Biodiversity loss and habitat degradation are big challenges to be tackled by conservation planning since their effects on both ecological and social-economic systems are remarkably detrimental. Efforts to limit anthropogenic impacts on species and habitats need to be assisted by tools for biodiversity monitoring. Effective monitoring tools could help bridge the gap between science and policy, better assess trade-offs between biodiversity and other services, and potentially reduce the associated social costs of conservation. Here, we assessed the feasibility of monitoring habitat quality for bird communities in Central Italy using the InVEST Habitat Quality model. InVEST was parameterized using outputs from species distribution models (SDMs) and expert-based models to explore their viability to support conservation planning. Our results highlight that InVEST parameterized by SDMs produced habitat quality maps that correlated highly with spatial patterns of observed species richness, while the expert-derived InVEST outcomes showed lower correlation. However, the latter approach proved useful as a first-line analysis to identify large-scale areas of conservation concern, where field data and modeling approaches such as SDMs are needed to assess fine-scale conservation value. We show SDM-informed habitat quality maps can accurately identify conservation priority areas, though their applicability is overall limited by data availability. On the other hand, expert-based habitat quality maps can be used as a surrogate approach for preliminary and/or exploratory studies, especially in contexts characterized by poor data availability/quality and budgetary constraints. (C) 2018 The Authors. Published by Elsevier B.V.

Expert-based and correlative models to map habitat quality: Which gives better support to conservation planning?

Di Febbraro M.
Primo
Investigation
;
Sallustio L.
Secondo
Methodology
;
Vizzarri M.
Data Curation
;
Loy A.
Supervision
;
Marchetti M.
Ultimo
Writing – Review & Editing
2018

Abstract

Biodiversity loss and habitat degradation are big challenges to be tackled by conservation planning since their effects on both ecological and social-economic systems are remarkably detrimental. Efforts to limit anthropogenic impacts on species and habitats need to be assisted by tools for biodiversity monitoring. Effective monitoring tools could help bridge the gap between science and policy, better assess trade-offs between biodiversity and other services, and potentially reduce the associated social costs of conservation. Here, we assessed the feasibility of monitoring habitat quality for bird communities in Central Italy using the InVEST Habitat Quality model. InVEST was parameterized using outputs from species distribution models (SDMs) and expert-based models to explore their viability to support conservation planning. Our results highlight that InVEST parameterized by SDMs produced habitat quality maps that correlated highly with spatial patterns of observed species richness, while the expert-derived InVEST outcomes showed lower correlation. However, the latter approach proved useful as a first-line analysis to identify large-scale areas of conservation concern, where field data and modeling approaches such as SDMs are needed to assess fine-scale conservation value. We show SDM-informed habitat quality maps can accurately identify conservation priority areas, though their applicability is overall limited by data availability. On the other hand, expert-based habitat quality maps can be used as a surrogate approach for preliminary and/or exploratory studies, especially in contexts characterized by poor data availability/quality and budgetary constraints. (C) 2018 The Authors. Published by Elsevier B.V.
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11695/89280
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 39
  • ???jsp.display-item.citation.isi??? 37
social impact