Species ranges are broadly determined by climate, primary productivity, phenology, and habitat structure; all of which vary across space and time. Ashley utilized a suite of novel remotely sensed environmental variables that she anticipated may contribute additional power beyond that of traditionally used variables, to improve accuracy of maps of forest bird species distributions in Argentina. She hoped to illustrate their usefulness for biodiversity mapping and conservation.
Halting biodiversity declines and promoting sustainable ecosystem usage are major conservation goals. To do so, it is necessary to understand the environmental correlates of biodiversity patterns.
Environmental variables used in biodiversity modelling come from a variety of sources and have varying levels of power to explain distributions of different species. Many environmental variables that have been used regularly for many years have shortcomings- they may not cover large areas, may not capture suitable habitat, or may not be able to capture changes in environmental conditions over time or space. Increasingly, novel remotely-sensed environmental data are being developed for modelling biodiversity patterns. Novel remotely sensed products may complement or even offer better results than environmental variables that have been used for many years.
Olah set out to identify sets of complementary variables, from among a set of standard variables and newly created variables, that can improve species distribution modelling. Olah used a combination of land cover, elevation, precipitation, and temperature variables, that are commonly used in species distribution modelling, and a set of novel-remotely sensed products to model distributions of forest affiliated bird species in Argentina.
The set of novel environmental variables were created by SILVIS lab postdoctoral researcher Eduarda Silveira. These products measure spatial and interannual variation in the phenology of land surface temperature and forest vegetation greenness. Olah predicted that areas with more spatial variability in phenology and thermal conditions are more likely to host more species because there are a variety of resources and thermal conditions in close proximity, allowing many species to coexist in a small area. These areas may also buffer against high year-to-year variation in conditions because organisms are more likely to have access to refugia or resources that could allow them to persist. Temporal variation in forest greenness or temperature describes how consistent conditions are between years. High variability means that phenological events are not occurring at a predictable time, while low variability means that events are occurring predictably each year.
In another new product developed by Silveira, ground forest inventory data was combined with radar-based remotely-sensed data, resulting in modelled forest structure wall-to-wall across Argentina. Silveira also developed maps of forest phenoclusters and phenocluster diversity. Phenoclusters classify different forest types in Argentina, based on vegetation phenology, land surface temperature, and precipitation. Olah thinks phenoclusters are a more ecologically relevant way to characterize habitat important to bird species than typical land cover maps. Phenoclusters capture functional rather than only compositional or structural characteristics. By comparing how well these novel remotely-sensed products and traditionally used variables perform in species distribution modelling Olah assessed their usefulness for biodiversity mapping.
Olah developed species distribution models for 152 forest bird species. She found that among three sets of models she constructed, those containing novel, traditional, or a mixed set of variables, performance was similar. However, models constructed from the mixed set of variables performed slightly better than models containing only one or the other set of data. The variables that were included in the greatest number of individual species’ distribution models included precipitation seasonality, precipitation of the driest quarter, as well as spatial heterogeneity in winter land surface temperature, which is a novel variable. Her results highlight how variables derived from different sources can offer complementary information for biodiversity modelling. Her models contribute to forest harvest planning in Argentina.
Story by Olah, Ashley