DOI: 10.5061/dryad.bk3j9kdhk
title: Quantitative support for the benefits of proactive management for wildlife disease control

description: 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.
description: <strong>USGS Disclaimer</strong>
 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.
 <strong>Data Description</strong>
 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.
description: # 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: 1.
 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. 2. Question Text - The question language
 for the specific parameter and action of interest. 3. lo - the lowest
 reasonable estimate provided by the expert 4. hi - the highest reasonable
 estimate provided by the expert 5. best - the most reasonable (i.e central
 estimate) provided by the expert 6. CI - the confidence level of the
 expert that the true value falls within their lowest and highest estimates
 7. mean - roughly estimate mean from google sheet entry form 8. sd -
 roughly estimate the standard deviation from google sheet entry form 9.
 expert - expert number ID for each expert with a group 10. Group - expert
 group ID number 11. Qnum - question number id for linking question to
 parameter names in code. 12. 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](https://code.usgs.gov/cooperativeresearchunits/proactive-management)