Mapping evolutionary process: a multi-taxa approach to conservation prioritization

Abstract

Human-induced land use changes are causing extensive habitat fragmentation. As a result, many species are not able to shift their ranges in response to climate change and will likely need to adapt in situ to changing climate conditions. Consequently, a prudent strategy to maintain the ability of populations to adapt is to focus conservation efforts on areas where levels of intraspecific variation are high. By doing so, the potential for an evolutionary response to environmental change is maximized. Here, we use modeling approaches in conjunction with environmental variables to model species distributions and patterns of genetic and morphological variation in seven Ecuadorian amphibian, bird, and mammal species. We then used reserve selection software to prioritize areas for conservation based on intraspecific variation or species-level diversity. Reserves selected using species richness and complementarity showed little overlap with those based on genetic and morphological variation. Priority areas for intraspecific variation were mainly located along the slopes of the Andes and were largely concordant among species, but were not well represented in existing reserves. Our results imply that in order to maximize representation of intraspecific variation in reserves, genetic and morphological variation should be included in conservation prioritization.

Keywords: Andes; Ecuador; conservation prioritization; ecological modeling; evolutionary process; generalized dissimilarity modeling; landscape genetics; species distribution.

Figure 1

Schematic of our workflow for this paper. (i) Species distribution models for the seven target taxa were created using Maxent (Phillips et al. 2006), species-presence point localities, and a set of environmental variables. The modeled species distributions served to delimit our study area for modeling intraspecific variation. (ii) We predicted the patterns of genetic and morphological diversity using in situ data and the environmental variables in a generalized dissimilarity modeling framework (Ferrier et al. 2007). For each genetic or morphological trait, these analyses resulted in a GIS layer with values representing 50 similarity classes. These classes were separated as individual layers in ArcGIS (version 9.3) and used in (iii) subsequent ResNet reserve selection software (Sarkar et al. 2009) to prioritize areas for conservation based on genetic and morphological variation. (iv) We also used species distributions of birds, mammals, and amphibians, available from public databases, to prioritize areas for conservation based on species richness and complementarity. Finally, we combined the results from steps (iii) and (iv) and compared all results with each other and with currently protected areas.

Figure 2

Maps indicating the highest 10% of variation per unit area (3 × 3 km) (alpha-diversity) in genetic and morphological variation in the seven target species. (A) wedge-billed woodcreeper Glyphorynchus spirurus; (B) masked flowerpiercer Diglossa cyanea; (C) streak-necked flycatcher Mionectes striaticollis; (D) the frog species Pristimantis w-nigrum; (E) silky short-tailed bat Carollia brevicauda; (F) chestnut short-tailed bat Carollia castanea; and (G) Seba's short-tailed bat Carollia perspicillata. Colors indicate the types of variation examined: red: genetic data; blue: morphological data (different shades of blue indicate different morphological traits); yellow: overlapping regions of high levels of alpha-diversity in both genetic and morphological data. Grey scale indicates elevation, with low elevations in black and high elevations in white.

Figure 3

Composite map of selected reserves at a 10% representation target using species-level data (green) and genetic and morphological data (blue) in ResNet and areas harboring high levels of intraspecific alpha-diversity (orange and red). Areas delimited with solid black lines indicate existing reserves. Areas indicated with dashed lines are those where one or more of the generalized dissimilarity models show high uncertainty, because the associated environmental conditions are outside the range of those encountered at sampled locations.