Linking camera-trap data to taxonomy: Identifying photographs of morphologically similar chipmunks

Cryptic species conservation: a review

Cryptic species are groups of two or more taxa that were previously classified as single nominal species. Being almost morphologically indistinguishable, cryptic species have historically been hard to detect. Only through modern morphometric, genetic, and molecular analyses has the hidden biodiversity of cryptic species complexes been revealed. Cryptic diversity is now widely acknowledged, but unlike more recognisable, charismatic species, scientists face additional challenges when studying cryptic taxa and protecting their wild populations. Demographical and ecological data are vital to facilitate and inform successful conservation actions, particularly at the individual species level, yet this information is lacking for many cryptic species due to their recent taxonomic description and lack of research attention. The first part of this article summarises cryptic speciation and diversity, and explores the numerous barriers and considerations that conservation biologists must navigate to detect, study and manage cryptic species populations effectively. The second part of the article seeks to address how we can overcome the challenges associated with efficiently and non-invasively detecting cryptic species in-situ, and filling vital knowledge gaps that are currently inhibiting applied conservation. The final section discusses future directions, and suggests that large-scale, holistic, and collaborative approaches that build upon successful existing applications will be vital for cryptic species conservation. This article also acknowledges that sufficient data to implement effective species-specific conservation will be difficult to attain for many cryptic animals, and protected area networks will be vital for their conservation in the short term.

Towards the fully automated monitoring of ecological communities

High-resolution monitoring is fundamental to understand ecosystems dynamics in an era of global change and biodiversity declines. While real-time and automated monitoring of abiotic components has been possible for some time, monitoring biotic components-for example, individual behaviours and traits, and species abundance and distribution-is far more challenging. Recent technological advancements offer potential solutions to achieve this through: (i) increasingly affordable high-throughput recording hardware, which can collect rich multidimensional data, and (ii) increasingly accessible artificial intelligence approaches, which can extract ecological knowledge from large datasets. However, automating the monitoring of facets of ecological communities via such technologies has primarily been achieved at low spatiotemporal resolutions within limited steps of the monitoring workflow. Here, we review existing technologies for data recording and processing that enable automated monitoring of ecological communities. We then present novel frameworks that combine such technologies, forming fully automated pipelines to detect, track, classify and count multiple species, and record behavioural and morphological traits, at resolutions which have previously been impossible to achieve. Based on these rapidly developing technologies, we illustrate a solution to one of the greatest challenges in ecology: the ability to rapidly generate high-resolution, multidimensional and standardised data across complex ecologies.

Misinterpretation of Genomic Data Matters for Endangered Species Listing: The Sub-specific Status of the Peñasco Least Chipmunk (Neotamias minimus atristriatus)

We provide a response to a recently published evaluation of the subspecies status of the Peñasco least chipmunk (Neotamias minimus atristriatus). The work we discuss used exon capture genomic approaches and concluded that their results did not support the distinction of this taxon as a subspecies, with recommendation that it be synonymized with N. m. operarius. We refute the interpretations, conclusions, and taxonomic recommendations of this study, and explain in clearer terms how to interpret genomic analyses for applied management. We identify four broad conceptual issues that led to errant recommendations: (1) interpretation of subspecies and diagnosability, (2) inappropriate use of reciprocal monophyly as a criterion for subspecies, (3) importance of geographic isolation, and (4) error in hypothesis testing and misinterpretation of results. We conclude that the data from this genomic appraisal add to information from prior studies providing strong support for recognition of N. m. atristriatus as a subspecies. Our conclusions have important and immediate implications for the proposed listing of N. m. atristriatus as an endangered species under the U.S. Endangered Species Act.