Using citizen science data for predicting the timing of ecological phenomena across regions.

The scarcity of long-term observational data has limited the use of statistical or machine-learning techniques for predicting intraannual ecological variation. However, time-stamped citizen-science observation records, supported by media data such as photographs, are increasingly available. In the present article, we present a novel framework based on the concept of relative phenological niche, using machine-learning algorithms to model observation records as a temporal sample of environmental conditions in which the represented ecological phenomenon occurs. Our approach accurately predicts the temporal dynamics of ecological events across large geographical scales and is robust to temporal bias in recording effort. These results highlight the vast potential of citizen-science observation data to predict ecological phenomena across space, including in near real time. The framework is also easily applicable for ecologists and practitioners already using machine-learning and statistics-based predictive approaches.

Drivers of Insect Community Change along the Margins of Mountain Streams in Serra da Estrela Natural Park (Portugal).

Mountain ecosystems are important biodiversity hotspots and valuable natural laboratories to study community assembly processes. Here, we analyze the diversity patterns of butterflies and odonates in a mountainous area of high conservation value-Serra da Estrela Natural Park (Portugal)-and we assess the drivers of community change for each of the two insect groups. The butterflies and odonates were sampled along 150 m transects near the margins of three mountain streams, at three elevation levels (500, 1000, and 1500 m). We found no significant differences in odonate species richness between elevations, but marginal differences (p = 0.058) were found for butterflies due to the lower number of species at high altitudes. Both insect groups showed significant differences in beta diversity (ßtotal) between elevations, with species richness differences being the most important component for odonates (ßrich = 55.2%), while species replacement drove the changes between butterfly assemblages (ßrepl = 60.3%). Climatic factors, particularly those depicting harsher conditions of temperature and precipitation, were the best predictors of total beta diversity (ßtotal) and its components (ßrich, ßrepl) for the two study groups. The study of insect biodiversity patterns in mountain ecosystems and of the role played by different predictors contribute to further our understanding on the community assembly processes and may help to better predict environmental change impacts on mountain biodiversity.

Local environmental variables are key drivers of ant taxonomic and functional beta-diversity in a Mediterranean dryland.

The decomposition of beta-diversity (ß-diversity) into its replacement (ßrepl) and richness (ßrich) components in combination with a taxonomic and functional approach, may help to identify processes driving community composition along environmental gradients. We aimed to understand which abiotic and spatial variables influence ant ß-diversity and identify which processes may drive ant ß-diversity patterns in Mediterranean drylands by measuring the percentage of variation in ant taxonomic and functional ß-diversity explained by local environmental, regional climatic and spatial variables. We found that taxonomic and functional replacement (ßrepl) primarily drove patterns in overall ß-diversity (ßtot). Variation partitioning analysis showed that respectively 16.8%, 12.9% and 21.6% of taxonomic ßtot, ßrepl and ßrich variation were mainly explained by local environmental variables. Local environmental variables were also the main determinants of functional ß-diversity, explaining 20.4%, 17.9% and 23.2% of ßtot, ßrepl and ßrich variation, respectively. Findings suggest that niche-based processes drive changes in ant ß-diversity, as local environmental variables may act as environmental filters on species and trait composition. While we found that local environmental variables were important predictors of ant ß-diversity, further analysis should address the contribution of other mechanisms, e.g. competitive exclusion and resource partitioning, on ant ß-diversity.

Spatial distribution of citizen science casuistic observations for different taxonomic groups.

Opportunistic citizen science databases are becoming an important way of gathering information on species distributions. These data are temporally and spatially dispersed and could have limitations regarding biases in the distribution of the observations in space and/or time. In this work, we test the influence of landscape variables in the distribution of citizen science observations for eight taxonomic groups. We use data collected through a Portuguese citizen science database (biodiversity4all.org). We use a zero-inflated negative binomial regression to model the distribution of observations as a function of a set of variables representing the landscape features plausibly influencing the spatial distribution of the records. Results suggest that the density of paths is the most important variable, having a statistically significant positive relationship with number of observations for seven of the eight taxa considered. Wetland coverage was also identified as having a significant, positive relationship, for birds, amphibians and reptiles, and mammals. Our results highlight that the distribution of species observations, in citizen science projects, is spatially biased. Higher frequency of observations is driven largely by accessibility and by the presence of water bodies. We conclude that efforts are required to increase the spatial evenness of sampling effort from volunteers.