Predicting spatial patterns of plant species richness: a comparison of direct macroecological and species stacking modelling approaches

Aim This study compares the direct, macroecological approach (MEM) for modelling species richness (SR) with the more recent approach of stacking predictions from individual species distributions (S‐SDM). We implemented both approaches on the same dataset and discuss their respective theoretical assumptions, strengths and drawbacks. We also tested how both approaches performed in reproducing observed patterns of SR along an elevational gradient. Location Two study areas in the Alps of Switzerland. Methods We implemented MEM by relating the species counts to environmental predictors with statistical models, assuming a Poisson distribution. S‐SDM was implemented by modelling each species distribution individually and then stacking the obtained prediction maps in three different ways – summing binary predictions, summing random draws of binomial trials and summing predicted probabilities – to obtain a final species count. Results The direct MEM approach yields nearly unbiased predictions centred around the observed mean values, but with a lower correlation between predictions and observations, than that achieved by the S‐SDM approaches. This method also cannot provide any information on species identity and, thus, community composition. It does, however, accurately reproduce the hump‐shaped pattern of SR observed along the elevational gradient. The S‐SDM approach summing binary maps can predict individual species and thus communities, but tends to overpredict SR. The two other S‐SDM approaches – the summed binomial trials based on predicted probabilities and summed predicted probabilities – do not overpredict richness, but they predict many competing end points of assembly or they lose the individual species predictions, respectively. Furthermore, all S‐SDM approaches fail to appropriately reproduce the observed hump‐shaped patterns of SR along the elevational gradient. Main conclusions Macroecological approach and S‐SDM have complementary strengths. We suggest that both could be used in combination to obtain better SR predictions by following the suggestion of constraining S‐SDM by MEM predictions.     

Can niche‐based distribution models outperform spatial interpolation?

Aim Distribution modelling relates sparse data on species occurrence or abundance to environmental information to predict the population of a species at any point in space. Recently, the importance of spatial autocorrelation in distributions has been recognized. Spatial autocorrelation can be categorized as exogenous (stemming from autocorrelation in the underlying variables) or endogenous (stemming from activities of the organism itself, such as dispersal). Typically, one asks whether spatial models explain additional variability (endogenous) in comparison to a fully specified habitat model. We turned this question around and asked: can habitat models explain additional variation when spatial structure is accounted for in a fully specified spatially explicit model? The aim was to find out to what degree habitat models may be inadvertently capturing spatial structure rather than true explanatory mechanisms. Location We used data from 190 species of the North American Breeding Bird Survey covering the conterminous United States and southern Canada. Methods We built 13 different models on 190 bird species using regression trees. Our habitat‐based models used climate and landcover variables as independent variables. We also used random variables and simulated ranges to validate our results. The two spatially explicit models included only geographical coordinates or a contagion term as independent variables. As another angle on the question of mechanism vs. spatial structure we pitted a model using related bird species as predictors against a model using randomly selected bird species. Results The spatially explicit models outperformed the traditional habitat models and the random predictor species outperformed the related predictor species. In addition, environmental variables produced a substantial R² in predicting artificial ranges. Main conclusions We conclude that many explanatory variables with suitable spatial structure can work well in species distribution models. The predictive power of environmental variables is not necessarily mechanistic, and spatial interpolation can outperform environmental explanatory variables.     

