Non‐invasive monitoring of multiple wildlife health factors by fecal microbiome analysis

Abstract

Fecal microbial biomarkers represent a less invasive alternative for acquiring information on wildlife populations than many traditional sampling methodologies. Our goal was to evaluate linkages between fecal microbiome communities in Rocky Mountain elk (Cervus canadensis) and four host factors including sex, age, population, and physical condition (body-fat). We paired a feature-selection algorithm with an LDAclassifier trained on elk differential bacterial abundance (16S-rRNA amplicon survey) to predict host health factors from 104 elk microbiomes across four elk populations.We validated the accuracy of the various classifier predictions with leave-one-out cross-validation using known measurements. We demonstrate that the elk fecal microbiome can predict the four host factors tested. Our results show that elk microbiomes respond to both the strong extrinsic factor of biogeography and simultaneously occurring, but more subtle, intrinsic forces of individual body-fat, sex, and age-class.

Gastrointestinal parasites in wild rodents in Chiloé Island-Chile

“…To establish an approximation for body condition, the Scaled Mass Index (SMI) was calculated (Peig & Green, 2009), in which the individual measurements of body weight (Mi) and body length (Li) were inserted into the formula [SMI = Mi×(L0/Li ) bSMA ]; L0 was the arithmetic mean of body length for each rodent species (i.e., A. olivacea = 81.4; A. manni = 91.9; G. valdivianus = 90.5; I. tarsalis = 81.4; L. micropus = 112; and O. longicaudatus = 82.1), and bSMA was the slope estimate of a standardized major axis (SMA) regression of the mass-length relationship (i.e., bSMA = β OLS/r = 0.48/0.60 = 0.75). Subsequently, the SMI of individuals was categorized into three groups: Low (<40), Medium (40-60), and High (>60) (thereafter called SMI categories), as carried out elsewhere (Pannoni et al, 2022;Valenzuela et al, 2022).…”

Best practice for wildlife gut microbiome research: A comprehensive review of methodology for 16S rRNA gene investigations

“…As noted above, it is increasingly appreciated that the nature of microbiome data is compositional (Gloor et al, 2017;Silverman et al, 2017;Weiss et al, 2017) with most studies comparing the relative abundances of taxa (Silverman et al, 2017). Traditional statistical methods assume that the nature of sequencing data is ecological (Gloor et al, 2017), with reads/sample being comparable to biological sampling effort (Weiss et al, 2017;Pannoni et al, 2022). Within one sequencing run, the library size total number of reads per sample can vary by orders of magnitude (McMurdie and Holmes, 2014;Weiss et al, 2017) and often contain many zeros (Weiss et al, 2017).…”

“…As such, numerous methods to normalize microbiome data have been developed to reduce statistical artifacts produced during analysis and address the compositional nature of the data. Some normalization techniques are mentioned below, but this is not discussed extensively in this review, as this area of microbiome research is constantly evolving and currently there is no consensus as to the best method for library normalization (Pannoni et al, 2022).…”

Noninvasive sampling for comparisons of wildlife microbiomes may be more reliable than sampling trapped animals

“…While we detected non-negligible differences in the microbial composition of feces collected using the two sampling methods, we also found that the total number of represented taxa for each method were both high and similar, suggesting that if all samples are collected in the same noninvasive manner, comparative analyses between populations or across time are likely valid. Of note, our noninvasive method is truly noninvasive – handing of animals is not required for any parts of the method unlike unlike (Knutie & Gotanda, 2018) and (Pannoni et al, 2022).…”