Community dynamics under environmental change: How can nextgeneration mechanistic models improve projections of speciesdistributions?

Abstract

tEnvironmental change is expected to shift the geographic range of species and communities. Toestimate the consequences of these shifts for the functioning and stability of ecosystems, reliablepredictions of alterations in species distributions are needed. Projections with correlative species dis-tribution models, which correlate species’ distributions to the abiotic environment, have become astandard approach. Criticism of this approach centres around the omission of relevant biotic feed-backs and triggered the search for alternatives. A new generation of mechanistic process-basedspecies distribution models aims at implementing formulations of relevant biotic processes to coverspecies’ life histories, physiology, dispersal abilities, evolution, and both intra- and interspecific interac-tions. Although this step towards more structural realism is considered important, it remains unclearwhether the resulting projections are more reliable. Structural realism has the advantage that geo-graphic range shifting emerges from the interplay of relevant abiotic and biotic processes. Havingimplemented the relevant response mechanisms, structural realistic models should better tackle thechallenge of generating projections of species responses to (non-analogous) environmental change.However, reliable projections of future species ranges demand ecological information that is cur-rently only available for few species. In this opinion paper, we discuss how the discrepancy betweendemand for structural realism on the one hand and the related knowledge gaps on the other handaffects the reliability of mechanistic species distribution models. We argue that omission of rele-vant processes potentially impairs projection accuracy (proximity of the mean outcome to the truevalue), particularly if species range shifts emerge from species and community dynamics. Yet, insuf-ficient knowledge that limits model specification and parameterization, as well as process complexity.