive Freshwater Crayfish: From Traditional to Innovative Methods

Invasive alien species are widespread in freshwater systems compared to terrestrial ecosystems. Among crustaceans, crayfish in particular have been widely introduced and are considered a major threat to freshwater ecosystem functioning. New emerging techniques for detecting and controlling invasive crayfish and protecting endangered native species are; thus, now highly desirable and several are under evaluation. Important innovations have been developed in recent years for detection of both invasive and native crayfish, mainly through eDNA, which allows for the detection of the target species even at low abundance levels and when not directly observable. Forecasting models have also moved towards the creation of realistic invasion scenarios, allowing effective management plans to be developed in advance of invasions. The importance of monitoring the spread and impacts of crayfish and pathogens in developing national data and research networks is emphasised; here “citizen science” can also play a role. Emerging techniques are still being considered in the field of invasive crayfish control. Although for decades the main traditional techniques to manage invasive crayfish were solely based on trapping, since 2010 biological, biocidal, autocidal controls and sexual attractants, monosex populations, RNA interference, the sterile male release technique and oral delivery have all also been investigated for crayfish control. In this review, ongoing methodologies applied to the detection and management of invasive crayfish are discussed, highlighting their benefits and limitations.

Predicting New Zealand riverine fish reference assemblages

Biomonitoring is a common method to monitor environmental change in river ecosystems, a key advantage of biomonitoring over snap-shot physicochemical monitoring is that it provides a more stable, long-term insight into change that is also effects-based. In New Zealand, the main biomonitoring method is a macroinvertebrate sensitivity scoring index, with little established methods available for biomonitoring of fish. This study models the contemporary distribution of common freshwater fish and then uses those models to predict freshwater fish assemblages for each river reach under reference conditions. Comparison of current fish assemblages with those predicted in reference conditions (as observed/expected (O/E) ratios) may provide a suitable option for freshwater fish biomonitoring. Most of the fish communities throughout the central North Island and lower reaches show substantial deviation from the modelled reference community. Most of this deviation is explained by nutrient enrichment, followed by downstream barriers (i.e. dams) and loss of riparian vegetation. The presence of modelled introduced species had relatively little impact on the presence of the modelled native fish. The maps of O/E fish assemblage may provide a rapid way to identify potential restoration sites.