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Ongoing work shows that species richness patterns on volcanic oceanic islands are shaped by surface area changes driven by longer time scale (>1 ka) geological processes and natural sea level fluctuations. A key question is: what are the rates and magnitudes of the forces driving spatial changes on volcanic oceanic islands which in turn affect evolutionary and biogeographic processes? We quantified the rates of surface-area changes of a whole island resulting from both volcanogenic flows and sea level change over the last glacial-interglacial (GI) cycle (120 ka) for the volcanically active island of Terceira, (Azores, Macaronesia, Portugal). Volcanogenic activity led to incidental but long-lasting surface area expansions by the formation of a new volcanic cone and lava-deltas, whereas sea level changes led to both contractions and expansions of area. The total surface area of Terceira decreased by as much as 24% per time step due to changing sea levels and increased by 37% per time step due to volcanism per time step of 10 ka. However, while sea levels nearly continuously changed the total surface area, volcanic activity only impacted total surface area during two time steps over the past 120 ka. The surface area of the coastal and lowland region (here defined as area

Invasive alien species are among the most important, least controlled, and least reversible of human impacts on the world's ecosystems, with negative consequences affecting biodiversity and socioeconomic systems. Species distribution models have become a fundamental tool in assessing the potential spread of invasive species in face of their native counterparts. In this study we compared two different modeling techniques: (i) fixed effects models accounting for the effect of ecogeographical variables (EGVs); and (ii) mixed effects models including also a Gaussian random field (GRF) to model spatial correlation (Matérn covariance function). To estimate the potential distribution of Pittosporum undulatum and Morella faya (respectively, invasive and native trees), we used geo-referenced data of their distribution in Pico and São Miguel islands (Azores) and topographic, climatic and land use EGVs. Fixed effects models run with maximum likelihood or the INLA (Integrated Nested Laplace Approximation) approach provided very similar results, even when reducing the size of the presences data set. The addition of the GRF increased model adjustment (lower Deviance Information Criterion), particularly for the less abundant tree, M. faya. However, the random field parameters were clearly affected by sample size and species distribution pattern. A high degree of spatial autocorrelation was found and should be taken into account when modeling species distribution. [ABSTRACT FROM AUTHOR]