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Boavida, J., Assis, J., Silva, I., & Serrao, E. A. (2016). Overlooked habitat of a vulnerable gorgonian revealed in the Mediterranean and Eastern Atlantic by ecological niche modelling. Sci Rep, 6, 36460.
Résumé: Factors shaping the distribution of mesophotic octocorals (30-200 m depth) remain poorly understood, potentially leaving overlooked coral areas, particularly near their bathymetric and geographic distributional limits. Yet, detailed knowledge about habitat requirements is crucial for conservation of sensitive gorgonians. Here we use Ecological Niche Modelling (ENM) relating thirteen environmental predictors and a highly comprehensive presence dataset, enhanced by SCUBA diving surveys, to investigate the suitable habitat of an important structuring species, Paramuricea clavata, throughout its distribution (Mediterranean and adjacent Atlantic). Models showed that temperature (11.5-25.5 degrees C) and slope are the most important predictors carving the niche of P. clavata. Prediction throughout the full distribution (TSS 0.9) included known locations of P. clavata alongside with previously unknown or unreported sites along the coast of Portugal and Africa, including seamounts. These predictions increase the understanding of the potential distribution for the northern Mediterranean and indicate suitable hard bottom areas down to > 150 m depth. Poorly sampled habitats with predicted presence along Algeria, Alboran Sea and adjacent Atlantic coasts encourage further investigation. We propose that surveys of target areas from the predicted distribution map, together with local expert knowledge, may lead to discoveries of new P. clavata sites and identify priority conservation areas.
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Bouchet, P. J., Miller, D. L., Roberts, J. J., Mannocci, L., Harris, C. M., & Thomas, L. (2020). dsmextra: Extrapolation assessment tools for density surface models. Methods Ecol. Evol., 11(11), 1464–1469.
Résumé: Forecasting the responses of biodiversity to global change has never been more important. However, many ecologists faced with limited sample sizes and shoestring budgets often resort to extrapolating predictive models beyond the range of their data to support management actions in data-deficient contexts. This can lead to error-prone inference that has the potential to misdirect conservation interventions and undermine decision-making. Despite the perils associated with extrapolation, little guidance exists on the best way to identify it when it occurs, leaving users questioning how much credence they should place in model outputs. To address this, we present dsmextra, a new R package for measuring, summarizing and visualizing extrapolation in multivariate environmental space. dsmextra automates the process of conducting quantitative, spatially explicit assessments of extrapolation on the basis of two established metrics: the Extrapolation Detection (ExDet) tool and the percentage of data nearby (%N). The package provides user-friendly functions to (a) calculate these metrics, (b) create tabular and graphical summaries, (c) explore combinations of covariate sets as a means of informing covariate selection and (d) produce visual displays in the form of interactive html maps. dsmextra implements a model-agnostic approach to extrapolation detection that is applicable across taxonomic groups, modelling techniques and datasets. We present a case study fitting a density surface model to visual detections of pantropical spotted dolphinsStenella attenuatain the Gulf of Mexico. Predictive modelling seeks to deliver actionable information about the states and trajectories of ecological systems, yet model performance can be strongly impaired out of sample. By assessing conditions under which models are likely to fail or succeed in extrapolating, ecologists may gain a better understanding of biological patterns and their underlying drivers. Critical to this is a concerted effort to standardize best practice in model evaluation, with an emphasis on extrapolative capacity.
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Gruss, A., Yemane, D., & Fairweather, T. P. (2016). Exploring the spatial distribution patterns of South African Cape hakes using generalised additive models. Afr. J. Mar. Sci., 38(3), 395–409.
Résumé: We developed delta generalised additive models (GAMs) to predict the spatial distribution of different size classes of South African hakes, Merluccius capensis and M. paradoxus, using demersal trawl survey data and geographical (latitude and longitude) and environmental features (depth, temperature, bottom dissolved oxygen and sediment type). Our approach consists of fitting, for each hake size class, two independent models, a binomial GAM and a quasi-Poisson GAM, whose predictions are then combined using the delta method. Delta GAMs were validated using an iterative cross-validation procedure, and their predictions were then employed to produce distribution maps for the southern Benguela. Delta GAM predictions confirmed existing knowledge about the spatial distribution patterns of South African hakes, and brought new insights into the factors influencing the presence/absence and abundance of these species. Our GAM approach can be used to produce distribution maps for spatially explicit ecosystem models of the southern Benguela in a rigorous and objective way. Ecosystem models are critical features of the ecosystem approach to fisheries, and distribution maps constructed using our GAM approach will enable a reliable allocation of species biomasses in spatially explicit ecosystem models, which will increase trust in the spatial overlaps and, therefore, the trophic interactions predicted by these models.
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Saeedi, H., Reimer, J. D., Brandt, M., Dumais, P. - O., Jazdzewska, A. M., Jeffery, N. W., et al. (2019). Global marine biodiversity in the context of achieving the Aichi Targets: ways forward and addressing data gaps. PeerJ, 7, e7221.
Résumé: In 2010, the Conference of the Parties of the Convention on Biological Diversity agreed on the Strategic Plan for Biodiversity 2011-2020 in Aichi Prefecture, Japan. As this plan approaches its end, we discussed whether marine biodiversity and prediction studies were nearing the Aichi Targets during the 4th World Conference on Marine Biodiversity held in Montreal, Canada in June 2018. This article summarises the outcome of a five-day group discussion on how global marine biodiversity studies should be focused further to better understand the patterns of biodiversity. We discussed and reviewed seven fundamental biodiversity priorities related to nine Aichi Targets focusing on global biodiversity discovery and predictions to improve and enhance biodiversity data standards (quantity and quality), tools and techniques, spatial and temporal scale framing, and stewardship and dissemination. We discuss how identifying biodiversity knowledge gaps and promoting efforts have and will reduce such gaps, including via the use of new databases, tools and technology, and how these resources could be improved in the future. The group recognised significant progress toward Target 19 in relation to scientific knowledge, but negligible progress with regard to Targets 6 to 13 which aimed to safeguard and reduce human impacts on biodiversity.
Mots-Clés: Aichi targets; assemblages; benefits; Biodiversity tools and pipelines; Biogeography; conservation; coral-reefs; Data standard; Data standards; deep-sea; Discovery; Dissemination; diversity gradient; life; Marine biodiversity; patterns; Prediction; progress; species richness; Stewardship; Stewardship and dissemination; Tools and pipelines
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