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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 species–area relationship (SAR) constitutes one of the most general ecological patterns globally. A number of different SAR models have been proposed. Recent work has shown that no single model universally provides the best fit to empirical SAR datasets: multiple models may be of practical and theoretical interest. However, there are no software packages available that a) allow users to fit the full range of published SAR models, or b) provide functions to undertake a range of additional SAR-related analyses. To address these needs, we have developed the R package ‘sars’ that provides a wide variety of SAR-related functionality. The package provides functions to: a) fit 20 SAR models using non-linear and linear regression, b) calculate multi-model averaged curves using various information criteria, and c) generate confidence intervals using bootstrapping. Plotting functions allow users to depict and scrutinize the fits of individual models and multi-model averaged curves. The package also provides additional SAR functionality, including functions to fit, plot and evaluate the random placement model using a species–sites abundance matrix, and to fit the general dynamic model of oceanic island biogeography. The ‘sars’ R package will aid future SAR research by providing a comprehensive set of simple to use tools that enable in-depth exploration of SARs and SAR-related patterns. The package has been designed to allow other researchers to add new functions and models in the future and thus the package represents a resource for future SAR work that can be built on and expanded by workers in the field. © 2019 The Authors. Ecography © 2019 Nordic Society Oikos

The diversity of natural communities is classically estimated through species identification (taxonomic diversity) but can also be estimated from the ecological functions performed by the species (functional diversity), or from the phylogenetic relationships among them (phylogenetic diversity). Estimating functional diversity requires the definition of specific functional traits, i.e., phenotypic characteristics that impact fitness and are relevant to ecosystem functioning. Estimating phylogenetic diversity requires the description of phylogenetic relationships, for instance by using molecular tools. In the present study, we computed the functional and the phylogenetic diversity of copepod surface communities in the Mediterranean Sea and compared these different biodiversity facets among ecoregions. Firstly, we implemented a specific trait database for the most commonly-sampled and abundant copepod species of the Mediterranean Sea. Our database includes 191 species, described by seven traits encompassing diverse ecological functions: minimal and maximal body length, trophic group, feeding type, spawning strategy, diel vertical migration and vertical habitat. Clustering analysis in the functional trait space revealed that Mediterranean copepods can be gathered into groups that have different ecological roles. Secondly, we reconstructed a phylogenetic tree using the available sequences of 18S rRNA. Our tree included 154 of the analyzed Mediterranean copepod species. Then, we used these two datasets to describe the functional and phylogenetic diversity of copepod surface communities in the Mediterranean Sea and compare them among ecoregions. The replacement component (turn-over) and the species richness difference component (nestedness) of the beta diversity indices were identified. Finally, by comparing various and complementary aspects of plankton diversity (taxonomic, functional, and phylogenetic diversity) we were able to gain a better understanding of the relationships among the zooplankton community, biodiversity, ecosystem function, and environmental forcing.

Aim The relationship between species number and area is of fundamental importance within macroecology and conservation science. Yet, the implications of different means of quantitative depiction of the relationship remain contentious. We set out (i) to establish the variation in form of the relationship between two distinct methods applied to the same habitat island datasets, (ii) to explore the relevance of several key dataset properties for variation in parameters of these relationships, and (iii) to assess implications for applications of the resulting models. Locations Global Methods Through literature search we compiled 97 habitat island datasets. For each we analysed the form of the island species–area relationship (ISAR) and several versions of species accumulation curve (SAC), giving priority to a randomized form (Ran-SAC). Having established the validity of the power model, we compared the slopes (z-values) between the ISAR and the SAC for each dataset. We used boosted regression tree and simulation analyses to investigate the effect of nestedness and other variables in driving observed differences in z values between ISARs and SACs. Results The Ran-SAC was steeper than the ISAR in 77% of datasets. The differences were primarily driven by the degree of nestedness, although other variables (e.g. number of islands in a dataset) were also important. The ISAR was often a poor predictor of archipelago species richness. Main conclusions Slopes of the ISAR and SAC for the same data set can vary substantially, revealing their non-equivalence, with implications for applications of species–area curve parameters in conservation science. For example, the ISAR was a poor predictor of archipelagic richness in datasets with a low degree of nestedness. Caution should be employed when using the ISAR for extrapolation and prediction purposes in habitat island systems.

