Niche modelling is typically used to assess the effects of anthropogenic land use and climate change on species distributions and to inform spatial conservation planning. These models focus on the suitability of local biotic and abiotic conditions for a species in environmental space (E-space). Although movements also affect species occurrence, efforts to formally integrate geographic space (G-space) into niche modelling have been hindered by the lack of comprehensive theoretical frameworks. 

We propose the 'functional habitat' framework to define areas that are simultaneously of high-quality in E-space and functionally connected to other suitable habitat in G-space. Originating in metapopulation ecology, approaches have been developed to assess the amount of suitable connected habitat, based on the proximity between pairs of locations. Using network theory, which operates in topological space (T-space, defined by a network), we extended these metapopulation approaches to integrate movement constraints in G-space with niche modelling in E-space. 

We demonstrate the functional habitat framework using empirical data (GPS-tracking and population monitoring) throughout the European wild mountain reindeer (Rangifer t. tarandus) distribution range. We show that functional habitat outperforms traditional suitability in explaining the species' distribution. This approach integrates effects from habitat loss and fragmentation for spatial conservation planning and avoids overemphasizing small, inaccessible areas with locally suitable habitat. The functional habitat framework formally integrates biotic, abiotic, and movement constraints in niche modeling using network theory, thus opening a wide range of applications in spatial conservation planning.

The data and analysis are supporting the paper: 'Habitat Functionality: integrating environmental and geographic space in niche modelling for conservation planning', see the description therein.

The repository contains Data and Scripts for I. the functional habitat computation and II. the comparison of functionality and suitability for reindeer distribution.

I. Functional habitat computation
1) RSF & SSF maps (data/rsf_sum.tif & data/ssf_sum.tif)
2) The study areas (data/reindeer_areas.shp)
3) Data preparation in R (data_prep.r)
4) Habitat functionality computation in Julia (functionality_computation.jl)

II. Functionality vs. suitability comparison
1) data frame with data (data/rastdf.rda)
2) model fitting script (model_comparison.r)