a b s t r a c tMost habitats in the Azores have undergone substantial land-use changes and anthropogenic disturbance during the last six centuries. In this study we assessed how the richness, abundance and composition of arthropod communities change with: (1) habitat type and (2) the surrounding land-use at different spatial scales. The research was conducted in Terceira Island, Azores. In eighty-one sites of four different habitat types (natural and exotic forests, semi-natural and intensively managed pastures), epigaeic arthropods were captured with pitfall traps and classified as endemic, native or introduced. The land-use surrounding each site was characterized within a radius ranging from 100 to 5000 m. Nonparametric tests were used to identify differences in species richness, abundance and composition between habitat types at different spatial scales. Endemic and native species were more abundant in natural forests, while introduced species were more abundant in intensively managed pastures. Natural forests and intensively managed pastures influenced arthropod species richness and composition at all spatial scales. Exotic forests and semi-natural pastures, however, influenced the composition of arthropod communities at larger scales, promoting the connectivity of endemic and native species populations. Local species richness, abundance and composition of arthropod communities are mostly determined by the presence of nearby natural forests and/or intensively managed pastures. However, semi-natural pastures and exotic forests seem to play an important role as corridors between natural forests for both endemic and native species. Furthermore, exotic forests may serve as a refuge for some native species.

Islands harbour evolutionary and ecologically unique biota, which are currently disproportionately threatened by a multitude of anthropogenic factors, including habitat loss, 2568 Biodivers Conserv (2018) 27:2567-2586 1 3 invasive species and climate change. Native forests on oceanic islands are important refugia for endemic species, many of which are rare and highly threatened. Long-term monitoring schemes for those biota and ecosystems are urgently needed: (i) to provide quantitative baselines for detecting changes within island ecosystems, (ii) to evaluate the effectiveness of conservation and management actions, and (iii) to identify general ecological patterns and processes using multiple island systems as repeated 'natural experiments'. In this contribution, we call for a Global Island Monitoring Scheme (GIMS) for monitoring the remaining native island forests, using bryophytes, vascular plants, selected groups of arthropods and vertebrates as model taxa. As a basis for the GIMS, we also present new, optimized monitoring protocols for bryophytes and arthropods that were developed based on former standardized inventory protocols. Effective inventorying and monitoring of native island forests will require: (i) permanent plots covering diverse ecological gradients (e.g. elevation, age of terrain, anthropogenic disturbance); (ii) a multiple-taxa approach that is based on standardized and replicable protocols; (iii) a common set of indicator taxa and community properties that are indicative of native island forests' welfare, building on, and harmonized with existing sampling and monitoring efforts; (iv) capacity building and training of local researchers, collaboration and continuous dialogue with local stakeholders; and (v) long-term commitment by funding agencies to maintain a global network of native island forest monitoring plots.

Prediction error is considered an important problem in species distribution models. To address this issue, we here examined the accuracy of overlays of presence‐only‐based models for many individual species in representing patterns of assemblage diversity. For this purpose, we used a database of 977 160 records of seed plant occurrences on an intensively surveyed, species‐rich island (Tenerife, Canary Islands) for modelling the distribution of all its 841 native plant species individually. The modelling was done using Maxent, one of the best‐performing presence‐only modelling techniques, using various thresholds to convert the estimated suitability values into predicted presence or absence. Distribution models for each individual species were overlaid to predict species richness and composition, which were then compared to the observed values for well‐surveyed grid cells. We found high levels of compositional error, when the best performing suitability threshold for predicting species richness was applied. Our best prediction had a mean species richness error of 24% and a mean compositional error of 60% relative to the observed values for the well‐surveyed cells; >50% of all species were included erroneously in >25% of the well‐surveyed cells. Hence, large quantities of data are not necessarily enough to obtain reliable predictions of assemblage diversity, limiting the usefulness of this methodology in conservation planning.

Aim  Because intertidal organisms often live close to their physiological tolerance limits, they are potentially sensitive indicators of climate‐driven changes in the environment. The goals of this study were to assess the effect of climatic and non‐climatic factors on the geographical distribution of intertidal macroalgae, and to predict future distributions under different climate‐warming scenarios. Location  North‐western Iberian Peninsula, southern Europe. Methods  We developed distribution models for six ecologically important intertidal seaweed species. Occurrence and microhabitat data were sampled at 1‐km2 resolution and analysed with climate variables measured at larger spatial scales. We used generalized linear models and applied the deviance and Bayesian information criterion to model the relationship between environmental variables and the distribution of each target species. We also used hierarchical partitioning (HP) to identify predictor variables with higher independent explanatory power. Results  The distributions of Himanthalia elongata and Bifurcaria bifurcata were correlated with measures of terrestrial and marine climate, although in opposite directions. Model projections under two warming scenarios indicated the extinction of the former at a faster rate in the Cantabrian Sea (northern Spain) than in the Atlantic (west). In contrast, these models predicted an increase in the occurrence of B. bifurcata in both areas. The occurrences of Ascophyllum nodosum and Pelvetia canaliculata, species showing rather static historical distributions, were related to specific non‐climatic environmental conditions and locations, such as the location of sheltered sites. At the southernmost distributional limit, these habitats may present favourable microclimatic conditions or provide refuges from competitors or natural enemies. Model performances for Fucus vesiculosus and F. serratus were similar and poor, but several climatic variables influenced the occurrence of the latter in the HP analyses. Main conclusions  The correlation between species distributions and climate was evident for two species, whereas the distributions of the others were associated with non‐climatic predictors. We hypothesize that the distribution of F. serratus responds to diverse combinations of factors in different sections of the north‐west Iberian Peninsula. Our study shows how the response of species distributions to climatic and non‐climatic variables may be complex and vary geographically. Our analyses also highlight the difficulty of making predictions based solely on variation in climatic factors measured at coarse spatial scales.