Mismatches between demographic niches and geographic distributions are strongest in poorly dispersed and highly persistent plant species.

The ecological niche of a species describes the variation in population growth rates along environmental gradients that drives geographic range dynamics. Niches are thus central for understanding and forecasting species' geographic distributions. However, theory predicts that migration limitation, source-sink dynamics, and time-lagged local extinction can cause mismatches between niches and geographic distributions. It is still unclear how relevant these niche-distribution mismatches are for biodiversity dynamics and how they depend on species life-history traits. This is mainly due to a lack of the comprehensive, range-wide demographic data needed to directly infer ecological niches for multiple species. Here we quantify niches from extensive demographic measurements along environmental gradients across the geographic ranges of 26 plant species (Proteaceae; South Africa). We then test whether life history explains variation in species' niches and niche-distribution mismatches. Niches are generally wider for species with high seed dispersal or persistence abilities. Life-history traits also explain the considerable interspecific variation in niche-distribution mismatches: poorer dispersers are absent from larger parts of their potential geographic ranges, whereas species with higher persistence ability more frequently occupy environments outside their ecological niche. Our study thus identifies major demographic and functional determinants of species' niches and geographic distributions. It highlights that the inference of ecological niches from geographical distributions is most problematic for poorly dispersed and highly persistent species. We conclude that the direct quantification of ecological niches from demographic responses to environmental variation is a crucial step toward a better predictive understanding of biodiversity dynamics under environmental change.

Transgenerational effects of mild heat in Arabidopsis thaliana show strong genotype specificity that is explained by climate at origin.

Transgenerational environmental effects can trigger strong phenotypic variation. However, it is unclear how cues from different preceding generations interact. Also, little is known about the genetic variation for these life history traits. Here, we present the effects of grandparental and parental mild heat, and their combination, on four traits of the third-generation phenotype of 14 Arabidopsis thaliana genotypes. We tested for correlations of these effects with climate and constructed a conceptual model to identify the environmental conditions that favour the parental effect on flowering time. We observed strong evidence for genotype-specific transgenerational effects. On average, A. thaliana accustomed to mild heat produced more seeds after two generations. Parental effects overruled grandparental effects in all traits except reproductive biomass. Flowering was generally accelerated by all transgenerational effects. Notably, the parental effect triggered earliest flowering in genotypes adapted to dry summers. Accordingly, this parental effect was favoured in the model when early summer heat terminated the growing season and environments were correlated across generations. Our results suggest that A. thaliana can partly accustom to mild heat over two generations and genotype-specific parental effects show non-random evolutionary divergence across populations that may support climate change adaptation in the Mediterranean.

Benchmarking novel approaches for modelling species range dynamics.

Increasing biodiversity loss due to climate change is one of the most vital challenges of the 21st century. To anticipate and mitigate biodiversity loss, models are needed that reliably project species' range dynamics and extinction risks. Recently, several new approaches to model range dynamics have been developed to supplement correlative species distribution models (SDMs), but applications clearly lag behind model development. Indeed, no comparative analysis has been performed to evaluate their performance. Here, we build on process-based, simulated data for benchmarking five range (dynamic) models of varying complexity including classical SDMs, SDMs coupled with simple dispersal or more complex population dynamic models (SDM hybrids), and a hierarchical Bayesian process-based dynamic range model (DRM). We specifically test the effects of demographic and community processes on model predictive performance. Under current climate, DRMs performed best, although only marginally. Under climate change, predictive performance varied considerably, with no clear winners. Yet, all range dynamic models improved predictions under climate change substantially compared to purely correlative SDMs, and the population dynamic models also predicted reasonable extinction risks for most scenarios. When benchmarking data were simulated with more complex demographic and community processes, simple SDM hybrids including only dispersal often proved most reliable. Finally, we found that structural decisions during model building can have great impact on model accuracy, but prior system knowledge on important processes can reduce these uncertainties considerably. Our results reassure the clear merit in using dynamic approaches for modelling species' response to climate change but also emphasize several needs for further model and data improvement. We propose and discuss perspectives for improving range projections through combination of multiple models and for making these approaches operational for large numbers of species.

The influence of interspecific interactions on species range expansion rates.