Aim: We undertook the largest comparative study to date of the form of the island species–area relationship (ISAR) using 207 habitat island datasets and 601 true island datasets. We also undertook analyses of (a) the factors influencing z- and c-values of the power (log–log) model and (b) how z and c vary between different island types. Location: Global. Methods: We used an information theoretic approach to compare the fit of 20 ISAR models to 207 habitat island datasets. Model performance was ranked according to pre-set criteria, including metrics of generality and efficiency. We also fitted the power (log–log) model to each dataset and analysed variation in parameter estimates and model fits as a function of key dataset characteristics using linear models and constrained analysis of principal coordinates. Results: The power (nonlinear) model provided the best fit to the most datasets, and was the highest ranked model overall. In general, the more complex models performed badly. Average z-values were significantly lower for habitat island datasets than for true islands, and were higher for mountaintop and urban habitat islands than for other habitat island types. Average c-values were significantly lower for oceanic islands, and significantly higher for inland water-body islands, than for habitat islands. Values of z and c were related to dataset characteristics including the ratio of the largest to smallest island and the maximum and minimum richness values in a dataset. Main conclusions: Our multimodel comparisons demonstrated the nonlinear implementation of the power model to be the best overall model and thus to be a sensible choice for general use. As the z-value of the log–log power model varied in relation to ecological and geographical properties of the study systems, caution should be employed when using canonical values for applied purposes. © 2015 John Wiley & Sons Ltd

Aim: The relationship between species number and area is of fundamental importance in macroecology and conservation science, yet the implications of different means of quantitative depiction of the relationship remain contentious. We set out (1) to establish the variation in form of the relationship between two distinct methods applied to the same habitat island datasets, (2) to explore the relevance of several key dataset properties for variation in the parameters of these relationships, and (3) to assess the implications for application of the resulting models. Locations: Global. Methods: Through literature search we compiled 97 habitat island datasets. For each we analysed the form of the island species-area relationship (ISAR) and several versions of species accumulation curve (SAC), giving priority to a randomized form (Ran-SAC). Having established the validity of the power model, we compared the slopes (z-values) between the ISAR and the SAC for each dataset. We used boosted regression tree and simulation analyses to investigate the effect of nestedness and other variables in driving observed differences in z-values between ISARs and SACs. Results: The Ran-SAC was steeper than the ISAR in 77% of datasets. The differences were primarily driven by the degree of nestedness, although other variables (e.g. the number of islands in a dataset) were also important. The ISAR was often a poor predictor of archipelago species richness. Main conclusions: Slopes of the ISAR and SAC for the same data set can vary substantially, revealing their non-equivalence, with implications for applications of species-area curve parameters in conservation science. For example, the ISAR was a poor predictor of archipelagic richness in datasets with a low degree of nestedness. Caution should be employed when using the ISAR for the purposes of extrapolation and prediction in habitat island systems. © 2016 John Wiley & Sons Ltd.

Aim: Understanding the mechanisms that generate diversity patterns requires analyses at spatial and temporal scales that are appropriate to the dispersal capacities and ecological requirements of organisms. Oceanic archipelagos provide a range of island sizes and configurations which should predictably influence colonization, diversification and extinction. To explore the influence of these factors on archipelagic diversity, we relate the numbers of native and endemic species of vascular plants, birds, land snails and spiders - taxa having different dispersal capabilities and population densities - to the number and sizes of islands in the major oceanic archipelagos of the globe. Location: Fourteen major oceanic archipelagos of the globe. Methods: Species richness was collated for native and endemic species in each archipelago. We used linear mixed effect models to quantify the influence on diversity of total area, number of islands, isolation and latitude. We then applied process-based modelling in a Bayesian framework to evaluate how speciation, colonization and extinction are influenced by characteristics of archipelagos associated with species richness, i.e. area, isolation and number of islands. Results: We found parallel scaling of species richness among taxa with respect to total area and number of islands across groups. The process-based model supported effects of isolation on colonization and of area and number of islands on extinction rates, with the scaling exponents mostly similar across taxa. Data are consistent with a range of scaling exponents for speciation rate, implying that those relationships are difficult to infer from the data used. Conclusions: We demonstrate an unexpected parallel scaling of species richness of four taxa with area and number of islands for the major oceanic archipelagos of the globe. We infer that this scaling arises through similar effects of the physical characteristics of archipelagos on extinction, colonization and speciation rates across these disparate taxa, indicating that similar mechanisms have created variation in diversity. © 2015 John Wiley & Sons Ltd.

