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.
Highlights
Habitat association analysis is used in determining the likely habitat requirements of individual species (Cole et al 2010)
Information on habitat associations is generally derived from expert knowledge (Lonsdorf et al 2009) or analysis over a small geographic area (Ball et al 2013, De Lima et al 2016, Ferrão et al 2018) and is often limited to associations with a single habitat or a few broad habitats (Webb et al 2017)
We focus in this paper on the Phi coefficient of correlation, (“correlation index”) which like IndVal is simpler than ordination
Summary
Habitat association analysis is used in determining the likely habitat requirements of individual species (Cole et al 2010). Their method worked well, albeit with large variation in the associations within individual species, but needed approximately 5000 records to ensure accuracy They used this approach, as other methods required more precise locations information than the 1 km they used. Unlike IndVal, the correlation index gives a negative association value when a species appears to avoid a habitat, and uses species’ absences to provide extra information (De Cáceres and Legendre 2009). The binary nature of the correlation index requires either removal of mixed or uncertain habitat data or a judgement as to which habitat to assign While this might be considered as an error in the record, movement of individuals from preferred into adjacent less-preferred habitats is common (Ries et al 2004), and so the precise location in which a mobile individual is found may not be in a preferred habitat. Ascertain how many records are required to give a valid estimate of habitat association
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