GEOAI FOR MARINE ECOSYSTEM MONITORING: A COMPLETE WORKFLOW TO GENERATE MAPS FROM AI MODEL PREDICTIONS

Mapping and monitoring marine ecosystems imply several challenges for data collection and processing: water depth, restricted access to locations, instrumentation costs or weather constraints for sampling, among others. Nowadays, Artificial Intelligence (AI) and Geographic Information System (GIS) open source software can be combined in new kinds of workflows, to annotate and predict objects directly on georeferenced raster data (e.g. orthomosaics). Here, we describe and share the code of a generic method to train a deep learning model with spatial annotations and use it to directly generate model predictions as spatial features. This workflow has been tested and validated in three use cases related to marine ecosystem monitoring at different geographic scales: (i) segmentation of corals on orthomosaics made of underwater images to automate coral reef habitats mapping, (ii) detection and classification of fishing vessels on remote sensing satellite imagery to estimate a proxy of fishing effort (iii) segmentation of marine species and habitats on underwater images with a simple geolocation. Models have been successfully trained and the models predictions are displayed with maps in the three use cases

The Diversity of Coral Reefs: What Are We Missing?

Tropical reefs shelter one quarter to one third of all marine species but one third of the coral species that construct reefs are now at risk of extinction. Because traditional methods for assessing reef diversity are extremely time consuming, taxonomic expertise for many groups is lacking, and marine organisms are thought to be less vulnerable to extinction, most discussions of reef conservation focus on maintenance of ecosystem services rather than biodiversity loss. In this study involving the three major oceans with reef growth, we provide new biodiversity estimates based on quantitative sampling and DNA barcoding. We focus on crustaceans, which are the second most diverse group of marine metazoans. We show exceptionally high numbers of crustacean species associated with coral reefs relative to sampling effort (525 species from a combined, globally distributed sample area of 6.3 m2). The high prevalence of rare species (38% encountered only once), the low level of spatial overlap (81% found in only one locality) and the biogeographic patterns of diversity detected (Indo-West Pacific>Central Pacific>Caribbean) are consistent with results from traditional survey methods, making this approach a reliable and efficient method for assessing and monitoring biodiversity. The finding of such large numbers of species in a small total area suggests that coral reef diversity is seriously under-detected using traditional survey methods, and by implication, underestimated

A Climatic Stability Approach to Prioritizing Global Conservation Investments

Climate change is impacting species and ecosystems globally. Many existing templates to identify the most important areas to conserve terrestrial biodiversity at the global scale neglect the future impacts of climate change. Unstable climatic conditions are predicted to undermine conservation investments in the future. This paper presents an approach to developing a resource allocation algorithm for conservation investment that incorporates the ecological stability of ecoregions under climate change. We discover that allocating funds in this way changes the optimal schedule of global investments both spatially and temporally. This allocation reduces the biodiversity loss of terrestrial endemic species from protected areas due to climate change by 22% for the period of 2002–2052, when compared to allocations that do not consider climate change. To maximize the resilience of global biodiversity to climate change we recommend that funding be increased in ecoregions located in the tropics and/or mid-elevation habitats, where climatic conditions are predicted to remain relatively stable. Accounting for the ecological stability of ecoregions provides a realistic approach to incorporating climate change into global conservation planning, with potential to save more species from extinction in the long term

Small mammal responses to Amazonian forest islands are modulated by their forest dependence

Hydroelectric dams have induced widespread loss, fragmentation and degradation of terrestrial habitats in lowland tropical forests. Yet their ecological impacts have been widely neglected, particularly in developing countries, which are currently earmarked for exponential hydropower development. Here we assess small mammal assemblage responses to Amazonian forest habitat insularization induced by the 28-year-old Balbina Hydroelectric Dam. We sampled small mammals on 25 forest islands (0.83–1466 ha) and four continuous forest sites in the mainland to assess the overall community structure and species-specific responses to forest insularization. We classified all species according to their degree of forest-dependency using a multi-scale approach, considering landscape, patch and local habitat characteristics. Based on 65,520 trap-nights, we recorded 884 individuals of at least 22 small mammal species. Species richness was best predicted by island area and isolation, with small islands ( 200 ha; 10.8 ± 1.3 species) and continuous forest sites (∞ ha; 12.5 ± 2.5 species) exhibited similarly high species richness. Forest-dependent species showed higher local extinction rates and were often either absent or persisted at low abundances on small islands, where non-forest-dependent species became hyper-abundant. Species capacity to use non-forest habitat matrices appears to dictate small mammal success in small isolated islands. We suggest that ecosystem functioning may be highly disrupted on small islands, which account for 62.7% of all 3546 islands in the Balbina Reservoir

Multigenic phylogeny and analysis of tree incongruences in Triticeae (Poaceae)

Background: Introgressive events (e.g., hybridization, gene flow, horizontal gene transfer) and incomplete lineage sorting of ancestral polymorphisms are a challenge for phylogenetic analyses since different genes may exhibit conflicting genealogical histories. Grasses of the Triticeae tribe provide a particularly striking example of incongruence among gene trees. Previous phylogenies, mostly inferred with one gene, are in conflict for several taxon positions. Therefore, obtaining a resolved picture of relationships among genera and species of this tribe has been a challenging task. Here, we obtain the most comprehensive molecular dataset to date in Triticeae, including one chloroplastic and 26 nuclear genes. We aim to test whether it is possible to infer phylogenetic relationships in the face of (potentially) large-scale introgressive events and/or incomplete lineage sorting; to identify parts of the evolutionary history that have not evolved in a tree-like manner; and to decipher the biological causes of genetree conflicts in this tribe. Results: We obtain resolved phylogenetic hypotheses using the supermatrix and Bayesian Concordance Factors (BCF) approaches despite numerous incongruences among gene trees. These phylogenies suggest the existence of 4-5 major clades within Triticeae, with Psathyrostachys and Hordeum being the deepest genera. In addition, we construct a multigenic network that highlights parts of the Triticeae history that have not evolved in a tree-lik

