Quantitative Developments

1. Surveillance programs are essential for detecting emerging pathogens and often rely on molecular methods to make inference about the presence of a target disease agent. However, molecular methods rarely detect target DNA perfectly. For example, molecular pathogen detection methods can result in misclassification (i.e., false positives and false negatives) or partial detection errors (i.e., detections with ‘ambiguous’, ‘uncertain’, or ‘equivocal’ results). Then, when data are to be analyzed, these?partial observations?are?either?discarded?or censored;?this, however, disregards information that could be used to make inference about the true state of the system. There is a critical need for more direction and guidance related to how many samples is enough to declare a unit of interest ‘pathogen-free’.
2. Here, we develop a Bayesian hierarchal framework that accommodates false negative, false positive, and uncertain detections to improve inference related to the occupancy of a pathogen. We apply our modeling framework to a case study of the fungal pathogen Pseudogymnoascus destructans (Pd) identified in Texas bats at the invasion front of white-nose syndrome. To improve future surveillance programs, we provide guidance on sample sizes required to be 95% certain a target organism is absent from a site.
3. We found that the presence of uncertain detections increased the variability of resulting posterior probability distributions of pathogen occurrence, and that our estimates of required sample size were very sensitive to prior information about pathogen occupancy, pathogen prevalence, and diagnostic test specificity. In the Pd case study, we found that the posterior probability of occupancy was very low in 2018, but occupancy probability approached 1 in 2020, reflecting increasing prior probabilities of occupancy and prevalence elicited from the site manager.
4. Our modeling framework provides the user a posterior probability distribution of pathogen occurrence, which allows for subjective interpretation by the decision-maker. To help readers apply and use the methods we developed, we provide an interactive?RShiny?app?that generates target species?occupancy estimation and sample size estimates to make these methods more accessible?to the scientific community (https://rmummah.shinyapps.io/ambigDetect_sampleSize).?This modeling framework and sample size guide may be useful for improving inferences from molecular surveillance data about emerging pathogens, non-native invasive species, and endangered species where misclassifications and ambiguous detections occur.

Climate data allow people to examine species distributions and possible distributions. This script takes ERA5-Land climate estimates (https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5) for precipitation and lake temperature and processes them to create summary climate layers for use with biological organisms in lakes. This code could be modified to use a different subset of years.

These BioLake raster data provide global estimates (~10.0 x 12.4 km resolution) of twelve bioclimatic variables based on estimated lake temperature. Eleven of these twelve variables (BioLake01 - BioLake11) are estimated for each of three lake strata: lake mix (surface) layer, lake bottom, and total lake water column. These eleven variables correspond to CHELSA (Climatologies at high resolution for the earth's land surface areas) bioclimatic variables BIO1 - BIO11, except that these BioLake variables are based on lake water temperature and CHELSA BIO1 - BIO11 variables are based on air temperature. CHELSA BIO is also calculated a finer spatial resolution (~1 x 1 km). The twelfth variable (BioLake20; months with non-zero ice cover) does not correspond to any CHELSA bioclimatic variable. The data are supplied as a multi-layer raster (.grd) file in the World Mollweide projection, accompanied by a header file (.gri) with layer names.

For BioLake layer download, see https://doi.org/10.5066/P96QLN5Y

These BioLake raster data provide global estimates (~10.0 x 12.4 km resolution) of twelve bioclimatic variables based on estimated lake temperature. Eleven of these twelve variables (BioLake01 - BioLake11) are estimated for each of three lake strata: lake mix (surface) layer, lake bottom, and total lake water column. These eleven variables correspond to CHELSA (Climatologies at high resolution for the earth's land surface areas) bioclimatic variables BIO1 - BIO11, except that these BioLake variables are based on lake water temperature and CHELSA BIO1 - BIO11 variables are based on air temperature. CHELSA BIO is also calculated a finer spatial resolution (~1 x 1 km). The twelfth variable (BioLake20; months with non-zero ice cover) does not correspond to any CHELSA bioclimatic variable. The data are supplied as a multi-layer raster (.grd) file in the World Mollweide projection, accompanied by a header file (.gri) with layer names.