Ongoing and predicted global change makes understanding and predicting species' range shifts an urgent scientific priority. Here, we provide a synthetic perspective on the so far poorly understood effects of interspecific interactions on range expansion rates. We present theoretical foundations for how interspecific interactions may modulate range expansion rates, consider examples from empirical studies of biological invasions and natural range expansions as well as process-based simulations, and discuss how interspecific interactions can be more broadly represented in process-based, spatiotemporally explicit range forecasts. Theory tells us that interspecific interactions affect expansion rates via alteration of local population growth rates and spatial displacement rates, but also via effects on other demographic parameters. The best empirical evidence for interspecific effects on expansion rates comes from studies of biological invasions. Notably, invasion studies indicate that competitive dominance and release from specialized enemies can enhance expansion rates. Studies of natural range expansions especially point to the potential for competition from resident species to reduce expansion rates. Overall, it is clear that interspecific interactions may have important consequences for range dynamics, but also that their effects have received too little attention to robustly generalize on their importance. We then discuss how interspecific interactions effects can be more widely incorporated in dynamic modeling of range expansions. Importantly, models must describe spatiotemporal variation in both local population dynamics and dispersal. Finally, we derive the following guidelines for when it is particularly important to explicitly represent interspecific interactions in dynamic range expansion forecasts: if most interacting species show correlated spatial or temporal trends in their effects on the target species, if the number of interacting species is low, and if the abundance of one or more strongly interacting species is not closely linked to the abundance of the target species.

Data Integration for Large-Scale Models of Species Distributions.

With the expansion in the quantity and types of biodiversity data being collected, there is a need to find ways to combine these different sources to provide cohesive summaries of species' potential and realized distributions in space and time. Recently, model-based data integration has emerged as a means to achieve this by combining datasets in ways that retain the strengths of each. We describe a flexible approach to data integration using point process models, which provide a convenient way to translate across ecological currencies. We highlight recent examples of large-scale ecological models based on data integration and outline the conceptual and technical challenges and opportunities that arise.

Importance and effectiveness of correction methods for spatial sampling bias in species with sex-specific habitat preference.

AIM: Presence records from surveys with spatially heterogeneous sampling intensity are a key challenge for species distribution models (SDMs). When sex groups differ in their habitat association, the correction of the spatial bias becomes important for preventing model predictions that are biased toward one sex. The objectives of this study were to investigate the effectiveness of existing correction methods for spatial sampling bias for SDMs when male and female have different habitat preferences. LOCATION: Jura massif, France. METHODS: We used a spatially sex-segregated virtual species to understand the effect of three sampling designs (spatially biased, uniform random, and systematic), and two correction methods (targeted background points, and distance to trajectories) on estimated habitat preferences, sex ratios, and prediction accuracy. We then evaluated these effects for two empirical Capercaillie (Tetrao urogallus) presence-only datasets from a systematic and a spatially biased sampling design. RESULTS: Sampling design strongly affected parameter estimation accuracy for the virtual species: noncorrected spatially biased sampling resulted in biased estimates of habitat association and sex ratios. Both established methods of bias correction were successful in the case of virtual species, with the targeted correction methods showing stronger correction, as it more closely followed the simulated decay of detectability with distance from sampling locations. On the Capercaillie dataset, only the targeted background points method resulted in the same sex ratio estimate for the spatially biased sampling design as for the spatially unbiased sampling. MAIN CONCLUSIONS: We suggest that information on subgroups with distinct habitat associations should be included in SDMs analyses when possible. We conclude that current methods for correcting spatially biased sampling can improve estimates of both habitat association and subgroup ratios (e.g., sex and age), but that their efficiency depends on their ability to well represent the spatial observation bias.

Annual plants under cyclic disturbance regime: better understanding through model aggregation.

In their application for conservation ecology, "classical" analytical models and individual-based simulation models (IBMs) both entail their specific strengths and weaknesses, either in providing a detailed and realistic representation of processes or in regard to a comprehensive model analysis. This well-known dilemma may be resolved by the combination of both approaches when tackling certain problems of conservation ecology. Following this idea, we present the complementary use of both an IBM and a matrix population model in a case study on grassland conservation management. First, we develop a spatially explicit IBM to simulate the long-term response of the annual plant Thlaspi perfoliatum (Brassicaceae), claspleaf pennycress, to different management schemes (annual mowing vs. infrequent rototilling) based on field experiments. In order to complement the simulation results by further analyses, we aggregate the IBM to a spatially nonexplicit deterministic matrix population model. Within the periodic environment created by management regimes, population dynamics are described by periodic products of annual transition matrices. Such periodic matrix products provide a very conclusive framework to study the responses of species to different management return intervals. Thus, using tools of matrix model analysis (e.g., loop analysis), we can both identify dormancy within the age-structured seed bank as the pivotal strategy for persistence under cyclic disturbance regimes and reveal crucial thresholds in some less certain parameters. Results of matrix model analyses are therefore successfully tested by comparing their results to the respective IBM simulations. Their implications for an enhanced scientific basis for management decisions are discussed as well as some general benefits and limitations of the use of aggregating modeling approaches in conservation.