The optimal sampling design for littoral habitats modelling: A case study from the north-western Mediterranean

Cefalì, Maria Elena; Ballesteros, Enric; Riera, Joan Lluís; Chappuis, Eglantine; Terradas, Marc; Mariani, Simone; Cebrián, Emma

doi:10.1371/journal.pone.0197234

Cited by 6 publications

(2 citation statements)

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“…On the other hand, the spatial resolution of our study, imposed by the available resolution for predictor variables, precluded a species-specific distribution assessment. Fine-scale data are indeed needed in order to improve the model in this sense (Cefalì et al, 2018). Our database suffers from all the limitations already described across the literature.…”

Section: Discussionmentioning

confidence: 99%

Modeling Macroalgal Forest Distribution at Mediterranean Scale: Present Status, Drivers of Changes and Insights for Conservation and Management

et al. 2020

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Macroalgal forests are one of the most productive and valuable marine ecosystems, but yet strongly exposed to fragmentation and loss. Detailed large-scale information on their distribution is largely lacking, hindering conservation initiatives. In this study, a systematic effort to combine spatial data on Cystoseira C. Agardh canopies (Fucales, Phaeophyta) was carried out to develop a Habitat Suitability Model (HSM) at Mediterranean scale, providing critical tools to improve site prioritization for their management, restoration and protection. A georeferenced database on the occurrence of 20 Cystoseira species was produced collecting all the available information from published and grey literature, web data portals and co-authors personal data. Data were associated to 55 predictor variable layers in the (ASCII) raster format and were used in order to develop the HSM by means of a Random Forest, a very effective Machine Learning technique. Knowledge about the distribution of Cystoseira canopies was available for about the 14% of the Mediterranean coastline. Absence data were available only for the 2% of the basin. Despite these gaps, our HSM showed high accuracy levels in reproducing Cystoseira distribution so that the first continuous maps of the habitat across the entire basin was produced. Misclassification errors mainly occurred in the eastern and southern part of

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Section: Discussionmentioning

confidence: 99%

Modeling Macroalgal Forest Distribution at Mediterranean Scale: Present Status, Drivers of Changes and Insights for Conservation and Management

et al. 2020

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“…Modelling combines multiple predictor variables (e.g. coastline geomorphology, temperature, human pressures; see Cefalì et al, 2016;Cefalì et al, 2018;Fabbrizzi et al, 2020) and target species occurrence data. The quality of data feeding HSMs is of paramount importance, and planning large-scale restoration interventions in the absence of finescale information may seriously compromise output accuracy.…”

Section: Habitat Suitabilitymentioning

confidence: 99%

A decision-support framework for the restoration of Cystoseira sensu lato forests

Smith,

Verdura,

Papadopoulou

et al. 2023

Front. Mar. Sci.

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Macroalgal forests characterised by species of the genus Cystoseira sensu lato form important shallow coastal rocky habitats in the Mediterranean Sea. These forests support a high biodiversity and provide important ecosystem services and societal benefits. Currently these habitats are often in a poor condition in many areas, due to loss and degradation from both anthropogenic and climate stressors. Restoration has recently moved to the forefront of the United Nations and European Union agendas to reverse this trend, particularly in the last decade with the implementation of various international policies. However, this has been in the form of generic targets (e.g., restoration of 30% of degraded habitats by 2030) and has not been linked to specifically what habitat or species to restore, where and how. Initial targets have been missed, new targets are expected through the proposed EU Nature Restoration Law, but overall guidance is still lacking. There are few specific guides to marine habitat restoration limited to mostly seagrass, corals and shellfish. As a priority action for the recovery of coastal marine ecosystems a decision-support framework has been developed for the restoration of Mediterranean macroalgal forests, comprising a stepwise decision tree with additional descriptions of key elements to be considered for a restoration action. The decision tree includes steps concerning current and historical forest presence, site local condition assessment and choice of actions. Key considerations include restoration implementation (competence, society and support, finance and governance), success evaluation (at the target species and the ecosystem level) and long-term management. The framework builds on existing work on Cystoseira s.l. restoration, the work carried out in the EU AFRIMED project, but also on principles and guidelines in place for both generic and specific marine habitats. The work reported here has involved the expertise of scientists and information from stakeholders. Gaps were identified and recommendations were made, dealing with stressors, coordinating and networking stakeholders, integrating top down policy and bottom up initiatives, funding of restoration actions, establishing synergies between restoration, conservation and marine spatial planning and finally communication and publicity.

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Temporal transferability of marine distribution models: The role of algorithm selection

Hoz

Ramos

Puente

et al. 2019

Ecological Indicators

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The optimal sampling design for littoral habitats modelling: A case study from the north-western Mediterranean

Cited by 6 publications

References 58 publications

Modeling Macroalgal Forest Distribution at Mediterranean Scale: Present Status, Drivers of Changes and Insights for Conservation and Management

Modeling Macroalgal Forest Distribution at Mediterranean Scale: Present Status, Drivers of Changes and Insights for Conservation and Management

A decision-support framework for the restoration of Cystoseira sensu lato forests

Temporal transferability of marine distribution models: The role of algorithm selection

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