During the last fifty years, there has been a dramatic increase in the development of anthropogenic activities, and this is particularly threatening to marine coastal ecosystems. The management of these multiple and simultaneous anthropogenic pressures requires reliable and precise data on their distribution, as well as information (data, modelling) on their potential effects on sensitive ecosystems. Focusing on Posidonia oceanica beds, a threatened habitat-forming seagrass species endemic to the Mediterranean, we developed a statistical approach to study the complex relationship between human multiple activities and ecosystem status. We used Random Forest modelling to explain the degradation status of P. oceanica (defined herein as the shift from seagrass bed to dead matte) as a function of depth and 10 anthropogenic pressures along the French Mediterranean coast (1700 km of coastline including Corsica). Using a 50 x 50 m grid cells dataset, we obtained a particularly accurate model explaining 71.3 % of the variance, with a Pearson correlation of 0.84 between predicted and observed values. Human-made coastline, depth, coastal population, urbanization, and agriculture were the best global predictors of P. oceanica's degradation status. Aquaculture was the least important predictor, although its local individual influence was among the highest. Non-linear relationship between predictors and seagrass beds status was detected with tipping points (i.e. thresholds) for all variables except agriculture and industrial effluents. Using these tipping points, we built a map representing the coastal seagrass beds classified into four categories according to an increasing pressure gradient and its risk of phase shift. Our approach provides important information that can be used to help managers preserve this essential and endangered ecosystem.
Mesophotic marine ecosystems are characterized by lower light penetration supporting specialized fish fauna. Due to their depths (−30-−150 m), accessibility is challenging, and the structure of mesophotic fish assemblages is generally less known than either shallow reefs or deep zones with soft bottoms which are generally trawled.
Scalable assessments of biodiversity are required to successfully and adaptively manage coastal ecosystems. Assessments must account for habitat variations at multiple spatial scales, including the small scales (<100 m) at which biotic and abiotic habitat components structure the distribution of fauna, including fishes. Associated challenges include achieving consistent habitat descriptions and upscaling from in situ‐monitored stations to larger scales. We developed a methodology for (a) determining habitat types consistent across scales within large management units, (b) characterizing heterogeneities within each habitat, and (c) predicting habitat from new survey data. It relies on clustering techniques and supervised classification rules and was applied to a set of 3,145 underwater video observations of fish and benthic habitats collected in all reef and lagoon habitats around New Caledonia. A baseline habitat typology was established with five habitat types clearly characterized by abiotic and biotic attributes. In a complex mosaic of habitats, habitat type is an indispensable covariate for explaining spatial variations in fish communities. Habitat types were further described by 26 rules capturing the range of habitat features encountered. Rules provided intuitive habitat descriptions and predicted habitat type for new monitoring observations, both straightforwardly and with known confidence. Images are convenient for interacting with managers and stakeholders. Our scheme is (a) consistent at the scale of New Caledonia reefs and lagoons (1.4 million km 2 ) and (b) ubiquitous by providing data in all habitats, for example, showcasing a substantial fish abundance in rarely monitored soft‐bottom habitats. Both features must be part of an ecosystem‐based monitoring strategy relevant for management. This is the first study applying data mining techniques to in situ measurements to characterize coastal habitats over regional‐scale management areas. This approach can be applied to other types of observations and other ecosystems to characterize and predict local ecological assets for assessments at larger scales.
1. To mitigate the ongoing threats to coastal ecosystems, and the biodiversity erosion they are causing, marine-protected areas (MPAs) have emerged as powerful and widespread conservation tools. Strictly no-take MPAs, also called marine reserves, undeniably promote fish biomass and density, but it remains unclear how biodiversity responds to protection. Identifying which facets of biodiversity respond to protection is critical for the management of MPAs and the development of relevant conservation strategies towards the achievement of biodiversity targets.2. We collected 99 environmental DNA (eDNA) samples inside and outside nine marine reserves in the Mediterranean Sea to assess the effect of protection on 11 biodiversity indicators based on fish traits, phylogeny and vulnerability to fishing. We controlled for the effect of environmental heterogeneity (habitat, bathymetry, productivity, temperature and accessibility) using a principal component analysis, and for spatial autocorrelation due to potential unmeasured factors.3. We found a positive and significant effect of protection on only 3 out of 11 indicators: functional and phylogenic diversity but also the ratio between demersopelagic and benthic species richness. Rather, total fish richness responded significantly and negatively to protection. We did not detect any significant effect of protection on threatened and elasmobranch species richness, probably due to their large home range compared to the size of Mediterranean marine reserves.
Aim To determine the ecoregions (spatial marine areas with similar environmental and physical conditions associated with relatively homogeneous fish assemblages) for shallow reef fish assemblages based on predictive models of beta diversity (β‐diversity) that account for both large‐scale environmental factors and local habitat characteristics. We assessed the influence of a spatial scale to rank the importance of these factors. Location New Caledonian (south‐west Pacific Ocean, 17–24° S, 158–172° W) Exclusive Economic Zone, Coral Sea Marine Park. Taxon Fish. Methods Fish and habitat data that were collected at 13 sites around New Caledonia using unbaited rotating underwater video (285 sampling stations) were analysed. Gradient forest modelling was used to predict the fish β‐diversity along the gradients of environmental factors. Ecoregions were obtained by applying clustering methods to gradient forest predictions. Results The gradient forest models of β‐diversity retained 59 species (total: 206 fish species) with R² > 0, including 19 fish species with R² from 0.03% to 69%. For these 19 species, the models explained up to 26% of the variance. At a large scale, β‐diversity was significantly explained by nutrient concentrations, sea surface salinity and temperature. Among the eight ecoregions that were delineated based on the β‐diversity predictions, three regions corresponded to remote sites under oceanic influence where human pressures are low and the surface nutrient concentrations are high. On the local scale, the benthic habitat explained β‐diversity better than the physical and chemical parameters, particularly in the areas subject to anthropogenic pressures. Main conclusions On the local scale, the respective importance of environmental factors (physical and chemical parameters versus benthic habitat) differed according to ecosystem health. Our findings suggest that nutrient enrichment due to avifauna may have a positive effect on fish β‐diversity when an ecosystem is healthy. The ecoregions reflect fish species composition in relation to a large set of environmental parameters.
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