A return-on-investment approach for prioritization of rigorous taxonomic research needed to inform responses to the biodiversity crisis.

Global biodiversity loss is a profound consequence of human activity. Disturbingly, biodiversity loss is greater than realized because of the unknown number of undocumented species. Conservation fundamentally relies on taxonomic recognition of species, but only a fraction of biodiversity is described. Here, we provide a new quantitative approach for prioritizing rigorous taxonomic research for conservation. We implement this approach in a highly diverse vertebrate group-Australian lizards and snakes. Of 870 species assessed, we identified 282 (32.4%) with taxonomic uncertainty, of which 17.6% likely comprise undescribed species of conservation concern. We identify 24 species in need of immediate taxonomic attention to facilitate conservation. Using a broadly applicable return-on-investment framework, we demonstrate the importance of prioritizing the fundamental work of identifying species before they are lost.

Dealing with uncertainty in landscape genetic resistance models: a case of three co-occurring marsupials.

Landscape genetics lacks explicit methods for dealing with the uncertainty in landscape resistance estimation, which is particularly problematic when sample sizes of individuals are small. Unless uncertainty can be quantified, valuable but small data sets may be rendered unusable for conservation purposes. We offer a method to quantify uncertainty in landscape resistance estimates using multimodel inference as an improvement over single model-based inference. We illustrate the approach empirically using co-occurring, woodland-preferring Australian marsupials within a common study area: two arboreal gliders (Petaurus breviceps, and Petaurus norfolcensis) and one ground-dwelling antechinus (Antechinus flavipes). First, we use maximum-likelihood and a bootstrap procedure to identify the best-supported isolation-by-resistance model out of 56 models defined by linear and non-linear resistance functions. We then quantify uncertainty in resistance estimates by examining parameter selection probabilities from the bootstrapped data. The selection probabilities provide estimates of uncertainty in the parameters that drive the relationships between landscape features and resistance. We then validate our method for quantifying uncertainty using simulated genetic and landscape data showing that for most parameter combinations it provides sensible estimates of uncertainty. We conclude that small data sets can be informative in landscape genetic analyses provided uncertainty can be explicitly quantified. Being explicit about uncertainty in landscape genetic models will make results more interpretable and useful for conservation decision-making, where dealing with uncertainty is critical.

Using multilevel models to identify drivers of landscape-genetic structure among management areas.

Landscape genetics offers a powerful approach to understanding species' dispersal patterns. However, a central obstacle is to account for ecological processes operating at multiple spatial scales, while keeping research outcomes applicable to conservation management. We address this challenge by applying a novel multilevel regression approach to model landscape drivers of genetic structure at both the resolution of individuals and at a spatial resolution relevant to management (i.e. local government management areas: LGAs) for the koala (Phascolartos cinereus) in Australia. Our approach allows for the simultaneous incorporation of drivers of landscape-genetic relationships operating at multiple spatial resolutions. Using microsatellite data for 1106 koalas, we show that, at the individual resolution, foliage projective cover (FPC) facilitates high gene flow (i.e. low resistance) until it falls below approximately 30%. Out of six additional land-cover variables, only highways and freeways further explained genetic distance after accounting for the effect of FPC. At the LGA resolution, there was significant variation in isolation-by-resistance (IBR) relationships in terms of their slopes and intercepts. This was predominantly explained by the average resistance distance among LGAs, with a weaker effect of historical forest cover. Rates of recent landscape change did not further explain variation in IBR relationships among LGAs. By using a novel multilevel model, we disentangle the effect of landscape resistance on gene flow at the fine resolution (i.e. among individuals) from effects occurring at coarser resolutions (i.e. among LGAs). This has important implications for our ability to identify appropriate scale-dependent management actions.