Image Credit: Chinmaysk, CC BY-SA 3.0, Image Cropped
Species data for understanding biodiversity dynamics: The what, where and when of species occurrence data collection (2021) Petersen et al., Ecological Solutions and Evidence, https://doi.org/10.1002/2688-8319.12048
With the rise of the internet, GPS’ and smartphones, the amount of openly available species occurrence data has reached previously unfathomable numbers. This increase is mostly due to the engagement of the citizen scientist – regular people getting out there in nature and taking part in data collection and research. From people taking photos of flowers in their backyard to organised salamander spotting safaris, citizen scientists have opened up data that previously would have cost massive amounts to produce.
The Global Biodiversity Information Facility (GBIF) is the largest hub of such data, collating data ranging from amateur observation to museum specimens to professional surveys. It is well-known, however, that this kind of openly available data comes with a myriad of caveats: some species groups are reported much more than others (I am looking at you, bird-watchers), and “roadside bias” (see Did You Know?) haunts the records. But how are the records distributed among different land-cover types on a country-scale, does it differ between groups of conservation concern, and does it depend on who the reporters are?
Species associations will change as the climate rises. So how can we attempt to predict these changes (Image Credit: Charles J Sharp, CC BY-SA 4.0, Image Cropped)
Using joint species distribution models for evaluating how species-to-species associations depend on the environmental context (2017) Tikhonov et al, Methods in Ecology and Evolution, DOI: https://doi.org/10.1111/2041-210X.12723
Statistical modelling is a crucial part of ecology. Being able to provide an (admittedly simplified) mathematical description of the relationship between species abundance, range or density and the surrounding environment is a huge help in taking proactive steps to manage an ecosystem, or predicting species numbers in other areas.
Historically models have used environmental variables to explain population or evolutionary developments in species. When modelling a single species, many ecologists have taken into account that the presence of other species (for example competitors or predators) may influence the presence of this single species. This has led to the rise of joint species distribution models (JSDMs), which take into account environmental variables, as well as the interactions between certain species. These models have become increasingly useful, and with environmental change now being the norm in many ecosystems, this week’s authors produced one such model that accounts for changes in species interactions in the face of changing environmental factors.