Paniw, M., García-Callejas, D., Lloret, F., Bassar, R.D., Travis, J. & Godoy, O. (2023) Pathways to global-change effects on biodiversity: new opportunities for dynamically forecasting demography and species interactions. Proceedings of the Royal Society B: Biological Sciences, 290, 20221494.
In structured populations, persistence under environmental change may be particularly threatened when abiotic factors simultaneously negatively affect survival and reproduction of several life cycle stages, as opposed to a single stage. Such effects can then be exacerbated when species interactions generate reciprocal feedbacks between the demographic rates of the different species. Despite the importance of such demographic feedbacks, forecasts that account for them are limited as individual-based data on interacting species are perceived to be essential for such mechanistic forecasting—but are rarely available. Here, we first review the current shortcomings in assessing demographic feedbacks in population and community dynamics. We then present an overview of advances in statistical tools that provide an opportunity to leverage population-level data on abundances of multiple species to infer stage-specific demography. Lastly, we showcase a state-of-the-art Bayesian method to infer and project stage-specific survival and reproduction for several interacting species in a Mediterranean shrub community. This case study shows that climate change threatens populations most strongly by changing the interaction effects of conspecific and heterospecific neighbours on both juvenile and adult survival. Thus, the repurposing of multi-species abundance data for mechanistic forecasting can substantially improve our understanding of emerging threats on biodiversity.
Bassar, R. D., et al. 2015. Changes in seasonal climate outpace compensatory density-dependence in eastern brook trout. Global Change Biology.
Understanding how multiple extrinsic (density-independent) factors and intrinsic (density-dependent) mechanisms influence population dynamics has become increasingly urgent in the face of rapidly changing climates. It is particularly unclear how multiple extrinsic factors with contrasting effects among seasons are related to declines in population numbers and changes in mean body size and whether there is a strong role for density-dependence. The primary goal of this study was to identify the roles of seasonal variation in climate driven environmental direct effects (mean stream flow and temperature) versus density-dependence on population size and mean body size in eastern brook trout (Salvelinus fontinalis). We use data from a 10-year capture-mark-recapture study of eastern brook trout in four streams in Western Massachusetts, USA to parameterize a discrete-time population projection model. The model integrates matrix modeling techniques used to characterize discrete population structures (age, habitat type and season) with integral projection models (IPMs) that characterize demographic rates as continuous functions of organismal traits (in this case body size). Using both stochastic and deterministic analyses we show that decreases in population size are due to changes in stream flow and temperature and that these changes are larger than what can be compensated for through density-dependent responses. We also show that the declines are due mostly to increasing mean stream temperatures decreasing the survival of the youngest age class. In contrast, increases in mean body size over the same period are the result of indirect changes in density with a lesser direct role of climate-driven environmental change.
Letcher, B. H., P. Schueller, R. D. Bassar, K. H. Nislow, J. A. Coombs, K. Sakrejda, M. Morrissey, D. Sigourney, A. Whiteley, M. O'Donnell, and T. Dubreuil. 2015. Robust estimates of seasonal environmental effects on population vital rates: an integrated capture-recapture model of brook trout growth, survival and movement in a stream network. Journal of Animal Ecology 84.
We developed and applied an integrated capture-recapture state-space model to estimate the effects of two key environmental drivers (stream flow and temperature) on demographic rates (body growth, movement, and survival) using a long-term (11 years), high resolution (individually tagged, sampled seasonally) dataset of brook trout (Salvelinus fontinalis) from four sites in a stream network. Our integrated model provides an effective context in which to estimate environmental driver effects because it takes full advantage of data by estimating (latent) state values for missing observations and because it accounts for the major demographic rates and interactions that contribute to annual survival. We found that stream flow and temperature had strong effects on brook trout demography. Some effects on survival, such as reduction in survival associated with low stream flow and high temperature during the summer season, were consistent across sites and age-classes, and concordant with previous studies and basic natural history considerations, suggesting that they may already serve robust assessment measures of vulnerability to environmental change. Other survival effects varied across ages, sites, and seasons, indicating that flow and temperature may not be the primary drivers of survival in those cases. Flow and temperature also affected growth rates, but growth responses varied dramatically between ages and very little among sites. Finally, we found that tributary and mainstem sites responded differently to variation in flow and temperature. These observations, combined with our ability to estimate the occurrence, magnitude and direction of movement between these habitat types, indicated that heterogeneity in response may provide a mechanism providing potential resilience to environmental change. Long term, high resolution population data combined with effective models provide valuable information for establishing relationships between environmental drivers and population responses. Given that the challenges we faced in our study are likely to be common to many of these intensive datasets, our modeling approach could be widely applicable and useful.