The PREDICTS project—Projecting Responses of Ecological Diversity In Changing Terrestrial Systems (www.predicts.org.uk)—has collated from published studies a large, reasonably representative database of comparable samples of biodiversity from multiple sites that differ in the nature or intensity of human impacts relating to land use. We have used this evidence base to develop global and regional statistical models of how local biodiversity responds to these measures. We describe and make freely available this 2016 release of the database, containing more than 3.2 million records sampled at over 26,000 locations and representing over 47,000 species. We outline how the database can help in answering a range of questions in ecology and conservation biology. To our knowledge, this is the largest and most geographically and taxonomically representative database of spatial comparisons of biodiversity that has been collated to date; it will be useful to researchers and international efforts wishing to model and understand the global status of biodiversity.

The objective of this study is to investigate whether presence/absence models can be used as surrogates of arthropod abundance, and eventually under which circumstances such surrogacy is guaranteed. Presence/absence data for 48 arthropod species from Terceira Island were modelled using artificial neural networks. Probabilities of occurrence were correlated with abundance data from a standardized arthropod survey programme. Although a tendency was found for vagile species to show relationships, only nine species showed significant positive correlations between probability of presence and abundance. Five of these were exotic spider species with high abundances and wide distributions in several human-modified habitats. The patchy distribution of pristine habitats, the capacity to reach them and the probable low dependence on limiting resources, other than food, enhance the relationship. A lack of significant correlations for the majority of the species may be due to historical events, inappropriate scale, demographic controls of density, or the incapacity of presence/absence models to account for environmental suitability. The difficulty to identify a priori the species for which the relationship will hold advises against the use of species distribution models as surrogates of arthropod abundance.

The storminess of the Azores region was investigated using newspaper records from AD 1836 onwards. The information obtained was rank-ordered for intensity and the time series of storm frequency analysed for interannual- to century-scale variability. The documentary data set was validated by comparison with objective cyclones intensity for the period AD 1958—2000. Results indicate that four periods of contrasting storm frequency are present (AD 1836—1870, 1870—1920, 1920—1940 and 1940—1998). The average storm lasts for 2.3 days and the average secular storm frequency is 3.1 storms/yr. Low intensity events occur four times every five years whereas an extreme storm occurs on average once every seven years. The documentary index of storminess is highly variable at different timescales, which is consistent with other studies of storminess in the North Atlantic. Nevertheless, an objective comparison between late nineteenth- and late twentieth-century storm frequency does not reveal a significant difference. Between AD 1865 and the late twentieth century the winter NAO and storminess indices show a statistically significant anti-correlation pattern at the monthly and seasonal scales. In the late nineteenth century and between AD 1950 and 1970 the NAO index was low and the storminess index high, whilst the opposite occurred from the early twentieth century until the middle 1950s; since AD 1970 both indexes reveal positive trends and are predominantly positive. The NAO mode of circulation is partially responsible for the storminess spatial pattern and temporal distribution over the Azores region since AD 1865 and for about a century, however this relation appears to have weakened since the 1960s.

Biodiversity accumulates hierarchically by means of ecological and evolutionary processes and feedbacks. Reconciling the relative importance of these processes is hindered by current theory, which tends to focus on a single spatial, temporal or taxonomic scale. We introduce a mechanistic model of community assembly, rooted in classic island biogeography theory, which makes temporally explicit joint predictions across three biodiversity data axes: i) species richness and abundances; ii) population genetic diversities; and iii) trait variation in a phylogenetic context. We demonstrate that each data axis captures information at different timescales, and that integrating these axes enables discriminating among previously unidentifiable community assembly models. We combine our massive eco-evolutionary synthesis simulations (MESS) with supervised machine learning to fit the parameters of the model to real data and infer processes underlying how biodiversity accumulates, using communities of tropical trees, arthropods, and gastropods as case studies that span a range of spatial scales.