Biology subjects

Kramer, A. M.

Publications and source records attributed to Kramer, A. M..

4 recordsLinked to original sources

bioRxiv2021

Economic costs of biological invasions in the United States

The United States has thousands of invasive species, representing a sizable, but unknown burden to the national economy. Given the potential economic repercussions of invasive species, quantifying these costs is of paramount importance both for national economies and invasion management. Here, we used a novel global database of invasion costs (InvaCost) to quantify the overall costs of invasive species in the United States across spatiotemporal, taxonomic, and socioeconomic scales. From 1960 to 2020, reported invasion costs totaled $4.52 trillion (USD 2017). Considering only observed, highly reliable costs, this total cost reached $1.22 trillion with an average annual cost of $19.94 billion/year. These costs increased from $2.00 billion annually between 1960-1969 to $21.08 billion annually between 2010-2020. Most costs (73%) were related to resource damages and losses ($896.22 billion), as opposed to management expenditures ($46.54 billion). Moreover, the majority of costs were reported from invaders from terrestrial habitats ($643.51 billion, 53%) and agriculture was the most impacted sector ($509.55 billion). From a taxonomic perspective, mammals ($234.71 billion) and insects ($126.42 billion) were the taxonomic groups responsible for the greatest costs. Considering the apparent rising costs of invasions, coupled with increasing numbers of invasive species and the current lack of cost information for most known invaders, our findings provide critical information for policymakers and managers.

ecology↗

bioRxiv2021

Spatial spread of white-nose syndrome in North America, 2006-2018

White-nose syndrome has caused massive mortality in multiple bat species and spread across much of North America, making it one of the most destructive wildlife diseases on record. This has also resulted in it being one of the most well-documented wildlife disease outbreaks, making it possible to look for changes in the pattern of spatial spread over time. We fit a series of spatial interaction models to the United States county-level observations of the pathogenic fungus, Pseudogymnoascus destructans, that causes white-nose syndrome. Models included the distance between caves, cave abundance, measures of winter length and winter onset, and species richness of all bats and hibernating bats only. We found that the best supported models included all of these factors, but that the particular structure and most informative covariates changed over the course of the outbreak, with winter length displacing winter onset as the most informative measure of winter conditions, and evidence for the effects total species richness and hibernation varying from year to year. We also found that weather had detectable effects on spread. While the effect sizes for cave abundance and species richness were relatively stable over the length of the outbreak, distance became less important as time went on. These findings indicate that although models produced early in the outbreak captured important and consistent aspects of the spatial spread of white-nose syndrome, there were also changes over time in the factors associated with spread, suggesting that forecasts may be improved by iterative model refinement.

ecology↗

bioRxiv2020

Analysing global economic costs of invasive alien species with the invacost R package

O_LIThe reported costs of invasive alien species from the global database InvaCost are heterogenous and cover different spatio-temporal scales. A standard procedure for aggregating invasive species cost estimates is necessary to ensure the repeatability and comparativeness of studies. C_LIO_LIWe introduce here the invacost R package, an open-source software designed to query and analyse the InvaCost database. We illustrate this package and its framework with cost data associated with invasive alien invertebrates. C_LIO_LIFirst, the invacost package provides updates of this dynamic database directly in the analytical environment R. Second, it helps understand the heteregoneous nature of monetary cost data for invasive species, processes to harmonize the data, and the inherent biases associated with such data. Third, it readily provides complementary methods to investigate the costs of invasive species at different scales, all the while accounting for econometric statistical issues. C_LIO_LIThis tool will be useful for scientists working on invasive alien species, by (i) facilitating access to and use of this multi-disciplinary data resource and (ii) providing a standard procedure which will facilitate reproducibility and comparability among studies, one of the major critics of this topic until now. It should facilitate further interdisciplinary works including economists and invasion ecology researchers. C_LI

ecology↗

bioRxiv2020

Deep learning for time series classification in ecology

Temporal data is ubiquitous in ecology and ecologists often face the challenge of accurately differentiating these data into predefined classes, such as biological entities or ecological states. The usual approach consists of transforming the time series into user-defined features and then using these features as predictors in conventional statistical or machine learning models. Here we suggest the use of deep learning models as an alternative to this approach. Recent deep learning techniques can perform the classification directly from the time series, eliminating subjective and resource-consuming data transformation steps, and potentially improving classification results. We describe some of the deep learning architectures relevant for time series classification and show how these architectures and their hyper-parameters can be tested and used for the classification problems at hand. We illustrate the approach using three case studies from distinct ecological subdisciplines: i) insect species identification from wingbeat spectrograms; ii) species distribution modelling from climate time series and iii) the classification of phenological phases from continuous meteorological data. The deep learning approach delivered ecologically sensible and accurate classifications demonstrating its potential for wide applicability across subfields of ecology.

ecology↗