Geology topics

Amelie Y. Davis

Publications and source records attributed to Amelie Y. Davis.

3 recordsLinked to original sources

USGS2022

Maximizing species distribution model performance when using historical occurrences and variables of varying persistency

Occurrence data used to build species distribution models often include historical records from locations in which the species no longer exists. When these records are paired with contemporary environmental values that no longer represent the conditions the species experienced, the model creates false associations that hurt predictive performance. The extent of mismatching increases with the number of historical occurrences and with inclusion of environmental variables that are prone to change over time. Indeed, the mismatch between occurrence data and contemporaneous environmental variables is a common dilemma when modeling rare or cryptic species, especially those of conservation concern that were once more abundant. Herein, we assess (1) the impact of historical occurrences on model performance across three sets of environmental variables of increasing persistency and (2) the performance of models built using selected-historical occurrences from locations that showed evidence of limited environmental change over time. Concepts are tested on federally listed flatwoods salamanders, reflecting real-world conservation management efforts. We predicted that, compared to other occurrence sets, (1) historical occurrences would perform best with environmental variables that were more persistent, (2) recent occurrences would perform best when the environmental variables were more impersistent, and that (3) our selected-historical occurrences would perform best with a combination of persistent and impersistent variables. Our results showed the expected inversion of model performance of recent and historical occurrences across environmental variables of increasing persistency when evaluated by correct predictions. However, the inversion was not seen in area under the curve performance, in which historical occurrences outperformed recent occurrence models across all variable sets. Selected-historical occurrences did not notably improve performance over all-historical occurrences in any metric or variable set. To maximize utility and performance, modelers could acknowledge potential trade-offs from inclusion of historical occurrences and consider number and age of recent and historical occurrences available, the persistency of environmental variables considered, and how their conservation goals are reflected in model design and evaluation, particularly with respect to sensitivity versus specificity. Our study lends support for inclusion of historical occurrences, with the potential exception of mostly impersistent variables when sensitivity is the highest priority.

Alabama, Florida, Georgia, South Carolina↗

USGS2021

The contribution of land cover change to the decline of honey yields in the Northern Great Plains

Decreased availability of forage, as well as increased pesticide exposure, are important factors in the decline of honey bee health. Here, we isolate land cover transitions and their effect on honey production at 160 commercial apiaries in the Northern Great Plains. We found that land cover changes from 2008 to 2012 caused an annual decline in honey yields of 0.9% in the study area. Transitions from grassland to soybean (but not corn) were particularly detrimental to honey yields, potentially due to bee contact with pesticides within and around agricultural fields. When our results are applied to known apiary locations across all of North Dakota (U.S.A.), we estimate a 2.5% (1.6 million USD) decline in 2012 honey yields due to land cover changes occurring between 2008 and 2012. Even when controlling for changes in land cover, we found that on average colonies in the study area experienced a 14% annual decline in honey yields. We discuss possible explanations for these non-land-cover-related honey yield declines, including changing economic conditions (e.g. pollination services), changes in land management (e.g. pesticides), and increases in pests or diseases.

South Dakota↗

USGS2016

A generalizable energetics-based model of avian migration to facilitate continental-scale waterbird conservation

Conserving migratory birds is made especially difficult because of movement among spatially disparate locations across the annual cycle. In light of challenges presented by the scale and ecology of migratory birds, successful conservation requires integrating objectives, management, and monitoring across scales, from local management units to ecoregional and flyway administrative boundaries. We present an integrated approach using a spatially explicit energetic-based mechanistic bird migration model useful to conservation decision-making across disparate scales and locations. This model moves a mallard-like bird (Anas platyrhynchos), through spring and fall migration as a function of caloric gains and losses across a continental scale energy landscape. We predicted with this model that fall migration, where birds moved from breeding to wintering habitat, took a mean of 27.5 days of flight with a mean seasonal survivorship of 90.5% (95% CI = 89.2%, 91.9%) whereas spring migration took a mean of 23.5 days of flight with mean seasonal survivorship of 93.6% (95% CI = 92.5%, 94.7%). Sensitivity analyses suggested that survival during migration was sensitive to flight speed, flight cost, the amount of energy the animal could carry and the spatial pattern of energy availability, but generally insensitive to total energy availability per se. Nevertheless, continental patterns in the bird-use days occurred principally in relation to wetland cover and agricultural habitat in the fall. Bird-use days were highest in both spring and fall in the Mississippi Alluvial Valley and along the coast and near-shore environments of South Carolina. Spatial sensitivity analyses suggested that locations nearer to migratory endpoints were less important to survivorship; for instance, removing energy from a 1,036 km2 stopover site at a time from the Atlantic Flyway suggested coastal areas between New Jersey and North Carolina, including Chesapeake Bay and the North Carolina piedmont, are essential locations for efficient migration and increasing survivorship during spring migration but not locations in Ontario and Massachusetts. This sort of spatially explicit information may allow decision-makers to prioritize their conservation actions toward locations most influential to migratory success. Thus, this mechanistic model of avian migration provides a decision-analytic medium integrating the potential consequences of local actions to flyway-scale phenomena.

Ecological Applications↗