Peer review of: Hunting strategies to increase detection of chronic wasting disease in cervids (2020)

Reviewers' Comments: 
 
Reviewer #1: 
Remarks to the Author: 
Mysterud et al. tackle the issue of how to manage and reduce the risk of a potential disease outbreak 
in a population or species that is of conservation concern (e.g., threatened). They develop and test a 
proactive hunting regime to: 1) survey for early detection of a disease and identify freedom from 
infection; while also 2) avoiding unwanted population decline. The authors use a combination of strong 
empirical data on population and disease parameters in concert with simulation to examine the effect 
of different hypothetical (and actual) management options on the probability that a population is free 
from infection. They develop and test their regime using the Chronic Wasting Disease outbreak in 
reindeer in Norway. There are many interesting results in this paper, but of most importance is that 
with proactive hunting measures (e.g., harvesting sex and age classes with the highest pattern of 
infection [or relative risk]), the authors demonstrate that a 99% probability of freedom from infection 
can occur in 1/3 of the time relative to ordinary harvest surveillance. 
 
I find this article to be a fascinating example where ecological knowledge, theory, and modeling can 
be utilized to proactively manage and conserve a population or species. The pitch of the paper, 
empirical data, and modeling and simulation techniques are all strong. I only have a couple of 
comments: 
 
1) The authors do not include how the environment influences the population. I am curious why not, 
and am curious if there is any reason that the environment could change significantly the predicted 
results and interpretation of the conceptualized proactive harvest regimes? Note that I do see that the 
authors include stochasticity in some of the demographic parameters, which does, at some level, take 
into account variation due to environment. 
 
2) I found it pretty hard to read the results without a detailed read of Table 1 and/or reading the 
methods. For example, some of the terms are jargon and aren’t defined until the methods (or in table 
1). Design prevalence is one example. Including a bit more information/details when transitioning 
from the introduction to the results would be warranted (especially for the terms). Another option is to 
potentially provide more details in the figure legends, which would help them stand alone better and 
provide some more detail without having to skip ahead to the methods. 
 
3) Some of the parameters are developed using partial expert opinion (e.g., probability of 
introduction). While I am confident the authors know their system well and discussed these 
parameters at length in developing the models, I wonder why simple sensitivity analyses were not 
done? Such an analysis can help ‘verify’ that results are not sensitive to certain choices of values. 
 
 
 
Reviewer #2: 
Remarks to the Author: 
Review of Mysterud et al. “Hunting wildlife for disease detection” 
 
The authors develop a surveillance framework for CWD using hunter harvest data. Hunter harvest is a 
common method of conducting disease surveillance but the authors suggest that recommendations for 
hunter harvest policies could be changed to optimize both population management and disease 
surveillance. Usually, hunting policies are only optimized for population management and disease 
surveillance is an opportunistic add-on. This aim is novel, interesting, and very worthwhile to pursue. 
 

My main concern is that the framework doesn’t consider disease dynamics. It relies on pre-existing 
knowledge of relative risk in different age classes. However, the interaction of demographic dynamics 
and R0/Re/FOI will drive age-based infection risk (to change under different combinations of these 
rates) so I don’t see how a constant assumption about age-based risk is valid. 
 
I also found it difficult to understand all the components of the framework because much of it was 
developed as part of previous work, and they weren’t combined in the Supp info with data input/model 
structure details so the reader has to go back to individual papers (although I did appreciate Fig. 2 as 
a broad overview) to understand how separate models were integrated. Relatedly, in its current form, 
I didn’t feel the description of the framework is directly transferable to other systems, both in terms of 
minimally sufficient data to use the method and in terms of recommendations. I think the paper could 
benefit greatly from some kind of ‘recommendations synthesis’ maybe in the form of a decision tree 
that would help guide when different strategies would be used and which data would be required to 
use them. 
 
L185 – provides 
 
Table 1 – why is ‘Principle and Basis’ cell for ‘Harvest rate and composition’ blank? 
 
Fig. 2 caption – what do the different colored text mean? 
 
Fig. 3 caption – I’m not sure what the ‘tested’ trajectory is representing; what is the skew? 
 
Fig. 4 – I don’t find this figure very informative as it’s completely predictable from the harvest 
strategies. It would be better to predict population abundance or growth rates as a metric of impact of 
the different strategies 
 
SUPP info – L67 – it would be useful to provide some description of the type of model from Nilsen and 
Strand so one doesn’t have to go looking, i.e., Bayesian hierarchical is super vague so the reader gets 
no insight as to which data are needed and how they are used. 
 
SUPP info – L80 – same as above. There is not enough info here to understand what kind of model 
this is and which data are needed to use it.