AnimalTraits - a curated animal trait database for body mass, metabolic rate and brain size

Using unstructured crowd-sourced data to evaluate urban tolerance of terrestrial native animal species within a California Mega-City

In response to biodiversity loss and biotic community homogenization in urbanized landscapes, there are increasing efforts to conserve and increase biodiversity within urban areas. Accordingly, around the world, previously extirpated species are (re)colonizing and otherwise infiltrating urban landscapes, while other species are disappearing from these landscapes. Tracking the occurrence of traditionally urban intolerant species and loss of traditionally urban tolerant species should be a management goal of urban areas, but we generally lack tools to study this phenomenon. To address this gap, we first used species' occurrences from iNaturalist, a large collaborative dataset of species observations, to calculate an urban association index (UAI) for 967 native animal species that occur in the city of Los Angeles. On average, the occurrence of native species was negatively associated with our composite measure of urban intensity, with the exception of snails and slugs, which instead occur more frequently in areas of increased urban intensity. Next, we assessed 8,348 0.25 x 0.25 mile grids across the City of Los Angeles to determine the average grid-level UAI scores (i.e., a summary of the UAIs present in a grid cell, which we term Community Urban Tolerance Index or CUTI). We found that areas of higher urban intensity host more urban tolerant species, but also that taxonomic groups differ in their aggregate tolerance of urban areas, and that spatial patterns of tolerance vary between groups. The framework established here has been designed to be iteratively reevaluated by city managers of Los Angeles in order to track the progress of initiatives to preserve and encourage urban biodiversity, but can be rescaled to sample different regions within the city or different cities altogether to provide a valuable tool for city managers globally.

ZooTraits: An R shiny app for exploring animal trait data for ecological and evolutionary research

Animal trait data are scattered across several datasets, making it challenging to compile and compare trait information across different groups. For plants, the TRY database has been an unwavering success for those ecologists interested in addressing how plant traits influence a wide variety of processes and patterns, but the same is not true for most animal taxonomic groups. Here, we introduce ZooTraits, a Shiny app designed to help users explore and obtain animal trait data for research in ecology and evolution. ZooTraits was developed to tackle the challenge of finding in a single site information of multiple trait datasets and facilitating access to traits by providing an easy-to-use, open-source platform. This app combines datasets centralized in the Open Trait Network, raw data from the AnimalTraits database, and trait information for animals compiled by Gonçalves-Souza et al. (2023, Ecology and Evolution 13, e10016). Importantly, the ZooTraits app can be accessed freely and provides a user-friendly interface through three functionalities that will allow users to easily visualize, compare, download, and upload trait data across the animal tree of life-ExploreTrait, FeedTrait, and GetTrait. By using ExploreTrait and GetTrait, users can explore, compare, and extract 3954 trait records from 23,394 species centralized in the Open Traits Network, and trait data for ~2000 species from the AnimalTraits database. The app summarizes trait information for numerous taxonomic groups within the Animal Kingdom, encompassing data from diverse aquatic and terrestrial ecosystems and various geographic regions worldwide. Moreover, ZooTraits enables researchers to upload trait information, serving as a hub for a continually expanding global trait database. By promoting the centralization of trait datasets and offering a platform for data sharing, ZooTraits is facilitating advancements in trait-based ecological and evolutionary studies. We hope that other trait databases will evolve to mirror the approach we have outlined here.

Advancing exposure assessment approaches to improve wildlife risk assessment

The exposure assessment component of a Wildlife Ecological Risk Assessment aims to estimate the magnitude, frequency, and duration of exposure to a chemical or environmental contaminant, along with characteristics of the exposed population. This can be challenging in wildlife as there is often high uncertainty and error caused by broad-based, interspecific extrapolation and assumptions often because of a lack of data. Both the US Environmental Protection Agency (USEPA) and European Food Safety Authority (EFSA) have broadly directed exposure assessments to include estimates of the quantity (dose or concentration), frequency, and duration of exposure to a contaminant of interest while considering "all relevant factors." This ambiguity in the inclusion or exclusion of specific factors (e.g., individual and species-specific biology, diet, or proportion time in treated or contaminated area) can significantly influence the overall risk characterization. In this review, we identify four discrete categories of complexity that should be considered in an exposure assessment-chemical, environmental, organismal, and ecological. These may require more data, but a degree of inclusion at all stages of the risk assessment is critical to moving beyond screening-level methods that have a high degree of uncertainty and suffer from conservatism and a lack of realism. We demonstrate that there are many existing and emerging scientific tools and cross-cutting solutions for tackling exposure complexity. To foster greater application of these methods in wildlife exposure assessments, we present a new framework for risk assessors to construct an "exposure matrix." Using three case studies, we illustrate how the matrix can better inform, integrate, and more transparently communicate the important elements of complexity and realism in exposure assessments for wildlife. Modernizing wildlife exposure assessments is long overdue and will require improved collaboration, data sharing, application of standardized exposure scenarios, better communication of assumptions and uncertainty, and postregulatory tracking. Integr Environ Assess Manag 2024;20:674-698. © 2023 SETAC.

New estimates indicate that males are not larger than females in most mammal species

Sexual size dimorphism has motivated a large body of research on mammalian mating strategies and sexual selection. Despite some contrary evidence, the narrative that larger males are the norm in mammals-upheld since Darwin's Descent of Man-still dominates today, supported by meta-analyses that use coarse measures of dimorphism and taxonomically-biased sampling. With newly-available datasets and primary sources reporting sex-segregated means and variances in adult body mass, we estimate statistically-determined rates of sexual size dimorphism in mammals, sampling taxa by their species richness at the family level. Our analyses of wild, non-provisioned populations representing >400 species indicate that although males tend to be larger than females when dimorphism occurs, males are not larger in most mammal species, suggesting a need to revisit other assumptions in sexual selection research.

