TY  - JOUR
A1  - Geyer, Juliane
A1  - Kiefer, Iris
A1  - Kreft, Stefan
A1  - Chavez, Veronica
A1  - Salafsky, Nick
A1  - Jeltsch, Florian
A1  - Ibisch, Pierre L.
T1  - Classification of climate-change-induced stresses on biological diversity
JF  - Conservation biology : the journal of the Society for Conservation Biology
N2  - Conservation actions need to account for and be adapted to address changes that will occur under global climate change. The identification of stresses on biological diversity (as defined in the Convention on Biological Diversity) is key in the process of adaptive conservation management. We considered any impact of climate change on biological diversity a stress because such an effect represents a change (negative or positive) in key ecological attributes of an ecosystem or parts of it. We applied a systemic approach and a hierarchical framework in a comprehensive classification of stresses to biological diversity that are caused directly by global climate change. Through analyses of 20 conservation sites in 7 countries and a review of the literature, we identified climate-change-induced stresses. We grouped the identified stresses according to 3 levels of biological diversity: stresses that affect individuals and populations, stresses that affect biological communities, and stresses that affect ecosystem structure and function. For each stress category, we differentiated 3 hierarchical levels of stress: stress class (thematic grouping with the coarsest resolution, 8); general stresses (thematic groups of specific stresses, 21); and specific stresses (most detailed definition of stresses, 90). We also compiled an overview of effects of climate change on ecosystem services using the categories of the Millennium Ecosystem Assessment and 2 additional categories. Our classification may be used to identify key climate-change-related stresses to biological diversity and may assist in the development of appropriate conservation strategies. The classification is in list format, but it accounts for relations among climate-change-induced stresses.
KW  - adaptation of conservation strategies
KW  - adaptive management
KW  - climate change
KW  - conservation planning
KW  - conservation targets
KW  - hierarchical framework
KW  - threats to biological diversity
Y1  - 2011
U6  - https://doi.org/10.1111/j.1523-1739.2011.01676.x
SN  - 0888-8892
VL  - 25
IS  - 4
SP  - 708
EP  - 715
PB  - Wiley-Blackwell
CY  - Malden
ER  - 
TY  - JOUR
A1  - Zurell, Damaris
A1  - König, Christian
A1  - Malchow, Anne-Kathleen
A1  - Kapitza, Simon
A1  - Bocedi, Greta
A1  - Travis, Justin M. J.
A1  - Fandos, Guillermo
T1  - Spatially explicit models for decision-making in animal conservation and restoration
JF  - Ecography : pattern and diversity in ecology / Nordic Ecologic Society Oikos
N2  - Models are useful tools for understanding and predicting ecological patterns and processes. Under ongoing climate and biodiversity change, they can greatly facilitate decision-making in conservation and restoration and help designing adequate management strategies for an uncertain future. Here, we review the use of spatially explicit models for decision support and to identify key gaps in current modelling in conservation and restoration. Of 650 reviewed publications, 217 publications had a clear management application and were included in our quantitative analyses. Overall, modelling studies were biased towards static models (79%), towards the species and population level (80%) and towards conservation (rather than restoration) applications (71%). Correlative niche models were the most widely used model type. Dynamic models as well as the gene-to-individual level and the community-to-ecosystem level were underrepresented, and explicit cost optimisation approaches were only used in 10% of the studies. We present a new model typology for selecting models for animal conservation and restoration, characterising model types according to organisational levels, biological processes of interest and desired management applications. This typology will help to more closely link models to management goals. Additionally, future efforts need to overcome important challenges related to data integration, model integration and decision-making. We conclude with five key recommendations, suggesting that wider usage of spatially explicit models for decision support can be achieved by 1) developing a toolbox with multiple, easier-to-use methods, 2) improving calibration and validation of dynamic modelling approaches and 3) developing best-practise guidelines for applying these models. Further, more robust decision-making can be achieved by 4) combining multiple modelling approaches to assess uncertainty, and 5) placing models at the core of adaptive management. These efforts must be accompanied by long-term funding for modelling and monitoring, and improved communication between research and practise to ensure optimal conservation and restoration outcomes.
KW  - adaptive management
KW  - biodiversity conservation
KW  - cost optimisation
KW  - ecosystem restoration
KW  - global change
KW  - predictive models
Y1  - 2021
U6  - https://doi.org/10.1111/ecog.05787
SN  - 1600-0587
IS  - 4
SP  - 1
EP  - 16
PB  - Wiley-Blackwell
CY  - Oxford
ER  -