Predictive modeling of freshwater mussels (Unionidae) in the Appalachians

Freshwater mussels are in decline, particularly in the Appalachian region of North America. This region contains the world\u27s greatest diversity of freshwater mussels, but many species are now threatened or endangered. Little is known of the basic ecology and distributions of species of freshwater mussels relative to other freshwater organisms. The goal of this study was to use predictive modeling to predict distributions of freshwater mussels in the Appalachians and identify correlated factors using a watershed framework. Models were developed in the upper Mid-Atlantic and Ohio drainage regions using subwatersheds and separately in the Tennessee region using catchments. Models developed at this scale had low predictive ability because few surveys of freshwater mussels are available at the subwatershed scale and a regional extent. Independent data were unavailable to evaluate catchment-based models. Additional mussel surveys are necessary to expand the potential for developing robust predictive models of most freshwater mussel species

Can fire atlas data improve species distribution model projections?

Correlative species distribution models (SDMs) are widely used in studies of climate change impacts, yet are often criticized for failing to incorporate disturbance processes that can influence species distributions. Here we use two temporally independent data sets of vascular plant distributions, climate data, and fire atlas data to examine the influence of disturbance history on SDM projection accuracy through time in the mountain ranges of California, USA. We used hierarchical partitioning to examine the influence of fire occurrence on the distribution of 144 vascular plant species and built a suite of SDMs to examine how the inclusion of fire-related predictors (fire occurrence and departure from historical fire return intervals) affects SDM projection accuracy. Fire occurrence provided the least explanatory power among predictor variables for predicting species’ distributions, but provided improved explanatory power for species whose regeneration is tied closely to fire. A measure of the departure from historic fire return interval had greater explanatory power for calibrating modern SDMs than fire occurrence. This variable did not improve internal model accuracy for most species, although it did provide marginal improvement to models for species adapted to high-frequency fire regimes. Fire occurrence and fire return interval departure were strongly related to the climatic covariates used in SDM development, suggesting that improvements in model accuracy may not be expected due to limited additional explanatory power. Our results suggest that the inclusion of coarse-scale measures of disturbance in SDMs may not be necessary to predict species distributions under climate change, particularly for disturbance processes that are largely mediated by climate

Modeling plant ranges over 75 years of climate change in California, USA: temporal transferability and species traits

Species distribution model (SDM) projections under future climate scenarios are increasingly being used to inform resource management and conservation strategies. A critical assumption for projecting climate change responses is that SDMs are transferable through time, an assumption that is largely untested because investigators often lack temporally independent data for assessing transferability. Further, understanding how the ecology of species influences temporal transferability is critical yet almost wholly lacking. This raises two questions. (1) Are SDM projections transferable in time? (2) Does temporal transferability relate to species ecological traits? To address these questions we developed SDMs for 133 vascular plant species using data from the mountain ranges of California (USA) from two time periods: the 1930s and the present day. We forecast historical models over 75 years of measured climate change and assessed their projections against current distributions. Similarly, we hindcast contemporary models and compared their projections to historical data. We quantified transferability and related it to species ecological traits including physiognomy, endemism, dispersal capacity, fire adaptation, and commonness. We found that non-endemic species with greater dispersal capacity, intermediate levels of prevalence, and little fire adaptation had higher transferability than endemic species with limited dispersal capacity that rely on fire for reproduction. We demonstrate that variability in model performance was driven principally by differences among species as compared to model algorithms or time period of model calibration. Further, our results suggest that the traits correlated with prediction accuracy in a single time period may not be related to transferability between time periods. Our findings provide a priori guidance for the suitability of SDM as an approach for forecasting climate change responses for certain taxa

Appendix A. Hierarchical partitioning and species distribution model (SDM) accuracy results when considering number of fires as an ordinal predictor.

Hierarchical partitioning and species distribution model (SDM) accuracy results when considering number of fires as an ordinal predictor

Appendix B. Change in model accuracy metrics for alternative SDM algorithms.

Change in model accuracy metrics for alternative SDM algorithms

Appendix A. Summary of species ecological traits.

Summary of species ecological traits