10.5061/DRYAD.CJSXKSN33
Simensen, Trond
0000-0002-7022-1349
University of Oslo
Horvath, Peter
University of Oslo
Vollering, Julien
University of Oslo
Erikstad, Lars
University of Oslo
Halvorsen, Rune
University of Oslo
Bryn, Anders
University of Oslo
Composite landscape predictors improve distribution models of ecosystem types
Dryad
dataset
2020
distribution modelling
IUCN Red List of Ecosystems
conservation planning
landscape gradient
The Research Council of Norway
https://ror.org/00epmv149
2021-06-24T00:00:00Z
2021-06-24T00:00:00Z
en
https://doi.org/10.1111/ddi.13060
9446723029 bytes
2
CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
Aim: Distribution modelling is a useful approach to obtain knowledge about
the spatial distribution of biodiversity, required for e.g., red list
assessments. While distribution modelling methods have been applied mostly
to single species, modelling of communities and ecosystems (EDM;
ecosystem-level distribution modelling) produces results that are more
directly relevant for management and decision-making. Although the choice
of predictors is a pivotal part of the modelling process, few studies have
compared the suitability of different sets of predictors for EDM. In this
study, we compare the performance of 50 single environmental variables
with that of 11 composite landscape gradients (CLGs) for prediction of
ecosystem types. The CLGs represent gradients in landscape element
composition derived from multivariate analyses, e.g., ‘inner-outer coast’
and ‘land use intensity’. Location: Norway. Methods: We used data from
field-based ecosystem type mapping of nine ecosystem types, and
environmental variables with a resolution of 100×100 m. We built nine
models for each ecosystem type with variables from different predictor
sets. Logistic regression with forward selection of variables was used for
EDM. Models were evaluated with independently collected data. Results:
Most ecosystem types could be predicted reliably, although model
performance differed among ecosystem types. We identified significant
differences in predictive power and model parsimony across models built
from different predictor sets. Climatic variables alone performed poorly,
indicating that the current climate alone is not sufficient to predict the
current distribution of ecosystems. Used alone, the CLGs resulted in
parsimonious models with relatively high predictive power. Used together
with other variables, they consistently improved the models. Main
conclusions: We argue that the use of composite variables as proxies for
complex environmental gradients has the potential to improve predictions
from EDMs and thus to inform conservation planning as well as improve the
precision and credibility of red lists and global change assessments.
See description in article (main text and supplementary material).
The available scripts are general scripts, and can be adapted to any
ecosystem type (or other modelling targets). The R working directory and
reference to the specific predictors applied in a study should be set
specifically in the scripts. The training data for the response variables
(i.e. ecosystem types) and the spatial data not uploaded here (i.e., the
climatic and non-climatic ‘basic’ predictor variables) is available on
request from the authors. Due to the restrictions with ownership of the
original area frame survey data (AR18X18), these data are not openly
available from the authors.