Predicting richness and composition in mountain insect communities at high resolution: a new test of the SESAM framework

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Type
Article: article from journal or magazin.
Collection
Publications
Title
Predicting richness and composition in mountain insect communities at high resolution: a new test of the SESAM framework
Journal
Global Ecology and Biogeography
Author(s)
D'Amen M., Pradervand J.-N., Guisan A.
ISSN
1466-8238
ISSN-L
1466-822X
Publication state
Published
Issued date
2015
Peer-reviewed
Oui
Volume
24
Number
12
Pages
1443-1453
Language
english
Abstract
Aim The aim of this study was to test different modelling approaches, including a
new framework, for predicting the spatial distribution of richness and composition
of two insect groups.
Location The western Swiss Alps.
Methods We compared two community modelling approaches: the classical
method of stacking binary prediction obtained fromindividual species distribution
models (binary stacked species distribution models, bS-SDMs), and various implementations
of a recent framework (spatially explicit species assemblage modelling,
SESAM) based on four steps that integrate the different drivers of the assembly
process in a unique modelling procedure. We used: (1) five methods to create
bS-SDM predictions; (2) two approaches for predicting species richness, by
summing individual SDM probabilities or by modelling the number of species (i.e.
richness) directly; and (3) five different biotic rules based either on ranking probabilities
from SDMs or on community co-occurrence patterns. Combining these
various options resulted in 47 implementations for each taxon.
Results Species richness of the two taxonomic groups was predicted with good
accuracy overall, and in most cases bS-SDM did not produce a biased prediction
exceeding the actual number of species in each unit. In the prediction of community
composition bS-SDM often also yielded the best evaluation score. In the case
of poor performance of bS-SDM (i.e. when bS-SDM overestimated the prediction
of richness) the SESAM framework improved predictions of species composition.
Main conclusions Our results differed from previous findings using
community-level models. First, we show that overprediction of richness by
bS-SDM is not a general rule, thus highlighting the relevance of producing good
individual SDMs to capture the ecological filters that are important for the assembly
process. Second, we confirm the potential of SESAM when richness is
overpredicted by bS-SDM; limiting the number of species for each unit and applying
biotic rules (here using the ranking of SDM probabilities) can improve predictions
of species composition
Keywords
Biotic rules, co-occurrence analysis, macroecological models, SESAM framework, stacked species distribution models, thresholding.
Web of science
Open Access
Yes
Create date
01/08/2015 16:24
Last modification date
20/08/2019 16:50
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