Multidisciplinary population monitoring when demographic data are sparse: a case study of remote trout populations

Abstract The potential of genetic, genomic, and phenotypic metrics for monitoring population trends may be especially high in isolated regions, where traditional demographic monitoring is logistically difficult and only sporadic sampling is possible. This potential, however, is relatively underexplored empirically. Over eleven years, we assessed several such metrics along with traditional ecological knowledge and catch data in a socioeconomically important trout species occupying a large, remote lake. The data revealed largely stable characteristics in two populations over 2-3 generations, but possible contemporary changes in a third population. These potential shifts were suggested by reduced catch rates, reduced body size, and changes in selection implied at one gene-associated single nucleotide polymorphism. A demographic decline in this population, however, was ambiguously supported, based on the apparent lack of temporal change in effective population size, and corresponding traditional knowledge suggesting little change in catch. We illustrate how the pluralistic approach employed has practicality for setting future monitoring efforts of these populations, by guiding monitoring priorities according to the relative merits of different metrics and availability of resources. Our study also considers some advantages and disadvantages to adopting a pluralistic approach to population monitoring where demographic data are not easily obtained

Misrelating values and empirical matters in conservation: A problem and solutions

We uncover a largely unnoticed and unaddressed problem in conservation research: arguments built within studies are sometimes defective in more fundamental and specific ways than appreciated, because they misrelate values and empirical matters. We call this the unraveled rope problem because just as strands of rope must be properly and intricately wound with each other so the rope supports its load, empirical aspects and value aspects of an argument must be related intricately and properly if the argument is to objectively support its conclusion. By characterizing this problem with precision, our study differs from but complements existing studies of value issues in conservation science, in two ways. First, it focuses on key relationships between empirical issues and value issues, relations that have sometimes been obscured by focusing on these issues more independently of each other. Second, it focuses on these relationships within arguments and deploys a method of argument analysis and evaluation honed in other fields but under-utilized in conservation science. By combining our study's novel features, we detail six families of argument defect that exemplify the unraveled rope problem within existing literature. These defects sometimes manifest within basic research stages, rather than just in more applied downstream stages where roles for values are already obvious to many. As scientific reasoning and communication become increasingly important for addressing society's greatest environmental challenges, overcoming the unraveled rope problem will be essential to the success and integrity of conservation research. Therefore, we also outline potential solutions, preventative measures, and useful further work for conservation researchers

Does source population size affect performance in new environments?

Small populations are predicted to perform poorly relative to large populations when experiencing environmental change. To explore this prediction in nature, data from reciprocal transplant, common garden, and translocation studies were compared meta-analytically. We contrasted changes in performance resulting from transplantation to new environments among individuals originating from different sized source populations from plants and salmonids. We then evaluated the effect of source population size on performance in natural common garden environments and the relationship between population size and habitat quality. In ‘home-away’ contrasts, large populations exhibited reduced performance in new environments. In common gardens, the effect of source population size on performance was inconsistent across life-history stages (LHS) and environments. When transplanted to the same set of new environments, small populations either performed equally well or better than large populations, depending on life stage. Conversely, large populations outperformed small populations within native environments, but only at later life stages. Population size was not associated with habitat quality. Several factors might explain the negative association between source population size and performance in new environments: (i) stronger local adaptation in large populations and antagonistic pleiotropy, (ii) the maintenance of genetic variation in small populations, and (iii) potential environmental differences between large and small populations

Effective/census population size ratio estimation: a compendium and appraisal

With an ecological-evolutionary perspective increasingly applied toward the conservation and management of endangered or exploited species, the genetic estimation of effective population size (N e) has proliferated. Based on a comprehensive analysis of empirical literature from the past two decades, we asked: (i) how often do studies link N e to the adult census population size (N)? (ii) To what extent is N e correctly linked to N? (iii) How readily is uncertainty accounted for in both N e and N when quantifying N e/N ratios? and (iv) how frequently and to what degree might errors in the estimation of N e or N affect inferences of N e/N ratios? We found that only 20% of available N e estimates (508 of 2617; 233 studies) explicitly attempted to link N e and N; of these, only 31% (160 of 508) correctly linked N e and N. Moreover, only 7% (41 of 508) of N e/N ratios (correctly linked or not) reported confidence intervals for both N e and N; for those cases where confidence intervals were reported for N e only, 31% of N e/N ratios overlapped with 1, of which more than half also reached below N e/N = 0.01. Uncertainty in N e /N ratios thus sometimes spanned at least two orders of magnitude. We conclude that the estimation of N e/N ratios in natural populations could be significantly improved, discuss several options for doing so, and briefly outline some future research directions

Multidisciplinary population monitoring when demographic data are sparse: a case study of remote trout populations

