Species distribution modeling with Gaussian processes: A case study with the youngest stages of sea spawning whitefish (Coregonus lavaretus L. s.l.) larvae

Does weighting presence records improve the performance of species distribution models? A test using fish larval stages in the Yangtze Estuary

To obtain realistic forecasts of the impacts of climate change on species habitat suitability, novel approaches based on species distribution models (SDMs) are being developed and scrutinized. We argue here that, when dealing with data from long-term monitoring programmes, incorporating a temporal weight on the occurrence points may result in a more realistic prediction of a species' potential distribution. Using larval fish presence records collected from 1999 to 2013 in the Yangtze Estuary, China, we compared the performance of ensembles of standard SDMs versus SDMs constructed with weighted time-series presence records in predicting the present and future distributions of the larval stages of two dominant fish. The results of the ensemble SDMs showed that weighted presence records can significantly improve SDM performance, as measured through standard validation metrics. The SDM projections suggest that suitable habitat for both species will decrease under future climate scenarios, with one species (Stolephorus commersonnii) predicted to be more susceptible to climate change than the other (Engraulis japonicus). In addition to range contraction, model projections suggest that the future habitats of both species will shift northward-an implication of climate change that should be considered in future management and conservation strategies for the Yangtze Estuary.

Integrating experimental and distribution data to predict future species patterns

Predictive species distribution models are mostly based on statistical dependence between environmental and distributional data and therefore may fail to account for physiological limits and biological interactions that are fundamental when modelling species distributions under future climate conditions. Here, we developed a state-of-the-art method integrating biological theory with survey and experimental data in a way that allows us to explicitly model both physical tolerance limits of species and inherent natural variability in regional conditions and thereby improve the reliability of species distribution predictions under future climate conditions. By using a macroalga-herbivore association (Fucus vesiculosus - Idotea balthica) as a case study, we illustrated how salinity reduction and temperature increase under future climate conditions may significantly reduce the occurrence and biomass of these important coastal species. Moreover, we showed that the reduction of herbivore occurrence is linked to reduction of their host macroalgae. Spatial predictive modelling and experimental biology have been traditionally seen as separate fields but stronger interlinkages between these disciplines can improve species distribution projections under climate change. Experiments enable qualitative prior knowledge to be defined and identify cause-effect relationships, and thereby better foresee alterations in ecosystem structure and functioning under future climate conditions that are not necessarily seen in projections based on non-causal statistical relationships alone.