Measuring biodiversity to explain community assembly: a unified approach

One of the oldest challenges in ecology is to understand the processes that underpin the composition of communities. Historically, an obvious way in which to describe community compositions has been diversity in terms of the number and abundances of species. However, the failure to reject contradictory models has led to communities now being characterized by trait and phylogenetic diversities. Our objective here is to demonstrate how species, trait and phylogenetic diversity can be combined together from large to local spatial scales to reveal the historical, deterministic and stochastic processes that impact the compositions of local communities. Research in this area has recently been advanced by the development of mathematical measures that incorporate trait dissimilarities and phylogenetic relatedness between species. However, measures of trait diversity have been developed independently of phylogenetic measures and conversely most of the phylogenetic diversity measures have been developed independently of trait diversity measures. This has led to semantic confusions particularly when classical ecological and evolutionary approaches are integrated so closely together. Consequently, we propose a unified semantic framework and demonstrate the importance of the links among species, phylogenetic and trait diversity indices. Furthermore, species, trait and phylogenetic diversity indices differ in the ways they can be used across different spatial scales. The connections between large‐scale, regional and local processes allow the consideration of historical factors in addition to local ecological deterministic or stochastic processes. Phylogenetic and trait diversity have been used in large‐scale analyses to determine how historical and/or environmental factors affect both the formation of species assemblages and patterns in species richness across latitude or elevation gradients. Both phylogenetic and trait diversity have been used at different spatial scales to identify the relative impacts of ecological deterministic processes such as environmental filtering and limiting similarity from alternative processes such as random speciation and extinction, random dispersal and ecological drift. Measures of phylogenetic diversity combine phenotypic and genetic diversity and have the potential to reveal both the ecological and historical factors that impact local communities. Consequently, we demonstrate that, when used in a comparative way, species, trait and phylogenetic structures have the potential to reveal essential details that might act simultaneously in the assembly of species communities. We highlight potential directions for future research. These might include how variation in trait and phylogenetic diversity alters with spatial distances, the role of trait and phylogenetic diversity in global‐scale gradients, the connections between traits and phylogeny, the importance of trait rarity and independent evolutionary history in community assembly, the loss of trait and phylogenetic diversity due to human impacts, and the mathematical developments of biodiversity indices including within‐species variations.     

Incorporating spatial autocorrelation into species distribution models alters forecasts of climate‐mediated range shifts

Species distribution models (SDMs) are widely used to forecast changes in the spatial distributions of species and communities in response to climate change. However, spatial autocorrelation (SA) is rarely accounted for in these models, despite its ubiquity in broad‐scale ecological data. While spatial autocorrelation in model residuals is known to result in biased parameter estimates and the inflation of type I errors, the influence of unmodeled SA on species' range forecasts is poorly understood. Here we quantify how accounting for SA in SDMs influences the magnitude of range shift forecasts produced by SDMs for multiple climate change scenarios. SDMs were fitted to simulated data with a known autocorrelation structure, and to field observations of three mangrove communities from northern Australia displaying strong spatial autocorrelation. Three modeling approaches were implemented: environment‐only models (most frequently applied in species' range forecasts), and two approaches that incorporate SA; autologistic models and residuals autocovariate (RAC) models. Differences in forecasts among modeling approaches and climate scenarios were quantified. While all model predictions at the current time closely matched that of the actual current distribution of the mangrove communities, under the climate change scenarios environment‐only models forecast substantially greater range shifts than models incorporating SA. Furthermore, the magnitude of these differences intensified with increasing increments of climate change across the scenarios. When models do not account for SA, forecasts of species' range shifts indicate more extreme impacts of climate change, compared to models that explicitly account for SA. Therefore, where biological or population processes induce substantial autocorrelation in the distribution of organisms, and this is not modeled, model predictions will be inaccurate. These results have global importance for conservation efforts as inaccurate forecasts lead to ineffective prioritization of conservation activities and potentially to avoidable species extinctions.     

Ectomycorrhizal fungal diversity and saprotrophic fungal diversity are linked to different tree community attributes in a field‐based tree experiment

Exploring the link between above‐ and belowground biodiversity has been a major theme of recent ecological research, due in large part to the increasingly well‐recognized role that soil microorganisms play in driving plant community processes. In this study, we utilized a field‐based tree experiment in Minnesota, USA, to assess the effect of changes in plant species richness and phylogenetic diversity on the richness and composition of both ectomycorrhizal and saprotrophic fungal communities. We found that ectomycorrhizal fungal species richness was significantly positively influenced by increasing plant phylogenetic diversity, while saprotrophic fungal species richness was significantly affected by plant leaf nitrogen content, specific root length and standing biomass. The increasing ectomycorrhizal fungal richness associated with increasing plant phylogenetic diversity was driven by the combined presence of ectomycorrhizal fungal specialists in plots with both gymnosperm and angiosperm hosts. Although the species composition of both the ectomycorrhizal and saprotrophic fungal communities changed significantly in response to changes in plant species composition, the effect was much greater for ectomycorrhizal fungi. In addition, ectomycorrhizal but not saprotrophic fungal species composition was significantly influenced by both plant phylum (angiosperm, gymnosperm, both) and origin (Europe, America, both). The phylum effect was caused by differences in ectomycorrhizal fungal community composition, while the origin effect was attributable to differences in community heterogeneity. Taken together, this study emphasizes that plant‐associated effects on soil fungal communities are largely guild‐specific and provides a mechanistic basis for the positive link between plant phylogenetic diversity and ectomycorrhizal fungal richness.     

