Microsatellite DNA analyses of a highly disjunct New Zealand tree reveal strong differentiation and imply a formerly more continuous distribution

Although New Zealand is a biodiversity hotspot, there has been little genetic investigation of why so many of its threatened and uncommon plants have naturally disjunct distributions. We investigated the small tree Pseudopanax ferox (Araliaceae), which has a widespread but highly disjunct lowland distribution within New Zealand. Genotyping of nuclear microsatellites and a chloroplast locus revealed pronounced genetic differentiation and four principal genetic clusters. Our results indicate that the disjunct distribution is a product of vicariance rather than long‐distance dispersal. This highlights the need to preserve multiple populations when disjunct distributions are the result of vicariance, rather than focusing conservation efforts on a core area, in order to retain as much as possible of a species’ evolutionary legacy and potential. Additionally, based on our genetic findings and the ecology of P. ferox, we hypothesize that it was more continuously distributed during the drier (but not maximally colder) interstadials of glacial periods and/or on the fertile soils available immediately postglacial. We further hypothesize that P. ferox belongs to a suite of species of drought‐prone and/or fertile habitats whose distributions are actually restricted during warmer and wetter interglacial periods, despite being principally of the lowlands. Our genetic data for P. ferox are also the first consistent with the survival during the Last Glacial Maxima of a lowland tree at high latitudes in the south‐eastern South Island.     

Genetic diversity,population structure and sex‐biased dispersal in three co‐evolving species

Genetic diversity and spatial structure of populations are important for antagonistic coevolution. We investigated genetic variation and population structure of three closely related European ant species: the social parasite Harpagoxenus sublaevis and its two host species Leptothorax acervorum and Leptothorax muscorum. We sampled populations in 12 countries and analysed eight microsatellite loci and an mtDNA sequence. We found high levels of genetic variation in all three species, only slightly less variation in the host L. muscorum. Using a newly introduced measure of differentiation (Jost’s D_est ), we detected strong population structuring in all species and less male‐biased dispersal than previously thought. We found no phylogeographic patterns that could give information on post‐glacial colonization routes – northern populations are as variable as more southern populations. We conclude that conditions for Thompson’s geographic mosaic of coevolution are ideal in this system: all three species show ample genetic variation and strong population structure.     

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.     

Forecasting species ranges by statistical estimation of ecological niches and spatial population dynamics

Aim The study and prediction of species–environment relationships is currently mainly based on species distribution models. These purely correlative models neglect spatial population dynamics and assume that species distributions are in equilibrium with their environment. This causes biased estimates of species niches and handicaps forecasts of range dynamics under environmental change. Here we aim to develop an approach that statistically estimates process‐based models of range dynamics from data on species distributions and permits a more comprehensive quantification of forecast uncertainties. Innovation We present an approach for the statistical estimation of process‐based dynamic range models (DRMs) that integrate Hutchinson's niche concept with spatial population dynamics. In a hierarchical Bayesian framework the environmental response of demographic rates, local population dynamics and dispersal are estimated conditional upon each other while accounting for various sources of uncertainty. The method thus: (1) jointly infers species niches and spatiotemporal population dynamics from occurrence and abundance data, and (2) provides fully probabilistic forecasts of future range dynamics under environmental change. In a simulation study, we investigate the performance of DRMs for a variety of scenarios that differ in both ecological dynamics and the data used for model estimation. Main conclusions Our results demonstrate the importance of considering dynamic aspects in the collection and analysis of biodiversity data. In combination with informative data, the presented framework has the potential to markedly improve the quantification of ecological niches, the process‐based understanding of range dynamics and the forecasting of species responses to environmental change. It thereby strengthens links between biogeography, population biology and theoretical and applied ecology.     

The role of climate,habitat, and species co‐occurrence as drivers of change in small mammal distributions over the past century

Species distribution models are commonly used to predict species responses to climate change. However, their usefulness in conservation planning and policy is controversial because they are difficult to validate across time and space. Here we capitalize on small mammal surveys repeated over a century in Yosemite National Park, USA, to assess accuracy of model predictions. Historical (1900–1940) climate, vegetation, and species occurrence data were used to develop single‐ and multi‐species multivariate adaptive regression spline distribution models for three species of chipmunk. Models were projected onto the current (1980–2007) environmental surface and then tested against modern field resurveys of each species. We evaluated models both within and between time periods and found that even with the inclusion of biotic predictors, climate alone is the dominant predictor explaining the distribution of the study species within a time period. However, climate was not consistently an adequate predictor of the distributional change observed in all three species across time. For two of the three species, climate alone or climate and vegetation models showed good predictive performance across time. The stability of the distribution from the past to present observed in the third species, however, was not predicted by our modeling approach. Our results demonstrate that correlative distribution models are useful in understanding species' potential responses to environmental change, but also show how changes in species‐environment correlations through time can limit the predictive performance of models.