{"refrec":{"BRefID":354621,"RR":"Stephenson, F.; Gladstone-Gallagher, R.V.; Bulmer, R.H.; Thrush, S.F.; Hewitt, J.E. (2022). Inclusion of biotic variables improves predictions of environmental niche models. Diversity Distrib. 28(7): 1373-1390. https://dx.doi.org/10.1111/ddi.13546","BEntID":352334,"PublicFlag":1,"CheckedFlag":0,"wosflag":1,"vabbflag":0,"RefStringPartII":". Diversity Distrib. 28(7): 1373-1390. https://dx.doi.org/10.1111/ddi.13546","DocTypID":8,"DocType":"Journal article","MarineFlag":1,"FreshFlag":0,"BrackishFlag":0,"TerrestrialFlag":0,"Authorstring":"Stephenson, F.; Gladstone-Gallagher, R.V.; Bulmer, R.H.; Thrush, S.F.; Hewitt, J.E.","OrigTitleTranslFlag":0,"Authorstringtrunc":"Stephenson, F. et al.","Englishabstract":"

Aim

Species Distribution Models (SDMs) are correlative models that predict the occurrence or abundance of species in relation to predictor variables. SDMs have become an important part of resource management and conservation biology yet they rarely incorporate species’ biology or demography into their predictions. To explore the possible influence of biotic relationships in explaining patterns of species’ distribution, abundance and explanatory power of SDMs, we chose two intertidal shellfish species with overlapping but different environmental preferences (Austrovenus stutchburyi and Macomona liliana) and modelled their distributions with and without biotic variables.

Location

New Zealand.

Methods

The relationship between environmental and biotic variables on the abundance of our two species was investigated using Boosted Regression Trees (BRTs) with increasing model complexity: (1) BRT models using environmental variables were fitted to each species; (2) BRT models using environmental variables and the co-occurring abundance of the study taxa not being modelled were fitted; (3) BRT models using environmental variables, the co-occurring abundance and the estimated abundance of the species’ patch of the study taxa not being modelled were fitted.

Results

A strong, non-linear effect of the abundance of Austrovenus on Macomona was observed but only a weak effect of Macomona on Austrovenus. The inclusion of biotic variables improved the model fit metrics for both species, as assessed by withheld evaluation data, markedly so for Macomona. The overall deviance explained by the models increased, the correlation of predicted vs observed abundance data increased and the variability in these measures decreased.

Main conclusions

The combination of the improvement in model performance and changes in the influence of variables with the inclusion of biotic variables is of importance when predicting into unsampled space (e.g. when predicting impacts of climate change). Our approach improves classic SDMs by integrating ecological theories of how species interactions can alter species distributions across environmental gradients.

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