TY  - JOUR
A1  - Schulz, Jennifer J.
A1  - Cayuela, Luis
A1  - Rey-Benayas, Jose M.
A1  - Schröder-Esselbach, Boris
T1  - Factors influencing vegetation cover change in Mediterranean Central Chile (1975-2008)
JF  - Applied vegetation science : official organ of the International Association for Vegetation Science
N2  - Questions: Which are the factors that influence forest and shrubland loss and regeneration and their underlying drivers?
 Location: Central Chile, a world biodiversity hotspot.
 Methods: Using land-cover data from the years 1975, 1985, 1999 and 2008, we fitted classification trees and multiple logistic regression models to account for the relationship between different trajectories of vegetation change and a range of biophysical and socio-economic factors.
 Results: The variables that most consistently showed significant effects on vegetation change across all time-intervals were slope and distance to primary roads. We found that forest and shrubland loss on one side and regeneration on the other often displayed opposite patterns in relation to the different explanatory variables. Deforestation was positively related to distance to primary roads and to distance within forest edges and was favoured by a low insolation and a low slope. In turn, forest regeneration was negatively related to the distance to primary roads and positively to the distance to the nearest forest patch, insolation and slope. Shrubland loss was positively influenced by slope and distance to cities and primary roads and negatively influenced by distance to rivers. Conversely, shrubland regeneration was negatively related to slope, distance to cities and distance to primary roads and positively related to distance from existing forest patches and distance to rivers.
 Conclusions: This article reveals how biophysical and socioeconomic factors influence vegetation cover change and the underlying social, political and economical drivers. This assessment provides a basis for management decisions, considering the crucial role of perennial vegetation cover for sustaining biodiversity and ecosystem services.
KW  - Deforestation
KW  - Driving forces
KW  - Forest regeneration
KW  - Land-cover change
KW  - Shrubland regeneration
Y1  - 2011
U6  - https://doi.org/10.1111/j.1654-109X.2011.01135.x
SN  - 1402-2001
VL  - 14
IS  - 4
SP  - 571
EP  - 582
PB  - Wiley-Blackwell
CY  - Hoboken
ER  - 
TY  - INPR
A1  - Wellstein, Camilla
A1  - Schröder-Esselbach, Boris
A1  - Reineking, Bjoern
A1  - Zimmermann, Niklaus E.
T1  - Understanding species and community response to environmental change - A functional trait perspective
T2  - Agriculture, ecosystems & environment : an international journal for scientific research on the relationship of agriculture and food production to the biosphere
KW  - Functional traits
KW  - Functional diversity
KW  - Database
KW  - Land use
KW  - Management
KW  - Climate change
KW  - Landscape
KW  - Ecosystem function
KW  - Clonal plants
KW  - Dispersal
KW  - Plant growth
KW  - Orthoptera
Y1  - 2011
U6  - https://doi.org/10.1016/j.agee.2011.06.024
SN  - 0167-8809
VL  - 145
IS  - 1
SP  - 1
EP  - 4
PB  - Elsevier
CY  - Amsterdam
ER  - 
TY  - JOUR
A1  - Wintle, Brendan A.
A1  - Bekessy, Sarah A.
A1  - Keith, David A.
A1  - van Wilgen, Brian W.
A1  - Cabeza, Mar
A1  - Schröder-Esselbach, Boris
A1  - Carvalho, Silvia B.
A1  - Falcucci, Alessandra
A1  - Maiorano, Luigi
A1  - Regan, Tracey J.
A1  - Rondinini, Carlo
A1  - Boitani, Luigi
A1  - Possingham, Hugh P.
T1  - Ecological-economic optimization of biodiversity conservation under climate change
JF  - Nature climate change
N2  - Substantial investment in climate change research has led to dire predictions of the impacts and risks to biodiversity. The Intergovernmental Panel on Climate Change fourth assessment report(1) cites 28,586 studies demonstrating significant biological changes in terrestrial systems(2). Already high extinction rates, driven primarily by habitat loss, are predicted to increase under climate change(3-6). Yet there is little specific advice or precedent in the literature to guide climate adaptation investment for conserving biodiversity within realistic economic constraints(7). Here we present a systematic ecological and economic analysis of a climate adaptation problem in one of the world's most species-rich and threatened ecosystems: the South African fynbos. We discover a counterintuitive optimal investment strategy that switches twice between options as the available adaptation budget increases. We demonstrate that optimal investment is nonlinearly dependent on available resources, making the choice of how much to invest as important as determining where to invest and what actions to take. Our study emphasizes the importance of a sound analytical framework for prioritizing adaptation investments(4). Integrating ecological predictions in an economic decision framework will help support complex choices between adaptation options under severe uncertainty. Our prioritization method can be applied at any scale to minimize species loss and to evaluate the robustness of decisions to uncertainty about key assumptions.
Y1  - 2011
U6  - https://doi.org/10.1038/NCLIMATE1227
SN  - 1758-678X
VL  - 1
IS  - 7
SP  - 355
EP  - 359
PB  - Nature Publ. Group
CY  - London
ER  -