SPEEDMIND

Motivation

The SPEEDMIND project addresses some of the most important challenges in biodiversity monitoring at large scale. These include the preferential (or opportunistic) sampling aspect of presence-only data in the absence of full surveys (inventories), and the fact that species distribution maps (SDMs) are often constructed for one species at a time (no joint modeling of multiple species). The preferential biased sampling challenge arises from the plant species sightings provided by InfoFlora, the national data and information center of the Swiss flora, and the way it relies on citizen science/crowdsourcing for plant sightings (Figure 1).

Proposed Approach / Solution

Prior to the development of new models for plant SDMs, we have integrated large amounts of heterogeneous data sources (environmental data streams, maps, trait data, phylogenies, species occurrence data) in a standardized warehouse, representing an important contribution as it brings in one place disparate ecological, spatial and thematic information. In particular, SPEEDMIND developed new types of predictors sets at a very high spatial resolution (93 m), with improved precision, and which enable a better description of the species ecological niche. First, we generated highly computational demanding maps of climate (temperature and precipitation) by downscaling CHELSA climate layers (Karger et al., 2017) from 1 km to 93 m spatial resolution in Switzerland. Second, by combining a massive amount of plant data occurrences with expert-based ecological indicators of the plant ecology, we used random forests to generate eight ecologically meaningful predictors of plants (e.g., soil acidity, soil moisture, etc.). The resulting predictors outperformed traditional predictors used in ecology and increased our ability to predict the distribution of plant species in Switzerland (Figure 2).

While modelling rare plant species is a main challenge using traditional SDMs (because of low data availability), we aim at achieving this by jointly modelling rare species with the more widespread ones and by integrating information on species ecological and morphological similarities. More precisely, we are using two separate approaches to build joint species distribution models. The first is a hierarchical Poisson factorization approach, a form of recommender system where the most likely location-species pairs are identified and distinct latent weights represent preferences of locations and prevalence of species. The second approach is a particular spatial point process, a log-Gaussian Cox process, where environmental information is introduced as smooth non-linear effects. This point process is further enhanced by including predicted intensity fields from other species, which achieves a joint modeling.

Impact

The development of SDMs jointly for 3500+ plant species over Switzerland improves the monitoring of potentially invasive plant species, helps the study of rare species and their habitat, and can play a direct role in revising biodiversity management at the national scale, with possible implications for land use.