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Data from: How do habitat amount and habitat fragmentation drive time-delayed responses of biodiversity to land-use change?

Citation

Semper-Pascual, Asunción (2020), Data from: How do habitat amount and habitat fragmentation drive time-delayed responses of biodiversity to land-use change?, Dryad, Dataset, https://doi.org/10.5061/dryad.9zw3r22c5

Abstract

Land-use change is a root cause of the extinction crisis, but links between habitat change and biodiversity loss are not fully understood. While there is evidence that habitat loss is an important extinction driver, the relevance of habitat fragmentation remains debated. Moreover, while time-delays of biodiversity responses to habitat transformation are well-documented, time-delayed effects have been ignored in the habitat loss vs. fragmentation debate. Here, using a hierarchical Bayesian multi- species occupancy framework, we systematically tested for time-delayed responses of bird and mammal communities to habitat loss and to habitat fragmentation. We focused on the Argentine Chaco, where deforestation has been widespread recently. We used an extensive field dataset on birds and mammals, along with a time series of annual woodland maps from 1985-2016 covering recent and historical habitat transformations. Contemporary habitat amount explained bird and mammal occupancy better than past habitat amount. However, occupancy was affected more by past rather than recent fragmentation, indicating a time-delayed response to fragmentation. Considering past landscape patterns is therefore crucial for understanding current biodiversity patterns. Not accounting for land-use history ignores the possibility of extinction debt and can thus obscure impacts of fragmentation, potentially explaining contrasting findings of habitat loss vs. fragmentation studies.

Methods

Biodiversity data:

Birds were recorded at 233 sites during three field surveys between 2009 and 2014, with a mean distance of 4.7-km between sites (standard deviation: SD=6.5-km). We conducted point counts at each site (two to nine point counts per site). Here, we only considered species that use woodland as their main habitat, as this is by far the dominant natural vegetation in the area and we wanted to test for time-delayed responses to woodland loss and fragmentation. We discarded migrant species to avoid seasonal effects and species associated with the Andean Cloud forest (i.e., Yungas) that were only recorded in the Chaco-Yungas ecotone.
Mammals were surveyed during two field surveys between 2013 and 2016, using a total of 198 camera-trap stations. The mean distance between adjacent sites was 1.44-km (SD=1.74). We set cameras off trail where possible, to reduce detection bias associated with targeted sampling. Cameras were active between 14 and 84 trapping days (mean=39.8 days), with a total sampling effort of 7,883 trapping days. We only considered woodland-dependent mammal species. 

Landscape predictors:

We calculated landscape-scale metrics of habitat amount and fragmentation, meaning that they described the spatial characteristics of entire landscapes, not individual patches. We extracted predictors for circular landscapes centered around each sampling site. Based on sensitivity analyses, we used a 4-km radius for birds, and a 2-km radius for mammals. We calculated one landscape predictor representing habitat amount: percentage of woodland. To characterize fragmentation, we calculated three predictors: percentage of edge, patch density and cohesion index. 

To define the contemporary landscape predictors, we related each site to the landscape predictors of the year when biodiversity was sampled (e.g., sites sampled in 2015 were related to the predictors from 2015). We then derived a time series of past landscape predictors 24 years back in time (i.e., landscape patterns from 1 year prior to sampling, 2 years prior to sampling, etc.). We used a maximum time period of 24 years because this is the time span between the oldest Landsat-based woodland map (1985) and the oldest biodiversity sampling (2009) in our dataset.
 

Funding

German Ministry of Education and Research, Award: BMBF, project PASANOA, 031B0034A

German Research Foundation, Award: DFG, project KU 2458/5-1

German Ministry of Education and Research, Award: BMBF, project PASANOA, 031B0034A

German Research Foundation, Award: DFG, project KU 2458/5-1