Genetic Population Structure of the Ground Beetle, Pterostichus oblongopunctatus, Inhabiting a Fragmented and Polluted Landscape: Evidence for Sex-Biased Dispersal

Ground beetles are an integral and functionally important part of many terrestrial ecosystems. Habitat change often influences population genetic structure of carabid beetles. In this study, genetic variation, population differentiation, and sex-specific dispersal patterns were studied in the forest ground beetle, Pterostichus oblongopunctatus F. (Coleoptera: Carabidae), in a fragmented and metal-polluted landscape to assess the consequences of human-induced changes on the population genetic structure. Genotypic variation at five microsatellite loci was screened in 309 beetles from 21 sample locations around zinc-and-lead smelter in southern Poland. Low levels of genetic differentiation among sampling sites were observed, suggesting high gene flow among populations. A negative correlation was found between levels of genetic differentiation and habitat patch size. No significant effects of metal pollution, in terms of genetic bottlenecks and genetic differentiation, were observed. Analyses revealed weak genetic clustering that is loosely tied to the geographic position of the sampled populations. Several tests of sex-biased dispersal were conducted. Most of them indicated male-biased dispersal. Differing levels of dispersal between females and males resulted in sex-specific spatial genetic patterns. Genetic differentiation was significantly correlated with geographical distance for males, but not for females, who were more diverged locally. Also, the effect of habitat patch size was sex-dependent, supporting the finding of different dispersal patterns between the sexes. This study demonstrated the application of microsatellite markers to answer questions regarding complex interactions between population structure and physical properties of the landscape. In the study system, migration appears to be sufficient to override potential effects of environmental pollution as well as habitat fragmentation. This investigation of population genetic structure indicated, for the first time, male-biased dispersal in carabid beetles

Low statistical power and overestimated anthropogenic impacts, exacerbated by publication bias, dominate field studies in global change biology

Field studies are essential to reliably quantify ecological responses to global change because they are exposed to realistic climate manipulations. Yet such studies are limited in replicates, resulting in less power and, therefore, potentially unreliable effect estimates. Furthermore, while manipulative field experiments are assumed to be more powerful than non-manipulative observations, it has rarely been scrutinized using extensive data. Here, using 3847 field experiments that were designed to estimate the effect of environmental stressors on ecosystems, we systematically quantified their statistical power and magnitude (Type M) and sign (Type S) errors. Our investigations focused upon the reliability of field experiments to assess the effect of stressors on both ecosystem's response magnitude and variability. When controlling for publication bias, single experiments were underpowered to detect response magnitude (median power: 18%–38% depending on effect sizes). Single experiments also had much lower power to detect response variability (6%–12% depending on effect sizes) than response magnitude. Such underpowered studies could exaggerate estimates of response magnitude by 2–3 times (Type M errors) and variability by 4–10 times. Type S errors were comparatively rare. These observations indicate that low power, coupled with publication bias, inflates the estimates of anthropogenic impacts. Importantly, we found that meta-analyses largely mitigated the issues of low power and exaggerated effect size estimates. Rather surprisingly, manipulative experiments and non-manipulative observations had very similar results in terms of their power, Type M and S errors. Therefore, the previous assumption about the superiority of manipulative experiments in terms of power is overstated. These results call for highly powered field studies to reliably inform theory building and policymaking, via more collaboration and team science, and large-scale ecosystem facilities. Future studies also require transparent reporting and open science practices to approach reproducible and reliable empirical work and evidence synthesis

orchaRd 2.0 : an R package for visualising meta-analyses with orchard plots

1. Although meta-analysis has become an essential tool in ecology and evolution, reporting of meta-analytic results can still be much improved. To aid this, we have introduced the orchard plot, which presents not only overall estimates and their confidence intervals, but also shows corresponding heterogeneity (as prediction intervals) and individual effect sizes. 2. Here, we have added significant enhancements by integrating many new functionalities into orchaRd 2.0. This updated version allows the visualisation of heteroscedasticity (different variances across levels of a categorical moderator), marginal estimates (e.g. marginalising out effects other than the one visualised), conditional estimates (i.e. estimates of different groups conditioned upon specific values of a continuous variable) and visualisations of all types of interactions between two categorical/continuous moderators. 3. orchaRd 2.0 has additional functions which calculate key statistics from multilevel meta-analytic models such as $I^{2}$ and $R^{2}$. Importantly, orchaRd 2.0 contributes to better reporting by complying with PRISMA-EcoEvo (preferred reporting items for systematic reviews and meta-analyses in ecology and evolution). Taken together, orchaRd 2.0 can improve the presentation of meta-analytic results and facilitate the exploration of previously neglected patterns. 4. In addition, as a part of a literature survey, we found that graphical packages are rarely cited (~3%). We plea that researchers credit developers and maintainers of graphical packages, for example, by citations in a figure legend, acknowledging the use of relevant packages

Pharmacological manipulations of judgement bias:a systematic review and meta-analysis

Validated measures of animal affect are crucial to research spanning numerous disciplines. Judgement bias, which assesses decision-making under ambiguity, is a promising measure of animal affect. One way of validating this measure is to administer drugs with affect-altering properties in humans to non-human animals and determine whether the predicted judgement biases are observed. We conducted a systematic review and meta-analysis using data from 20 published research articles that use this approach, from which 557 effect sizes were extracted. Pharmacological manipulations overall altered judgement bias at the probe cues as predicted. However, there were several moderating factors including the neurobiological target of the drug, whether the drug induced a relatively positive or negative affective state in humans, dosage, and the presented cue. This may partially reflect interference from adverse effects of the drug which should be considered when interpreting results. Thus, the overall pattern of change in animal judgement bias appears to reflect the affect-altering properties of drugs in humans, and hence may be a valuable measure of animal affective valence.Funding Agencies|Biotechnology and Biological Sciences Research Council (BBSRC: SWBio Doctoral Training Programme)Biotechnology and Biological Sciences Research Council (BBSRC) [BB/M009122/1]; Australian Research Council (ARC)Australian Research Council [DP180100818]</p

Rapid literature mapping on the recent use of machine learning for wildlife imagery

Machine (especially deep) learning algorithms are changing the way wildlife imagery is processed. They dramatically speed up the time to detect, count, and classify animals and their behaviours. Yet, we currently have very few systematic literature surveys on its use in wildlife imagery. Through a literature survey (a ‘rapid’ review) and bibliometric mapping, we explored its use across: 1) species (vertebrates), 2) image types (e.g., camera traps, or drones), 3) study locations, 4) alternative machine learning algorithms, 5) outcomes (e.g., recognition, classification, or tracking), 6) reporting quality and openness, 7) author affiliation, and 8) publication journal types. We found that an increasing number of studies used convolutional neural networks (i.e., deep learning). Typically, studies have focused on large charismatic or iconic mammalian species. An increasing number of studies have been published in ecology-specific journals indicating the uptake of deep learning to transform the detection, classification and tracking of wildlife. Sharing of code was limited, with only 20% of studies providing links to analysis code. Much of the published research and focus on animals came from India, China, Australia, or the USA. There were relatively few collaborations across countries. Given the power of machine learning, we recommend increasing collaboration and sharing approaches to utilise increasing amounts of wildlife imagery more rapidly and transform and improve understanding of wildlife behaviour and conservation. Our survey, augmented with bibliometric analyses, provides valuable signposts for future studies to resolve and address shortcomings, gaps, and biases