Extrapolating demography with climate,proximity and phylogeny: approach with caution

Plant population responses are key to understanding the effects of threats such as climate change and invasions. However, we lack demographic data for most species, and the data we have are often geographically aggregated. We determined to what extent existing data can be extrapolated to predict population performance across larger sets of species and spatial areas. We used 550 matrix models, across 210 species, sourced from the COMPADRE Plant Matrix Database, to model how climate, geographic proximity and phylogeny predicted population performance. Models including only geographic proximity and phylogeny explained 5–40% of the variation in four key metrics of population performance. However, there was poor extrapolation between species and extrapolation was limited to geographic scales smaller than those at which landscape scale threats typically occur. Thus, demographic information should only be extrapolated with caution. Capturing demography at scales relevant to landscape level threats will require more geographically extensive sampling.     

Co‐occurrence patterns of trees along macro‐climatic gradients and their potential influence on the present and future distribution of Fagus sylvatica L.

Aim During recent and future climate change, shifts in large‐scale species ranges are expected due to the hypothesized major role of climatic factors in regulating species distributions. The stress‐gradient hypothesis suggests that biotic interactions may act as major constraints on species distributions under more favourable growing conditions, while climatic constraints may dominate under unfavourable conditions. We tested this hypothesis for one focal tree species having three major competitors using broad‐scale environmental data. We evaluated the variation of species co‐occurrence patterns in climate space and estimated the influence of these patterns on the distribution of the focal species for current and projected future climates. Location Europe. Methods We used ICP Forest Level 1 data as well as climatic, topographic and edaphic variables. First, correlations between the relative abundance of European beech (Fagus sylvatica) and three major competitor species (Picea abies, Pinus sylvestris and Quercus robur) were analysed in environmental space, and then projected to geographic space. Second, a sensitivity analysis was performed using generalized additive models (GAM) to evaluate where and how much the predicted F. sylvatica distribution varied under current and future climates if potential competitor species were included or excluded. We evaluated if these areas coincide with current species co‐occurrence patterns. Results Correlation analyses supported the stress‐gradient hypothesis: towards favourable growing conditions of F. sylvatica, its abundance was strongly linked to the abundance of its competitors, while this link weakened towards unfavourable growing conditions, with stronger correlations in the south and at low elevations than in the north and at high elevations. The sensitivity analysis showed a potential spatial segregation of species with changing climate and a pronounced shift of zones where co‐occurrence patterns may play a major role. Main conclusions Our results demonstrate the importance of species co‐occurrence patterns for calibrating improved species distribution models for use in projections of climate effects. The correlation approach is able to localize European areas where inclusion of biotic predictors is effective. The climate‐induced spatial segregation of the major tree species could have ecological and economic consequences.     

A demographic approach to study effects of climate change in desert plants

Desert species respond strongly to infrequent, intense pulses of precipitation. Consequently, indigenous flora has developed a rich repertoire of life-history strategies to deal with fluctuations in resource availability. Examinations of how future climate change will affect the biota often forecast negative impacts, but these—usually correlative—approaches overlook precipitation variation because they are based on averages. Here, we provide an overview of how variable precipitation affects perennial and annual desert plants, and then implement an innovative, mechanistic approach to examine the effects of precipitation on populations of two desert plant species. This approach couples robust climatic projections, including variable precipitation, with stochastic, stage-structured models constructed from long-term demographic datasets of the short-lived Cryptantha flava in the Colorado Plateau Desert (USA) and the annual Carrichtera annua in the Negev Desert (Israel). Our results highlight these populations'' potential to buffer future stochastic precipitation. Population growth rates in both species increased under future conditions: wetter, longer growing seasons for Cryptantha and drier years for Carrichtera. We determined that such changes are primarily due to survival and size changes for Cryptantha and the role of seed bank for Carrichtera. Our work suggests that desert plants, and thus the resources they provide, might be more resilient to climate change than previously thought.     

Conservation management and sustainable harvest quotas are sensitive to choice of climate modelling approach for two marine gastropods

Aim

To establish the robustness of two alternative methods for predicting the future ranges and abundances for two wild‐harvested abalone species (Haliotis rubra Donovan 1808 and H. laevigata Leach 1814): single atmosphere–ocean general circulation model (GCM) or ensemble‐averaged GCM forecasts.

Location

South Australia.

Methods

We assessed the ability of 20 GCMs to simulate observed seasonal sea surface temperature (SST) between 1980–1999, globally, and regionally for the Indian and Pacific Oceans south of the Equator. We used model rankings to characterize a set of representative climate futures, using three different‐sized GCM ensembles and two individual GCMs (the Parallel Climate Model and the Community Climate System Model, version 3.0). Ecological niche models were then coupled to physiological information to compare forecast changes in area of occupancy, population size and harvest area based on forecasts using the various GCM selection methods, as well as different greenhouse gas emission scenarios and climate sensitivities.

Results

We show that: (1) the skill with which climate models reproduce recent SST records varies considerably amongst GCMs, with multimodel ensemble averages showing closer agreement to observations than single models; (2) choice of GCM, and the decision on whether or not to use ensemble‐averaged climate forecasts, can strongly influence spatiotemporal predictions of range, abundance and fishing potential; and (3) comparable hindcasting skill does not necessarily guarantee that GCM forecasts and ecological and evolutionary responses to these forecast changes, will be similar amongst closely ranked models.

Conclusion

By averaging across an ensemble of seven highly ranked skilful GCMs, inherent uncertainties stemming from GCM differences are incorporated into forecasts of change in species range, abundance and sustainable fishing area. Our results highlight the need to make informed and explicit decisions on GCM choice, model sensitivity and emission scenarios when exploring conservation options for marine species and the sustainability of future harvests using ecological niche models.

     

Estimating indices of range shifts in birds using dynamic models when detection is imperfect

There is intense interest in basic and applied ecology about the effect of global change on current and future species distributions. Projections based on widely used static modeling methods implicitly assume that species are in equilibrium with the environment and that detection during surveys is perfect. We used multiseason correlated detection occupancy models, which avoid these assumptions, to relate climate data to distributional shifts of Louisiana Waterthrush in the North American Breeding Bird Survey (BBS) data. We summarized these shifts with indices of range size and position and compared them to the same indices obtained using more basic modeling approaches. Detection rates during point counts in BBS surveys were low, and models that ignored imperfect detection severely underestimated the proportion of area occupied and slightly overestimated mean latitude. Static models indicated Louisiana Waterthrush distribution was most closely associated with moderate temperatures, while dynamic occupancy models indicated that initial occupancy was associated with diurnal temperature ranges and colonization of sites was associated with moderate precipitation. Overall, the proportion of area occupied and mean latitude changed little during the 1997–2013 study period. Near‐term forecasts of species distribution generated by dynamic models were more similar to subsequently observed distributions than forecasts from static models. Occupancy models incorporating a finite mixture model on detection – a new extension to correlated detection occupancy models – were better supported and may reduce bias associated with detection heterogeneity. We argue that replacing phenomenological static models with more mechanistic dynamic models can improve projections of future species distributions. In turn, better projections can improve biodiversity forecasts, management decisions, and understanding of global change biology.