Complexity versus certainty in understanding species’ declines

Aim
Our understanding of and ability to predict species declines is limited, despite decades of study. We sought to expand our understanding of species declines within a regional landscape by testing models using both traditional hypotheses and those derived from a complex adaptive systems approach.

Location
Our study area was the dry mixed grassland of south-eastern Alberta, Canada, one of the largest remnants of native grassland in North America, and the adjacent grassland in Saskatchewan.

Methods
We used the breeding birds of the grassland to test the relationship between species declines and a suite of traits associated with decline (such as size, specialization and rarity, as well as distance to edge of a discontinuity, and edge of geographic range) in a stepwise regression with AICc values and bootstrapping via model averaging, followed by a refit procedure to obtain model-averaged parameter estimates. We used both provincial government and Breeding Bird Survey (BBS) classifications of decline. We also modelled degree of decline in the Alberta and Saskatchewan grasslands, which differ in amount of habitat remaining, to test whether severity of decline was explained by the same traits as species decline/not- decline.

Results
We found that the model for government-defined decline fulfilled government expectations that species' extinction risk is a function of being large, specialized, rare and carnivorous, whereas the model for BBS-defined decline suggested that the biological reality of decline is more complex, requiring the need to explicitly model scale-specific patterns. Furthermore, species decline/not- decline was explained by different traits than those that fit degree of decline, though complex systems- derived traits featured in both sets of models.

Main conclusions
Traditional approaches to predict species declines (e.g. government processes or IUCN Red Lists), may be too simplistic and may therefore misguide management and conservation. Using complex systems approaches that account for scale-specific patterns and processes have the potential to overcome these limitations.