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  1. Free, publicly-accessible full text available April 1, 2023
  2. Free, publicly-accessible full text available January 1, 2023
  3. A goal of many research programmes in biology is to extract meaningful insights from large, complex datasets. Researchers in ecology, evolution and behavior (EEB) often grapple with long-term, observational datasets from which they construct models to test causal hypotheses about biological processes. Similarly, epidemiologists analyse large, complex observational datasets to understand the distribution and determinants of human health. A key difference in the analytical workflows for these two distinct areas of biology is the delineation of data analysis tasks and explicit use of causal directed acyclic graphs (DAGs), widely adopted by epidemiologists. Here, we review the most recent causal inference literature and describe an analytical workflow that has direct applications for EEB. We start this commentary by defining four distinct analytical tasks (description, prediction, association, causal inference). The remainder of the text is dedicated to causal inference, specifically focusing on the use of DAGs to inform the modelling strategy. Given the increasing interest in causal inference and misperceptions regarding this task, we seek to facilitate an exchange of ideas between disciplinary silos and provide an analytical framework that is particularly relevant for making causal inference from observational data.
  4. Abstract Studies in rodents and captive primates suggest that the early-life social environment affects future phenotype, potentially through alterations to DNA methylation. Little is known of these associations in wild animals. In a wild population of spotted hyenas, we test the hypothesis that maternal care during the first year of life and social connectedness during two periods of early development leads to differences in DNA methylation and fecal glucocorticoid metabolites (fGCMs) later in life. Here we report that although maternal care and social connectedness during the den-dependent life stage are not associated with fGCMs, greater social connectedness during the subadult den-independent life stage is associated with lower adult fGCMs. Additionally, more maternal care and social connectedness after den independence correspond with higher global (%CCGG) DNA methylation. We also note differential DNA methylation near 5 genes involved in inflammation, immune response, and aging that may link maternal care with stress phenotype.
  5. Abstract

    Toxoplasma gondiiis hypothesized to manipulate the behavior of warm-blooded hosts to promote trophic transmission into the parasite’s definitive feline hosts. A key prediction of this hypothesis is thatT. gondiiinfections of non-feline hosts are associated with costly behavior towardT. gondii’s definitive hosts; however, this effect has not been documented in any of the parasite’s diverse wild hosts during naturally occurring interactions with felines. Here, three decades of field observations reveal thatT. gondii-infected hyena cubs approach lions more closely than uninfected peers and have higher rates of lion mortality. We discuss these results in light of 1) the possibility that hyena boldness represents an extended phenotype of the parasite, and 2) alternative scenarios in whichT. gondiihas not undergone selection to manipulate behavior in host hyenas. Both cases remain plausible and have important ramifications forT. gondii’s impacts on host behavior and fitness in the wild.

  6. Grassland monitoring can be challenging because it is time-consuming and expensive to measure grass condition at large spatial scales. Remote sensing offers a time- and cost-effective method for mapping and monitoring grassland condition at both large spatial extents and fine temporal resolutions. Combinations of remotely sensed optical and radar imagery are particularly promising because together they can measure differences in moisture, structure, and reflectance among land cover types. We combined multi-date radar (PALSAR-2 and Sentinel-1) and optical (Sentinel-2) imagery with field data and visual interpretation of aerial imagery to classify land cover in the Masai Mara National Reserve, Kenya using machine learning (Random Forests). This study area comprises a diverse array of land cover types and changes over time due to seasonal changes in precipitation, seasonal movements of large herds of resident and migratory ungulates, fires, and livestock grazing. We classified twelve land cover types with user’s and producer’s accuracies ranging from 66%–100% and an overall accuracy of 86%. These methods were able to distinguish among short, medium, and tall grass cover at user’s accuracies of 83%, 82%, and 85%, respectively. By yielding a highly accurate, fine-resolution map that distinguishes among grasses of different heights, this work not only outlinesmore »a viable method for future grassland mapping efforts but also will help inform local management decisions and research in the Masai Mara National Reserve.« less