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Ecol Evol. 2019 Sep 4;9(18):10457-10471. doi: 10.1002/ece3.5567. eCollection 2019 Sep.

Predicting species occurrences with habitat network models.

Author information

1
WSL Swiss Federal Research Institute Birmensdorf Switzerland.
2
Planning of Landscape and Urban Systems (PLUS) Institute for Spatial and Landscape Planning ETH Zurich Zürich Switzerland.
3
Department of Ecology and Evolution University of Lausanne Lausanne Switzerland.
4
Department of Environmental Systems Science ETH Zurich Zürich Switzerland.
5
Institute of Earth Surface Dynamics University of Lausanne Lausanne Switzerland.

Abstract

Biodiversity conservation requires modeling tools capable of predicting the presence or absence (i.e., occurrence-state) of species in habitat patches. Local habitat characteristics of a patch (lh), the cost of traversing the landscape matrix between patches (weighted connectivity [wc]), and the position of the patch in the habitat network topology (nt) all influence occurrence-state. Existing models are data demanding or consider only local habitat characteristics. We address these shortcomings and present a network-based modeling approach, which aims to predict species occurrence-state in habitat patches using readily available presence-only records.For the tree frog Hyla arborea in the Swiss Plateau, we delineated habitat network nodes from an ensemble habitat suitability model and used different cost surfaces to generate the edges of three networks: one limited only by dispersal distance (Uniform), another incorporating traffic, and a third based on inverse habitat suitability. For each network, we calculated explanatory variables representing the three categories (lh, wc, and nt). The response variable, occurrence-state, was parametrized by a sampling intensity procedure assessing observations of comparable species over a threshold of patch visits. The explanatory variables from the three networks and an additional non-topological model were related to the response variable with boosted regression trees.The habitat network models had a similar fit; they all outperformed the non-topological model. Habitat suitability index (lh) was the most important predictor in all networks, followed by third-order neighborhood (nt). Patch size (lh) was unimportant in all three networks.We found that topological variables of habitat networks are relevant for the prediction of species occurrence-state, a step-forward from models considering only local habitat characteristics. For any habitat patch, occurrence-state is most prominently influenced by its habitat suitability and then by the number of patches in a wide neighborhood. Our approach is generic and can be applied to multiple species in different habitats.

KEYWORDS:

connectivity; cost surface; habitat network; habitat suitability; network topology; species occurrence

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