Analyses of species-diversity patterns of remote islands have been crucial to the development of biogeographic theory, yet little is known about corresponding patterns in functional traits on islands and how, for example, they may be affected by the introduction of exotic species. We collated trait data for spiders and beetles and used a functional diversity index (FRic) to test for nonrandomness in the contribution of endemic, other native (also combined as indigenous), and exotic species to functional-trait space across the nine islands of the Azores. In general, for both taxa and for each distributional category, functional diversity increases with species richness, which, in turn scales with island area. Null simulations support the hypothesis that each distributional group contributes to functional diversity in proportion to their species richness. Exotic spiders have added novel trait space to a greater degree than have exotic beetles, likely indicating greater impact of the reduction of immigration filters and/or differential historical losses of indigenous species. Analyses of species occurring in native-forest remnants provide limited indications of the operation of habitat filtering of exotics for three islands, but only for beetles. Although the general linear (not saturating) pattern of trait-space increase with richness of exotics suggests an ongoing process of functional enrichment and accommodation, further work is urgently needed to determine how estimates of extinction debt of indigenous species should be adjusted in the light of these findings. © 2014 PNAS.

Aim: Although the increase in species richness with increasing area is considered one of the few laws in ecology, the role of environmental and taxon-specific features in shaping species-area relationships (SARs) remains controversial. Using 421 land-plant floras covering continents, continental islands and oceanic islands, we investigate whether variations in SAR parameters can be interpreted in terms of differences among lineages in speciation mode and dispersal capacities (TAXON), or of geological history and geographical isolation between continents and islands (GEO). Location: Global. Methods: Linear mixed-effects models describing variation in SARs, depending on the factors GEO and TAXON and controlling for differences between realms (REALM) and biomes (BIOME). Results: The best random-effect structure included both random slopes and random intercepts for GEO, TAXON, REALM and BIOME. This accounted for 77% of the total variation in species richness, substantially more than the 27% statistically explained by the model with fixed effects only (i.e. the simple SAR). The slopes of the SARs were higher for oceanic islands than for continental islands and continents, and higher in spermatophytes than in pteridophytes and bryophytes. The intercepts largely exhibited the reverse trend. TAXON was included in best-fit models restricted to oceanic and continental islands, but not continents. Analysing each plant lineage separately, the intercept of GEO was only included in the random structure of spermatophytes. Main conclusions: SAR parameters varied considerably depending on geological history and taxon-specific traits. Such differences in SARs among land plants challenge the neutral theory that the accumulation of species richness on islands is controlled exclusively by extrinsic factors. Taxon-specific differences in SARs were, however, confounded by interactions with geological history and geographical isolation. This highlights the importance of applying integrative frameworks that take both environmental context and taxonomic idiosyncrasies into account in SAR analyses. © 2014 John Wiley & Sons Ltd.

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Aim: We collate and analyse data for land snail diversity and endemism, as a means of testing the explanatory power of the general dynamic model of oceanic island biogeography (GDM): a theoretical model linking trends in species immigration, speciation and extinction to a generalized island ontogeny. Location: Eight oceanic archipelagos: Azores, Canaries, Hawaii, Galápagos, Madeira, Samoa, Society, Tristan da Cunha. Methods: Using data obtained from literature sources we examined the power of the GDM through its derivative ATT2 model (i.e. diversity metric = b1 + b2Area + b3Time + b4Time2), in comparison with all the possible simpler models, e.g. including only area or time. The diversity metrics considered were the number of (1) native species, (2) archipelagic endemic species, and (3) single-island endemic species. Models were evaluated using both log-transformed and untransformed diversity data by means of linear mixed effect models. For Hawaii and the Canaries, responses of different major taxonomic groups were also analysed separately. Results: The ATT2 model was always included within the group of best models and, in many cases, was the single-best model and was particularly successful in fitting the log-transformed diversity metrics. In four archipelagos, a hump-shaped relationship with time (island age) is apparent, while the other four archipelagos show a general increase of species richness with island age. In Hawaii and the Canaries outcomes vary between different taxonomic groups. Main conclusions: The GDM is an intentionally simplified representation of environmental and diversity dynamics on oceanic islands, which predicts a simple positive relationship between diversity and island area combined with a humped response to time. We find broad support for the applicability of this model, especially when a full range of island developmental stages is present. However, our results also show that the varied mechanisms of island origins and the differing responses of major taxa should be taken into consideration when interpreting diversity metrics in terms of the GDM. This heterogeneity is reflected in the fact that no single model outperforms all the other models for all datasets analysed. © 2012 Blackwell Publishing Ltd.