How and Why Does Metabolism Scale with Body Mass?

Most explanations for the relationship between body size and metabolism invoke physical constraints; such explanations are evolutionarily inert, limiting their predictive capacity. Contemporary approaches to metabolic rate and life history lack the pluralism of foundational work. Here, we call for reforging of the lost links between optimization approaches and physiology.

Toxicity assessment and detoxification metabolism of sodium pentachlorophenol (PCP-Na) on marine economic species: a case study of Moerella iridescens and Exopalaemon carinicauda

Sodium pentachlorophenol (PCP-Na) is widespread in the marine environment; however, its impact on marine organisms remains under-researched. Moerella iridescens and Exopalaemon carinicauda are marine species of economic importance in China and under threat from PCP-Na pollution. Thus, this study aimed to assess the toxicity and detoxification metabolism of PCP-Na on M. iridescens and E. carinicauda. The study revealed that the 96 h median lethal concentration (LC50) of PCP-Na for M. iridescens and E. carinicauda were 9.895 mg/L and 14.143 mg/L, respectively. A species sensitivity distribution (SSD) for PCP-Na was developed specifically for marine organisms, determining a hazardous concentration to 5% of the species (HC5) of 0.047 mg/L. During the sub-chronic exposure period, PCP-Na accumulated significantly in M. iridescens and E. carinicauda, with highest concentrations of 41.22 mg/kg in the soft tissues of M. iridescens, 42.58 mg/kg in the hepatopancreas of E. carinicauda, and only 0.85 mg/kg in the muscle of E. carinicauda. Furthermore, the study demonstrated that detoxifying metabolic enzymes and antioxidant defense system enzymes of E. carinicauda responded stronger to PCP-Na compared to M. iridescens, suggesting that E. carinicauda may possess a stronger detoxification capacity. Notably, five biomarkers were identified and proposed for monitoring and evaluating PCP-Na contamination. Overall, the results indicated that M. iridescens and E. carinicauda exhibit greater tolerance to PCP-Na than other marine species, but they are susceptible to accumulating PCP-Na in their tissues, posing a significant health risk. Consequently, conducting aquatic health risk assessments in areas with potential PCP-Na contamination is strongly recommended.

Vulnerability to climate change increases with trophic level in terrestrial organisms

The resilience of ecosystem function under global climate change is governed by individual species vulnerabilities and the functional groups they contribute to (e.g. decomposition, primary production, pollination, primary, secondary and tertiary consumption). Yet it remains unclear whether species that contribute to different functional groups, which underpin ecosystem function, differ in their vulnerability to climate change. We used existing upper thermal limit data across a range of terrestrial species (N = 1701) to calculate species warming margins (degrees distance between a species upper thermal limit and the maximum environmental temperature they inhabit), as a metric of climate change vulnerability. We examined whether species that comprise different functional groups exhibit differential vulnerability to climate change, and if vulnerability trends change across geographic space while considering evolutionary history. Primary producers had the broadest warming margins across the globe (μ = 18.72 °C) and tertiary consumers had the narrowest warming margins (μ = 9.64 °C), where vulnerability tended to increase with trophic level. Warming margins had a nonlinear relationship (second-degree polynomial) with absolute latitude, where warming margins were narrowest at about 33°, and were broader at lower and higher absolute latitudes. Evolutionary history explained significant variation in species warming margins, as did the methodology used to estimate species upper thermal limits. We investigated if variation in body mass across the trophic levels could explain why higher trophic level organisms had narrower warming margins than lower trophic level organisms, however, we did not find support for this hypothesis. This study provides a critical first step in linking individual species vulnerabilities with whole ecosystem responses to climate change.

Semi-automated detection of ungulates using UAV imagery and reflective spectrometry

In the field of species conservation, the use of unmanned aerial vehicles (UAV) is increasing in popularity as wildlife observation and monitoring tools. With large datasets created by UAV-based species surveying, the need arose to automate the detection process of the species. Although the use of computer learning algorithms for wildlife detection from UAV-derived imagery is an increasing trend, it depends on a large amount of imagery of the species to train the object detector effectively. However, there are alternatives like object-based image analysis (OBIA) software available if a large amount of imagery of the species is not available to develop a computer-learned object detector. The study tested the semi-automated detection of reintroduced Arabian Oryx (O. leucoryx), using the specie's coat sRGB-colour profiles as input for OBIA to identify adult O. leucoryx, applied to UAV acquired imagery. Our method uses lab-measured spectral reflection of hair sample values, collected from captive O. leucoryx as an input for OBIA ruleset to identify adult O. leucoryx from UAV survey imagery using semi-automated supervised classification. The converted mean CIE Lab reflective spectrometry colour values of n = 50 hair samples of adult O. leucoryx to 8-bit sRGB-colour profiles of the species resulted in the red-band value of 157.450, the green-band value of 151.390 and blue-band value of 140.832. The sRGB values and a minimum size permitter were added as the input of the OBIA ruleset identified adult O. leucoryx with a high degree of efficiency when applied to three UAV census datasets. Using species sRGB-colour profiles to identify re-introduced O. leucoryx and extract location data using a non-invasive UAV-based tool is a novel method with enormous application possibilities. Coat refection sRGB-colour profiles can be developed for a range of species and customised to autodetect and classify the species from remote sensing data.