Quantitative support for the benefits of proactive management for wildlife disease control
Data files
Jun 05, 2024 version files 764.53 KB
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BsalEE_All_Responses_Final.txt
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README.md
Abstract
Finding effective pathogen mitigation strategies is one of the biggest challenges humans face today. In the context of wildlife, emerging infectious diseases have repeatedly caused widespread host morbidity and population declines of numerous taxa. In areas yet unaffected by a pathogen, a proactive management approach has the potential to minimize or prevent host mortality. However, we typically lack critical information on the disease dynamics in a novel host system, have limited empirical evidence on efficacy of management interventions, and lack validated predictive models. As such, quantitative support for identifying effective management interventions is largely absent, and the opportunity for proactive management is often missed. Here, we consider the potential invasion of the chytrid fungus, Batrachochytrium salamandrivorans, whose expected emergence in North America poses a severe threat to hundreds of salamander species in this global salamander biodiversity hotspot. We developed and parameterized a dynamic multi-state occupancy model to forecast host and pathogen occurrence, following expected emergence of the pathogen, and evaluated the response of salamander populations to different management scenarios. Our model forecasts that taking no action is expected to be catastrophic to salamander populations. We also show that proactive action is expected to maximize host occupancy outcomes compared to ‘wait and see’ reactive management, thus providing quantitative support for proactive management opportunities. Additionally, we found that Bsal eradication is unlikely under any evaluated management options. Contrary to our expectations, even early pathogen detection had little effect on Bsal or host occupancy outcomes. Our analysis provides quantitative support that proactive management is the optimal strategy for promoting persistence of disease-threatened salamander populations. Our approach fills a critical gap by defining a framework for evaluating management options prior to pathogen invasion and can thus serve as a template for addressing novel disease threats that jeopardize wildlife and human health.
README: Start early and stay the course: Proactive management outperforms reactive actions for wildlife disease control :
To understand the impact of different management scenarios on host and pathogen persistence, we used a multistate dynamic occupancy model. We incorporated management actions (or lack of) via the estimated effects on parameters in the transition matrix. We considered four scenarios for the timing of management interventions on host and pathogen persistence: (i) no management scenario, (ii) proactive management scenario, (iii) reactive management scenario, and (iv) proactive + reactive management scenario. Expert elicitation was used to obtain estimates for the majority of the parameters given the limited empirical data available. The raw estimates and confidence level from the 4-point elicitation method are provided in the two data files provided here. Methodologies are further explained in the main text of the paper.
Description of the Data and file structure
The data file included here contains all estimates of the "best, lowest reasonable, "highest reasonable, and confidence level that true value is within the provided range"(i.e. values from the 4-point method) from the elicitation process. The columns are defined below:
- Action - This defines the management action to which the question applies. The timing (Proactive vs reactive) is defined in parentheses. Base Rate equates to no management action.
- Question Text - The question language for the specific parameter and action of interest.
- lo - the lowest reasonable estimate provided by the expert
- hi - the highest reasonable estimate provided by the expert
- best - the most reasonable (i.e central estimate) provided by the expert
- CI - the confidence level of the expert that the true value falls within their lowest and highest estimates
- mean - roughly estimate mean from google sheet entry form
- sd - roughly estimate the standard deviation from google sheet entry form
- expert - expert number ID for each expert with a group
- Group - expert group ID number
- Qnum - question number id for linking question to parameter names in code.
- Metric - defines the units for the lo, hi and best estimates provided by the experts
This data file is the initial input for the first of the code files provided in the code release folder. There is a Code Workflow image file to aid in using this code within the code archive.
Sharing/Access Information
Links to other publicly accessible locations of the data: https://code.usgs.gov/cooperativeresearchunits/proactive-management
Methods
USGS Disclaimer
Unless otherwise stated, all data, metadata, and related materials are considered to satisfy the quality standards relative to the purpose for which the data were collected. Although these data and associated metadata have been reviewed for accuracy and completeness and approved for release by the U.S. Geological Survey (USGS), no warranty expressed or implied is made regarding the display or utility of the data for other purposes, nor on all computer systems, nor shall the act of distribution constitute any such warranty.
Data Description
These data consist of the raw estimates and confidence values that were collected by expert elicitation from 35 experts to provide estimates for 158 model parameters of our develop multi-state occupancy model considering no management, 10 proactive actions and 10 reactive actions.