2015
DOI: 10.1111/ddi.12362
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Long-term effects of traditional and conservation-oriented forest management on the distribution of vertebrates in Mediterranean forests: a hierarchical hybrid modelling approach

Abstract: Aim Recently, increasing attention has been devoted to the development of sustainable forestry practices aimed at finding a balance between the maintenance and enhancement of different forest resources. However, the long‐term, large‐scale effects of conservation‐oriented forest management on vertebrates have been poorly studied. We tested the hypothesis that conservation‐oriented forest management, being conceived to mimic the dynamics of a natural forest succession more closely than does traditional forestry,… Show more

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Cited by 31 publications
(9 citation statements)
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References 100 publications
(133 reference statements)
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“…We calculated the relative importance of variables from the ensemble model using the functionality provided in the biomod2 package (Jiguet, Barbet‐Massin, & Henry, ). Lastly, we calculated the spatial autocorrelation in regional SDMs residuals through Moran's I correlograms (Pottier et al, ; Di Febbraro et al, ; Supporting Information Appendix , Figure .1).…”
Section: Methodsmentioning
confidence: 99%
See 1 more Smart Citation
“…We calculated the relative importance of variables from the ensemble model using the functionality provided in the biomod2 package (Jiguet, Barbet‐Massin, & Henry, ). Lastly, we calculated the spatial autocorrelation in regional SDMs residuals through Moran's I correlograms (Pottier et al, ; Di Febbraro et al, ; Supporting Information Appendix , Figure .1).…”
Section: Methodsmentioning
confidence: 99%
“…We calculated the relative importance of variables from the ensemble model using the functionality provided in the biomod2 package (Jiguet, Barbet-Massin, & Henry, 2010). Lastly, we calculated the spatial autocorrelation in regional SDMs residuals through Moran's I correlograms (Pottier et al, 2013;Di Febbraro et al, 2015; Supporting Information Appendix S3, Figure S3.1). We projected regional SDMs over two climate change scenarios derived by the fourth assessment of IPCC (IPCC, 2007), as well as two land-use change scenarios developed by Verburg et al (2006).…”
Section: Species Distribution Modelsmentioning
confidence: 99%
“…Current and future potential distributions of the three entities were built in order to evaluate how land‐use change could alter the current competition pattern between the two species by affecting the future potential distributions of their exclusive and shared breeding sites. SDMs were calibrated using a hierarchical structure, from global to regional scale (Gallien et al ., ; Di Febbraro et al ., ). Accordingly, models were first fitted considering the global species range (global SDM, Supporting Information, Appendix ).…”
Section: Methodsmentioning
confidence: 97%
“…The predictive performance of each model was assessed by measuring the area under the receiver operating characteristic curve (AUC; Hanley & McNeil, ) and the true skill statistic (TSS; Allouche, Tsoar & Kadmon, ). To avoid using poorly calibrated models, only projections from models with AUC ≥ 0.75 were considered in all subsequent analyses (Di Febbraro et al ., ). Model averaging was performed by weighting the individual model projections by their AUC scores and averaging the result (Marmion et al ., ).…”
Section: Methodsmentioning
confidence: 97%
“…Dynamic community process‐based forest landscape models (Scheller & Mladenoff, ) such as the LANDIS models (LANDIS‐II and LANDIS PRO; Figure a) that incorporate finer scale climate–vegetation–disturbance interactions compared to bioclimatic DGVMs are ideally suited for this integration (Di Febbraro et al, ; Iverson, Prasad, Matthews, & Peters, ; LeBrun et al, ; Tremblay, Boulanger, Cyr, Taylor, & Price, ). These models could improve woodpecker distribution modeling, especially within the context of multi‐objective management scenarios (Martin, Hurteau, Hungate, Koch, & North, ).…”
Section: Framework Integrationmentioning
confidence: 99%