The potential of genetic, genomic, and phenotypic metrics for monitoring population trends may be especially high in isolated regions, where traditional demographic monitoring is logistically difficult and only sporadic sampling is possible. This potential, however, is relatively underexplored empirically. Over eleven years, we assessed several such metrics along with traditional ecological knowledge and catch data in a socioeconomically important trout species occupying a large, remote lake. The data revealed largely stable characteristics in two populations over 2–3 generations, but possible contemporary changes in a third population. These potential shifts were suggested by reduced catch rates, reduced body size, and changes in selection implied at one gene-associated single nucleotide polymorphism. A demographic decline in this population, however, was ambiguously supported, based on the apparent lack of temporal change in effective population size, and corresponding traditional knowledge suggesting little change in catch. We illustrate how the pluralistic approach employed has practicality for setting future monitoring efforts of these populations, by guiding monitoring priorities according to the relative merits of different metrics and availability of resources. Our study also considers some advantages and disadvantages to adopting a pluralistic approach to population monitoring where demographic data are not easily obtained

Striking Phenotypic Variation yet Low Genetic Differentiation in Sympatric Lake Trout (Salvelinus namaycush)

The study of population differentiation in the context of ecological speciation is commonly assessed using populations with obvious discreteness. Fewer studies have examined diversifying populations with occasional adaptive variation and minor reproductive isolation, so factors impeding or facilitating the progress of early stage differentiation are less understood. We detected non-random genetic structuring in lake trout (Salvelinus namaycush) inhabiting a large, pristine, postglacial lake (Mistassini Lake, Canada), with up to five discernible genetic clusters having distinctions in body shape, size, colouration and head shape. However, genetic differentiation was low (FST = 0.017) and genetic clustering was largely incongruent between several population- and individual-based clustering approaches. Genotype- and phenotype-environment associations with spatial habitat, depth and fish community structure (competitors and prey) were either inconsistent or weak. Striking morphological variation was often more continuous within than among defined genetic clusters. Low genetic differentiation was a consequence of relatively high contemporary gene flow despite large effective population sizes, not migration-drift disequilibrium. Our results suggest a highly plastic propensity for occupying multiple habitat niches in lake trout and a low cost of morphological plasticity, which may constrain the speed and extent of adaptive divergence. We discuss how factors relating to niche conservatism in this species may also influence how plasticity affects adaptive divergence, even where ample ecological opportunity apparently exists

Striking Phenotypic Variation yet Low Genetic Differentiation in Sympatric Lake Trout (Salvelinus namaycush)

The study of population differentiation in the context of ecological speciation is commonly assessed using populations with obvious discreteness. Fewer studies have examined diversifying populations with occasional adaptive variation and minor reproductive isolation, so factors impeding or facilitating the progress of early stage differentiation are less understood. We detected non-random genetic structuring in lake trout (Salvelinus namaycush) inhabiting a large, pristine, postglacial lake (Mistassini Lake, Canada), with up to five discernible genetic clusters having distinctions in body shape, size, colouration and head shape. However, genetic differentiation was low (FST = 0.017) and genetic clustering was largely incongruent between several population- and individual-based clustering approaches. Genotype- and phenotype-environment associations with spatial habitat, depth and fish community structure (competitors and prey) were either inconsistent or weak. Striking morphological variation was often more continuous within than among defined genetic clusters. Low genetic differentiation was a consequence of relatively high contemporary gene flow despite large effective population sizes, not migration-drift disequilibrium. Our results suggest a highly plastic propensity for occupying multiple habitat niches in lake trout and a low cost of morphological plasticity, which may constrain the speed and extent of adaptive divergence. We discuss how factors relating to niche conservatism in this species may also influence how plasticity affects adaptive divergence, even where ample ecological opportunity apparently exists

A deep learning-based approach to diagnose mild traumatic brain injury using audio classification

Mild traumatic brain injury (mTBI or concussion) is receiving increased attention due to the incidence in contact sports and limitations with subjective (pen and paper) diagnostic approaches. If an mTBI is undiagnosed and the athlete prematurely returns to play, it can result in serious short-term and/or long-term health complications. This demonstrates the importance of providing more reliable mTBI diagnostic tools to mitigate misdiagnosis. Accordingly, there is a need to develop reliable and efficient objective approaches with computationally robust diagnostic methods. Here in this pilot study, we propose the extraction of Mel Frequency Cepstral Coefficient (MFCC) features from audio recordings of speech that were collected from athletes engaging in rugby union who were diagnosed with an mTBI or not. These features were trained on our novel particle swarm optimised (PSO) bidirectional long short-term memory attention (Bi-LSTM-A) deep learning model. Little-to-no overfitting occurred during the training process, indicating strong reliability of the approach regarding the current test dataset classification results and future test data. Sensitivity and specificity to distinguish those with an mTBI were 94.7% and 86.2%, respectively, with an AUROC score of 0.904. This indicates a strong potential for the deep learning approach, with future improvements in classification results relying on more participant data and further innovations to the Bi-LSTM-A model to fully establish this approach as a pragmatic mTBI diagnostic tool