The general dynamic model of oceanic island biogeography describes the evolution of species diversity properties, including species richness (SR), through time. We investigate the hypothesis that SR in organisms with high dispersal capacities is better predicted by island area and elevation (as a surrogate of habitat diversity) than by time elapsed since island emergence and geographic isolation. Linear mixed effect models (LMMs) subjected to information theoretic model selection were employed to describe moss and liverwort SR patterns from 67 oceanic islands across 12 archipelagos. Random effects, which are used to modulate model parameters to take differences among archipelagos into account, included only a random intercept in the best-fit model for liverworts and in one of the two best-fit models for mosses. In this case, the other coefficients are constant across archipelagos, and we interpret the intercept as a measure of the intrinsic carrying capacity of islands within each archipelago, independently of their size, age, elevation and geographic isolation. The contribution of area and elevation to the models was substantially higher than that of time, with the least contribution made by measures of geographic isolation. This reinforces the idea that oceanic barriers are not a major impediment for migration in bryophytes and, together with the almost complete absence of in situ insular diversification, explains the comparatively limited importance of time in the models. We hence suggest that time per se has little independent role in explaining bryophyte SR and principally features as a variable accounting for the changing area and topographic complexity during the life-cycle of oceanic islands. Simple area models reflecting habitat availability and diversity might hence prevail over more complex temporal models reflecting in-situ speciation and dispersal (time, geographic connectivity) in explaining patterns of biodiversity for exceptionally mobile organisms. © 2013 The Authors.

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.

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Ecosystems face both local hazards, such as over-exploitation, and global hazards, such as climate change. Since the impact of local hazards attenuates with distance from humans, local extinction risk should decrease with remoteness, making faraway areas safe havens for biodiversity. However, isolation and reduced anthropogenic disturbance may increase ecological specialization in remote communities, and hence their vulnerability to secondary effects of diversity loss propagating through networks of interacting species. We show this to be true for reef fish communities across the globe. An increase in fish-coral dependency with the distance of coral reefs from human settlements, paired with the far-reaching impacts of global hazards, increases the risk of fish species loss, counteracting the benefits of remoteness. Hotspots of fish risk from fish-coral dependency are distinct from those caused by direct human impacts, increasing the number of risk hotspots by ~30% globally. These findings might apply to other ecosystems on Earth and depict a world where no place, no matter how remote, is safe for biodiversity, calling for a reconsideration of global conservation priorities., Coral reefs face both local and global stressors. Here, the authors show how a positive relationship between distance from human settlements and ecological specialisation makes remote coral reef fish communities more vulnerable to coral loss.

WOS:000669987000010; Reef fishes are a treasured part of marine biodiversity, and also provide needed protein for many millions of people. Although most reef fishes might survive projected increases in ocean temperatures, corals are less tolerant. A few fish species strictly depend on corals for food and shelter, suggesting that coral extinctions could lead to some secondary fish extinctions. However, secondary extinctions could extend far beyond those few coral-dependent species. Furthermore, it is yet unknown how such fish declines might vary around the world. Current coral mass mortalities led us to ask how fish communities would respond to coral loss within and across oceans. We mapped 6964 coral-reef-fish species and 119 coral genera, and then regressed reef-fish species richness against coral generic richness at the 1 degrees scale (after controlling for biogeographic factors that drive species diversification). Consistent with small-scale studies, statistical extrapolations suggested that local fish richness across the globe would be around half its current value in a hypothetical world without coral, leading to more areas with low or intermediate fish species richness and fewer fish diversity hotspots.

Over the recent decades, the preservation of coastal and estuarine waters has been recognised as a priority at national and international levels. At the European scale, the Water Framework Directive (WFD) was established with the aim to achieve a good ecological status of all significant water bodies by the year 2015. Among the descriptors used to define the ecological status of water bodies, taxonomic diversity (usually species richness) is a widespread metric employed across taxa and habitats. However, species richness is known to increase with area at a decelerating rate, producing the species–area relationship (SAR). Thus, removing the effect of area (even in case of low magnitude), is mandatory before comparing species richness between sites. Here we tested recently developed multi-model SARs as a standardisation tool for comparing benthic species richness (annelids, arthropods, molluscs and total species richness) in 18 Italian coastal lagoons with a surface area ranging from 0.19 to 552 km2, i.e. three orders of magnitude. However, the sampling effort was often incompletely described and certainly heterogeneous among the studies retrieved from the database. Therefore, we used the number of studies as a proxy for the sampling effort in each lagoon and estimated species richness from observed values using non-parametric occurrence-based estimators. We further corrected for bias that might be induced by sampling efforts being unrepresentative for the surface area of different lagoons. After applying these corrections, we estimated that c. 25–30% of species richness could be explained by surface area. We investigated the spatial congruence of species richness patterns across taxa and showed that molluscs could serve as a potential surrogate for total macro-invertebrate species richness. We further found that the intensity of conservation focus and the gradient of ecological status are decoupled in Italian coastal lagoons. More generally, our study pave the way for the use of flexible tools for the comparison of species richness across water bodies in the context of the WFD.