10.5061/DRYAD.NP5HQBZV1
Redlich, Sarah
0000-0001-5609-0576
University of Würzburg
Zhang, Jie
University of Würzburg
Benjamin, Caryl
Technical University Munich
Singh Dhillon, Maninder
University of Würzburg
Englmeier, Jana
University of Würzburg
Ewald, Jörg
Weihenstephan-Triesdorf University of Applied Sciences
Fricke, Ute
University of Würzburg
Ganuza, Cristina
University of Würzburg
Hänsel, Maria
University of Bayreuth
Hovestadt, Thomas
University of Würzburg
Kollmann, Johannes
Technical University Munich
Koellner, Thomas
University of Bayreuth
Kübert-Flock, Carina
University of Würzburg
Kunstmann, Harald
University of Augsburg
Menzel, Annette
Technical University Munich
Moning, Christoph
Weihenstephan-Triesdorf University of Applied Sciences
Peters, Wibke
Bavarian State Institute of Forestry
Riebl, Rebekka
University of Bayreuth
Rummler, Thomas
University of Augsburg
Rojas Botero, Sandra
Technical University Munich
Tobisch, Cynthia
Weihenstephan-Triesdorf University of Applied Sciences
Uhler, Johannes
University of Würzburg
Uphus, Lars
Technical University Munich
Müller, Jörg
University of Würzburg
Steffan-Dewenter, Ingolf
University of Würzburg
Disentangling effects of climate and land use on biodiversity and
ecosystem services – a multi-scale experimental design
Dryad
dataset
2021
Bavarian Ministry of Science and the Arts*
2021-11-05T00:00:00Z
2021-11-05T00:00:00Z
en
https://doi.org/10.1101/2021.03.05.434036
https://doi.org/10.5281/zenodo.5592352
https://doi.org/10.5281/zenodo.5647035
22016909 bytes
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CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
1. Climate and land-use change are key drivers of environmental
degradation in the Anthropocene, but too little is known about their
interactive effects on biodiversity and ecosystem services. Long-term data
on biodiversity trends are currently lacking. Furthermore, previous
ecological studies have rarely considered climate and land use in a joint
design, did not achieve variable independence or lost statistical power by
not covering the full range of environmental gradients. 2. Here, we
introduce a multi-scale space-for-time study design to disentangle effects
of climate and land use on biodiversity and ecosystem services. The site
selection approach coupled extensive GIS-based exploration and correlation
heatmaps with a crossed and nested design covering regional, landscape and
local scales. Its implementation in Bavaria (Germany) resulted in a set of
study plots that maximize the potential range and independence of
environmental variables at different spatial scales. 3. Stratifying the
state of Bavaria into five climate zones (reference period 1981–2010) and
three prevailing land-use types, i.e. near-natural, agriculture and urban,
resulted in 60 study regions (5.8x5.8 km quadrants) covering a mean annual
temperature gradient of 5.6–9.8 °C and a spatial extent of ~310x310 km.
Within these regions, we nested 180 study plots located in contrasting
local land-use types, i.e. forests, grasslands, arable land or settlement
(local climate gradient 4.5–10 °C). This approach achieved low
correlations between climate and land use (proportional cover) at the
regional and landscape scale with |r≤0.33| and |r≤0.29|, respectively.
Furthermore, using correlation heatmaps for local plot selection reduced
potentially confounding relationships between landscape composition and
configuration for plots located in forests, arable land and settlements.
4. The suggested design expands upon previous research in covering a
significant range of environmental gradients and including a diversity of
dominant land-use types at different scales within different climatic
contexts. It allows independent assessment of the relative contribution of
multi-scale climate and land use on biodiversity and ecosystem services.
Understanding potential interdependencies among global change drivers is
essential to develop effective restoration and mitigation strategies
against biodiversity decline, especially in expectation of future climatic
changes. Importantly, this study also provides a baseline for long-term
ecological monitoring programs.
The data sets contains climate and land-use variables resulting from the
selection of 179 study plots within 60 study regions in Bavaria, Germany.
The nested, large-scale design aimed to minimize correlations between land
use and climate across the regional and local scale, while also reducing
the correlation between the configuration (edge density) and composition
(% land use cover) of landscapes in 1-km scale around the study plots. We
also provide the R code used to calculate correlations and create graphs
on Zenodo.
The ReadMe file contains an explanation of all variables of the main
datasets used in the analyses and their measurement units.