Twenty‐five years of plant community dynamics and invasion in New Zealand tussock grasslands

Understanding how plant communities respond to plant invasions is important both for understanding community structure and for predicting future ecosystem change. In a system undergoing intense plant invasion for 25 years, we investigated patterns of community change at a regional scale. Specifically, we sought to quantify how tussock grassland plant community structure had changed and whether changes were related to increases in plant invasion. Frequency data for all vascular plants were recorded on 124, permanent transects in tussock grasslands across the lower eastern South Island of New Zealand measured three times over a period of 25 years. Multivariate analyses of species richness were used to describe spatial and temporal patterns in the vegetation. Linear mixed‐effects models were used to relate temporal changes in community structure to the level and rate of invasion of three dominant invasive species in the genus Hieracium while accounting for relationships with other biotic and abiotic variables. There was a strong compositional gradient from exotic‐ to native‐dominated plant communities that correlated with increasing elevation. Over the 25 years, small‐scale species richness significantly decreased and then increased again; however, these changes differed in different plant communities. Exotic species frequency consistently increased on some transects and consistently declined on others. Species richness changes were correlated with the level of Hieracium invasion and abiotic factors, although the relationship with Hieracium changed from negative to positive over time. Compositional changes were not related to measured predictors. Our results suggest that observed broad‐scale fluctuations in species richness and community composition dynamics were not driven by Hieracium invasion. Given the relatively minor changes in community composition over time, we conclude that there is no evidence for widespread degradation of these grasslands over the last 25 years. However, because of continuing weed invasion, particularly at lower elevations, impacts may emerge in the longer term.     

Predicting the future of forests in the Mediterranean under climate change,with niche‐ and process‐based models: CO2 matters!

Assessing the potential future of current forest stands is a key to design conservation strategies and understanding potential future impacts to ecosystem service supplies. This is particularly true in the Mediterranean basin, where important future climatic changes are expected. Here, we assess and compare two commonly used modeling approaches (niche‐ and process‐based models) to project the future of current stands of three forest species with contrasting distributions, using regionalized climate for continental Spain. Results highlight variability in model ability to estimate current distributions, and the inherent large uncertainty involved in making projections into the future. CO₂ fertilization through projected increased atmospheric CO₂ concentrations is shown to increase forest productivity in the mechanistic process‐based model (despite increased drought stress) by up to three times that of the non‐CO₂ fertilization scenario by the period 2050–2080, which is in stark contrast to projections of reduced habitat suitability from the niche‐based models by the same period. This highlights the importance of introducing aspects of plant biogeochemistry into current niche‐based models for a realistic projection of future species distributions. We conclude that the future of current Mediterranean forest stands is highly uncertain and suggest that a new synergy between niche‐ and process‐based models is urgently needed in order to improve our predictive ability.     

Regression models for spatial prediction: their role for biodiversity and conservation

This paper is an introduction to a Special Issue on regression models for spatial predictions published in Biodiversity and Conservation following an international workshop held in Switzerland in 2001 (http://leba.unige.ch/workshop). This introduction describes how the exponential growth in computing power has improved our ability to reach spatially explicit assessment of biodiversity and to develop cost-effective conservation management. New questions arising from these modern approaches are listed, while papers presenting examples of applications are briefly introduced.     

Land use improves spatial predictions of mountain plant abundance but not presence‐absence