The Biodiversity of the Mediterranean Sea: Estimates, Patterns, and Threats

The Mediterranean Sea is a marine biodiversity hot spot. Here we combined an extensive literature analysis with expert opinions to update publicly available estimates of major taxa in this marine ecosystem and to revise and update several species lists. We also assessed overall spatial and temporal patterns of species diversity and identified major changes and threats. Our results listed approximately 17,000 marine species occurring in the Mediterranean Sea. However, our estimates of marine diversity are still incomplete as yet—undescribed species will be added in the future. Diversity for microbes is substantially underestimated, and the deep-sea areas and portions of the southern and eastern region are still poorly known. In addition, the invasion of alien species is a crucial factor that will continue to change the biodiversity of the Mediterranean, mainly in its eastern basin that can spread rapidly northwards and westwards due to the warming of the Mediterranean Sea. Spatial patterns showed a general decrease in biodiversity from northwestern to southeastern regions following a gradient of production, with some exceptions and caution due to gaps in our knowledge of the biota along the southern and eastern rims. Biodiversity was also generally higher in coastal areas and continental shelves, and decreases with depth. Temporal trends indicated that overexploitation and habitat loss have been the main human drivers of historical changes in biodiversity. At present, habitat loss and degradation, followed by fishing impacts, pollution, climate change, eutrophication, and the establishment of alien species are the most important threats and affect the greatest number of taxonomic groups. All these impacts are expected to grow in importance in the future, especially climate change and habitat degradation. The spatial identification of hot spots highlighted the ecological importance of most of the western Mediterranean shelves (and in particular, the Strait of Gibraltar and the adjacent Alboran Sea), western African coast, the Adriatic, and the Aegean Sea, which show high concentrations of endangered, threatened, or vulnerable species. The Levantine Basin, severely impacted by the invasion of species, is endangered as well

The island species-area relationship: Biology and statistics

Aim We conducted the most extensive quantitative analysis yet undertaken of the form taken by the island species-area relationship (ISAR), among 20 models, to determine: (1) the best-fit model, (2) the best-fit model family, (3) the best-fit ISAR shape (and presence of an asymptote), (4) system properties that may explain ISAR form, and (5) parameter values and interpretation of the logarithmic implementation of the power model. Location World-wide. Methods We amassed 601 data sets from terrestrial islands and employed an information-theoretic framework to test for the best-fit ISAR model, family, and shape, and for the presence/absence of an asymptote. Two main criteria were applied: generality (the proportion of cases for which the model provided an adequate fit) and efficiency (the overall probability of a model, when adequate, being the best at explaining ISARs; evaluated using the mean overall AIC c weight). Multivariate analyses were used to explore the potential of island system properties to explain trends in ISAR form, and to describe variation in the parameters of the logarithmic power model. Results Adequate fits were obtained for 465 data sets. The simpler models performed best, with the power model ranked first. Similar results were obtained at model family level. The ISAR form is most commonly convex upwards, without an asymptote. Island system traits had low descriptive power in relation to variation in ISAR form. However, the z and c parameters of the logarithmic power model show significant pattern in relation to island system type and taxon. Main conclusions Over most scales of space, ISARs are best represented by the power model and other simple models. More complex, sigmoid models may be applicable when the spatial range exceeds three orders of magnitude. With respect to the log power model, z-values are indicative of the process(es) establishing species richness and composition patterns, while c-values are indicative of the realized carrying capacity of the system per unit area. Variation in ISAR form is biologically meaningful, but the signal is noisy, as multiple processes constrain the ecological space available within island systems and the relative importance of these processes varies with the spatial scale of the system. © 2011 Blackwell Publishing Ltd

Fish diversity patterns in the Mediterranean Sea: deviations from a mid-domain model

International audienc

Taxonomic and regional uncertainty in species-area relationships and the identification of richness hotspots

Copyright © 2008 National Academy of SciencesSpecies-area relationships (SARs) are fundamental to the study of key and high-profile issues in conservation biology and are particularly widely used in establishing the broad patterns of biodiversity that underpin approaches to determining priority areas for biological conservation. Classically, the SAR has been argued in general to conform to a power-law relationship, and this form has been widely assumed in most applications in the field of conservation biology. Here, using nonlinear regressions within an information theoretical model selection framework, we included uncertainty regarding both model selection and parameter estimation in SAR modeling and conducted a global-scale analysis of the form of SARs for vascular plants and major vertebrate groups across 792 terrestrial ecoregions representing almost 97% of Earth's inhabited land. The results revealed a high level of uncertainty in model selection across biomes and taxa, and that the power-law model is clearly the most appropriate in only a minority of cases. Incorporating this uncertainty into a hotspots analysis using multimodel SARs led to the identification of a dramatically different set of global richness hotspots than when the power-law SAR was assumed. Our findings suggest that the results of analyses that assume a power-law model may be at severe odds with real ecological patterns, raising significant concerns for conservation priority-setting schemes and biogeographical studies