TY  - JOUR
A1  - Radchuk, Viktoriia
A1  - De Laender, Frederik
A1  - Cabral, Juliano Sarmento
A1  - Boulangeat, Isabelle
A1  - Crawford, Michael Scott
A1  - Bohn, Friedrich
A1  - De Raedt, Jonathan
A1  - Scherer, Cedric
A1  - Svenning, Jens-Christian
A1  - Thonicke, Kirsten
A1  - Schurr, Frank M.
A1  - Grimm, Volker
A1  - Kramer-Schadt, Stephanie
T1  - The dimensionality of stability depends on disturbance type
JF  - Ecology letters
N2  - Ecosystems respond in various ways to disturbances. Quantifying ecological stability therefore requires inspecting multiple stability properties, such as resistance, recovery, persistence and invariability. Correlations among these properties can reduce the dimensionality of stability, simplifying the study of environmental effects on ecosystems. A key question is how the kind of disturbance affects these correlations. We here investigated the effect of three disturbance types (random, species-specific, local) applied at four intensity levels, on the dimensionality of stability at the population and community level. We used previously parameterized models that represent five natural communities, varying in species richness and the number of trophic levels. We found that disturbance type but not intensity affected the dimensionality of stability and only at the population level. The dimensionality of stability also varied greatly among species and communities. Therefore, studying stability cannot be simplified to using a single metric and multi-dimensional assessments are still to be recommended.
KW  - Community model
KW  - disturbance intensity
KW  - disturbance type
KW  - extinction
KW  - individual-based model
KW  - invariability
KW  - persistence
KW  - recovery
KW  - resistance
Y1  - 2019
U6  - https://doi.org/10.1111/ele.13226
SN  - 1461-023X
SN  - 1461-0248
VL  - 22
IS  - 4
SP  - 674
EP  - 684
PB  - Wiley
CY  - Hoboken
ER  - 
TY  - JOUR
A1  - Dalleau, Mayeul
A1  - Kramer-Schadt, Stephanie
A1  - Gangat, Yassine
A1  - Bourjea, Jerome
A1  - Lajoie, Gilles
A1  - Grimm, Volker
T1  - Modeling the emergence of migratory corridors and foraging hot spots of the green sea turtle
JF  - Ecology and evolution
N2  - Environmental factors shape the spatial distribution and dynamics of populations. Understanding how these factors interact with movement behavior is critical for efficient conservation, in particular for migratory species. Adult female green sea turtles, Chelonia mydas, migrate between foraging and nesting sites that are generally separated by thousands of kilometers. As an emblematic endangered species, green turtles have been intensively studied, with a focus on nesting, migration, and foraging. Nevertheless, few attempts integrated these behaviors and their trade‐offs by considering the spatial configurations of foraging and nesting grounds as well as environmental heterogeneity like oceanic currents and food distribution. We developed an individual‐based model to investigate the impact of local environmental conditions on emerging migratory corridors and reproductive output and to thereby identify conservation priority sites. The model integrates movement, nesting, and foraging behavior. Despite being largely conceptual, the model captured realistic movement patterns which confirm field studies. The spatial distribution of migratory corridors and foraging hot spots was mostly constrained by features of the regional landscape, such as nesting site locations, distribution of feeding patches, and oceanic currents. These constraints also explained the mixing patterns in regional forager communities. By implementing alternative decision strategies of the turtles, we found that foraging site fidelity and nesting investment, two characteristics of green turtles' biology, are favorable strategies under unpredictable environmental conditions affecting their habitats. Based on our results, we propose specific guidelines for the regional conservation of green turtles as well as future research suggestions advancing spatial ecology of sea turtles. Being implemented in an easy to learn open‐source software, our model can coevolve with the collection and analysis of new data on energy budget and movement into a generic tool for sea turtle research and conservation. Our modeling approach could also be useful for supporting the conservation of other migratory marine animals.
KW  - connectivity
KW  - corridors
KW  - individual-based model
KW  - migration
KW  - movement
KW  - sea turtle
Y1  - 2019
U6  - https://doi.org/10.1002/ece3.5552
SN  - 2045-7758
VL  - 9
IS  - 18
SP  - 10317
EP  - 10342
PB  - Wiley
CY  - Hoboken
ER  -