A weighting method to improve habitat association analysis: tested on British carabids

Abstract

Analysis of species’ habitat associations is important for biodiversity conservation and spatial ecology. The original phi coefficient of association is a simple method that gives both positive and negative associations of individual species with habitats. The method originates in assessing the association of plant species with habitats, sampled by quadrats. Using this method for mobile animals creates problems as records often have imprecise locations, and would require either using only records related to a single habitat or arbitrarily choosing a single habitat to assign. We propose and test a new weighted version of the index that retains more records, which improves association estimates and allows assessment of more species. It weights habitats that lie within the area covered by the species record with their certainty level, in our case study, the proportion of the grid cell covered by that habitat. We used carabid beetle data from the National Biodiversity Network atlas and CEH Land Cover Map 2015 across Great Britain to compare the original method with the weighted version. We used presence‐only data, assigning species absences using a threshold based on the number of other species found at a location, and conducted a sensitivity analysis of this threshold. Qualitative descriptions of habitat associations were used as independent validation data. The weighted index allowed the analysis of 52 additional species (19% more) and gave results with as few as 50 records. For the species we could analyse using both indices, the weighted index explained 70% of the qualitative validation data compared to 68% for the original, indicating no accuracy loss. The weighted phi coefficient of association provides an improved method for habitat analysis giving information on preferred and avoided habitats for mobile species that have limited records, and can be used in modelling and analysis that directs conservation policy and practice.

Habitat Fragmentation Increases Overall Richness, but Not of Habitat-Dependent Species

“…We also randomly generated the habitat matrix between the patches of the focal-habitat, always having ten other habitats. Given the importance of the intervening habitat matrix in determining what species are in the landscape and how species move between patches (Brodie and Newmark, 2019;Chetcuti et al, 2019), future studies might look at the matrix specifically, non-randomly generating habitat matrices and including different mixes of anthropogenic and semi-natural habitats (Fletcher et al, 2018a). Our baseline model represented movement as a random walk, and we introduced bias based on habitat suitability in the more complex models.…”

“…Our research opens new avenues for research into how species demography and movement in relation to the focal-habitat affect biodiversity responses to FPS. Species' specializations, habitat preferences and demography in different habitats (Chetcuti et al, 2019) should be taken into consideration when planning conservation as well as considering that under some circumstances FPS may lead to the conservation objectives of increased beta-diversity.…”

“…We generated eleven habitat types: the focalhabitat, and ten others comprising the matrix. In keeping with the known complexity of species habitat associations, we allowed species to have a diversity of associations with and use of habitats within the landscape (Betts et al, 2014;Chetcuti et al, 2019). We generated a new landscape for every model run.…”

“…Being a generalist does not mean the species has no habitat preferences (Townsend et al, 2008;Da Silveira et al, 2016). Chetcuti et al (2019) analyzed the habitat associations of hundreds of beetle species, showing most species had a positive association with several habitats, and only a few species showed a strong restriction to only a narrow range of habitats. It is also often assumed that specialists are more competitive when in a preferred or more suitable habitat compared to generalists, but the generalists are more competitive on average across multiple habitats (Marvier et al, 2004).…”

“…In this study, we model FPS in terms of a single focal-habitat type, as is standard, but represent the matrix as a mix of different habitat types with randomized fragmentation and area (Bender and Fahrig, 2005). The multiple matrix habitats allow landscape-level effects to arise from species differences in their habitat preferences, thus better reflecting species' differences in nature (Bollmann et al, 2005;Betts et al, 2014;Brodie and Newmark, 2019;Chetcuti et al, 2019).…”