Integrating archaeology and ancient DNA analysis to address invasive species colonization in the Gulf of Alaska

The intentional and unintentional movement of plants and animals by humans has transformed ecosystems and landscapes globally. Assessing when and how a species was introduced are central to managing these transformed landscapes, particularly in island environments. In the Gulf of Alaska, there is considerable interest in the history of mammal introductions and rehabilitating Gulf of Alaska island environments by eradicating mammals classified as invasive species. The Arctic ground squirrel (Urocitellus parryii) is of concern because it affects vegetation and seabirds on Gulf of Alaska islands. This animal is assumed to have been introduced by historic settlers; however, ground squirrel remains in the prehistoric archaeological record of Chirikof Island, Alaska, challenge this timeline and suggest they colonized the islands long ago. We used 3 lines of evidence to address this problem: direct radiocarbon dating of archaeological squirrel remains; evidence of prehistoric human use of squirrels; and ancient DNA analysis of dated squirrel remains. Chirikof squirrels dated to at least 2000 years ago, and cut marks on squirrel bones suggested prehistoric use by people. Ancient squirrels also shared a mitochondrial haplotype with modern Chirikof squirrels. These results suggest that squirrels have been on Chirikof longer than previously assumed and that the current population of squirrels is closely related to the ancient population. Thus, it appears ground squirrels are not a recent, human‐mediated introduction and may have colonized the island via a natural dispersal event or an ancient human translocation.     

Protected‐Area Boundaries as Filters of Plant Invasions

Abstract: Human land uses surrounding protected areas provide propagules for colonization of these areas by non‐native species, and corridors between protected‐area networks and drainage systems of rivers provide pathways for long‐distance dispersal of non‐native species. Nevertheless, the influence of protected‐area boundaries on colonization of protected areas by invasive non‐native species is unknown. We drew on a spatially explicit data set of more than 27,000 non‐native plant presence records for South Africa's Kruger National Park to examine the role of boundaries in preventing colonization of protected areas by non‐native species. The number of records of non‐native invasive plants declined rapidly beyond 1500 m inside the park; thus, we believe that the park boundary limited the spread of non‐native plants. The number of non‐native invasive plants inside the park was a function of the amount of water runoff, density of major roads, and the presence of natural vegetation outside the park. Of the types of human‐induced disturbance, only the density of major roads outside the protected area significantly increased the number of non‐native plant records. Our findings suggest that the probability of incursion of invasive plants into protected areas can be quantified reliably.     

Effectiveness of protected areas for vertebrates based on taxonomic and phylogenetic diversity

Establishing protected areas is the primary goal and tool for preventing irreversible biodiversity loss. However, the effectiveness of protected areas that target specific species has been questioned for some time because targeting key species for conservation may impair the integral regional pool of species diversity and phylogenetic and functional diversity are seldom considered. We assessed the efficacy of protected areas in China for the conservation of phylogenetic diversity based on the ranges and phylogenies of 2279 terrestrial vertebrates. Phylogenetic and taxonomic diversity were strongly and positively correlated, and only 12.1–43.8% of priority conservation areas are currently protected. However, the patterns and coverage of phylogenetic diversity were affected when weighted by species richness. These results indicated that in China, protected areas targeting high species richness protected phylogenetic diversity well overall but failed to do so in some regions with more unique or threatened communities (e.g., coastal areas of eastern China, where severely threatened avian communities were less protected). Our results suggest that the current distribution of protected areas could be improved, although most protected areas protect both taxonomic and phylogenetic diversity.     

Effects of uncertainty and variability on population declines and IUCN Red List classifications

The International Union for Conservation of Nature (IUCN) Red List Categories and Criteria is a quantitative framework for classifying species according to extinction risk. Population models may be used to estimate extinction risk or population declines. Uncertainty and variability arise in threat classifications through measurement and process error in empirical data and uncertainty in the models used to estimate extinction risk and population declines. Furthermore, species traits are known to affect extinction risk. We investigated the effects of measurement and process error, model type, population growth rate, and age at first reproduction on the reliability of risk classifications based on projected population declines on IUCN Red List classifications. We used an age‐structured population model to simulate true population trajectories with different growth rates, reproductive ages and levels of variation, and subjected them to measurement error. We evaluated the ability of scalar and matrix models parameterized with these simulated time series to accurately capture the IUCN Red List classification generated with true population declines. Under all levels of measurement error tested and low process error, classifications were reasonably accurate; scalar and matrix models yielded roughly the same rate of misclassifications, but the distribution of errors differed; matrix models led to greater overestimation of extinction risk than underestimations; process error tended to contribute to misclassifications to a greater extent than measurement error; and more misclassifications occurred for fast, rather than slow, life histories. These results indicate that classifications of highly threatened taxa (i.e., taxa with low growth rates) under criterion A are more likely to be reliable than for less threatened taxa when assessed with population models. Greater scrutiny needs to be placed on data used to parameterize population models for species with high growth rates, particularly when available evidence indicates a potential transition to higher risk categories.