Question: Does a land‐use variable improve spatial predictions of plant species presence‐absence and abundance models at the regional scale in a mountain landscape? Location: Western Swiss Alps. Methods: Presence‐absence generalized linear models (GLM) and abundance ordinal logistic regression models (LRM) were fitted to data on 78 mountain plant species, with topo‐climatic and/or land‐use variables available at a 25‐m resolution. The additional contribution of land use when added to topo‐climatic models was evaluated by: (1) assessing the changes in model fit and (2) predictive power, (3) partitioning the deviance respectively explained by the topo‐climatic variables and the land‐use variable through variation partitioning, and (5) comparing spatial projections. Results: Land use significantly improved the fit of presence‐absence models but not their predictive power. In contrast, land use significantly improved both the fit and predictive power of abundance models. Variation partitioning also showed that the individual contribution of land use to the deviance explained by presence‐absence models was, on average, weak for both GLM and LRM (3.7% and 4.5%, respectively), but changes in spatial projections could nevertheless be important for some species. Conclusions: In this mountain area and at our regional scale, land use is important for predicting abundance, but not presence‐absence. The importance of adding land‐use information depends on the species considered. Even without a marked effect on model fit and predictive performance, adding land use can affect spatial projections of both presence‐absence and abundance models.     

Patterns of species richness hotspots and estimates of their protection are sensitive to spatial resolution

Aim

Species richness is a measure of biodiversity often used in spatial conservation assessments and mapped by summing species distribution maps. Commission errors inherent those maps influence richness patterns and conservation assessments. We sought to further the understanding of the sensitivity of hotspot delineation methods and conservation assessments to commission errors, and choice of threshold for hotspot delineation.

Location

United States.

Methods

We created range maps and 30‐m and 1‐km resolution habitat maps for terrestrial vertebrates in the United States and generated species richness maps with each dataset. With the richness maps and the GAP Protected Areas Dataset, we created species richness hotspot maps and calculated the proportion of hotspots within protected areas; calculating protection under a range of thresholds for defining hotspots. Our method allowed us to identify the influence of commission errors by comparing hotspot maps.

Results

Commission errors from coarse spatial grain data and lack of porosity in the range data inflated richness estimates and altered their spatial patterns. Coincidence of hotspots from different data types was low. The 30‐m hotspots were spatially dispersed, and some were very long distances from the hotspots mapped with coarser data. Estimates of protection were low for each of the taxa. The relationship between estimates of hotspot protection and threshold choice was nonlinear and inconsistent among data types (habitat and range) and grain size (30‐m and 1‐km).

Main conclusions

Coarse mapping methods and grain sizes can introduce commission errors into species distribution data that could result in misidentifications of the regions where hotspots occur and affect estimates of hotspot protection. Hotspot conservation assessments are also sensitive to choice of threshold for hotspot delineation. There is value in developing species distribution maps with high resolution and low rates of commission error for conservation assessments.     

Predictive modeling for allopatric Strix (Strigiformes: Strigidae) owls in South America: determinants of their distributions and ecological niche‐based processes

Strix (Strigidae) is a worldwide genus of 17 owl species typical of forested habitats, including Rusty‐barred Owls (S. hylophila), Chaco Owls (S. chacoensis), and Rufous‐legged Owls (S. rufipes) in South America. These species are distributed allopatrically, but the ecological traits that determine their distributions remain largely unknown and their phylogenetic relationships are unclear. We used species distribution models (SDMs) to identify variables explaining their distribution patterns and test hypotheses about ecological divergence and conservatism based on niche overlap analysis. For Rusty‐barred Owls and Chaco Owls, climatic factors related to temperature played a major role, whereas a rainfall variable was more important for Rufous‐legged Owls. When niche overlaps were compared, accounting for regional similarities in the habitat available to each species, an ecological niche divergence process was supported for Chaco Owl‐Rusty‐barred Owl and Chaco Owl‐Rufous‐legged Owl, whereas a niche conservatism process was supported for Rusty‐barred Owl‐Rufous‐legged Owl. Different ecological requirements support current species delimitation, but they are in disagreement with the two main hypotheses currently envisaged about their phylogenetic relationships (Chaco Owls as the sister taxa of either Rufous‐legged Owls or Rusty‐barred Owls) and support a new phylogenetic hypothesis (Rufous‐legged Owls as sister taxa of Rusty‐barred Owls). Our findings suggest that speciation of Rusty‐barred Owls and Rufous‐legged Owls was a vicariant event resulting from Atlantic marine transgressions in southern South America in the Miocene, but their niche was conserved because habitat changed little in their respective ranges. In contrast, Chaco Owls diverged ecologically from the other two species as a result of their adaptations to the habitat they currently occupy. Ecological and historical approaches in biogeography can be embedded to explain distribution patterns, and results provided by SDMs can be used to infer historical and ecological processes in an integrative way.     

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.