Machine learning approaches to assess microendemicity and conservation risk in cave-dwelling arachnofauna

Abstract

The biota of cave habitats faces heightened conservation risks, due to geographic isolation and high levels of endemism. Molecular datasets, in tandem with ecological surveys, have the potential to precisely delimit the nature of cave endemism and identify conservation priorities for microendemic species. Here, we sequenced ultraconserved elements of Tegenaria within, and at the entrances of, 25 cave sites to test phylogenetic relationships, combined with an unsupervised machine learning approach for detecting species. Our analyses identified clear and well-supported genetic breaks in the dataset that accorded closely with morphologically diagnosable units. Through these analyses, we also detected some previously unidentified, potential cryptic morphospecies. We then performed conservation assessments for seven troglobitic Israeli species of this genus and determined five of these to be critically endangered.

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RESEARCH
Conservation Genetics (2024) 25:1103–1110
https://doi.org/10.1007/s10592-024-01627-5
to delimit endemic cave species, as well as develop man-
agement strategies for endangered taxa (Paquin and Hedin
2004). Broadly, cave ecosystems share core abiotic features,
such as reduction or complete absence of light, high rela-
tive humidity, and buered temperature ranges compared
to their surrounding terrestrial surface climates (Barr and
Holsinger 1985). The existence and maintenance of biodi-
versity in cave habitats is predicated on the ability of biota
to adapt to such conditions. Consequently, unique pheno-
typic changes can be observed in cave-dwelling organisms
across the animal tree of life. These changes comprise both
reductive features (e.g., atrophy of structures not required
for subterranean life), as well as constructive adaptations
(e.g., compensatory gains in tactile appendages or olfac-
tory capacity; Re et al. 2018; Riddle et al. 2018). One of the
more conspicuous examples of this phenomenon is the par-
tial or complete loss of eyes in cave-dwelling species. The
Mexican cavesh (Astyanax mexicanus) is a well-studied
exemplar of eye loss in cave-dwelling species. The blind
morph of A. mexicanus is said to have evolved as recently
as 20,000 years ago, exemplifying phenotypic change over
rapid timescales and without the requirement of reproduc-
tive isolation (Fumey et al. 2018). Rapid evolution of dis-
parate phenotypes allows for the study of how speciation
begins in cave populations versus surface populations. Over
Introduction
Cave-dwelling taxa are at heightened risk of extinction due
to the limited ranges imposed by a single cave system or, in
extreme cases, a single cave. These taxa, sometimes referred
to as short-range endemics or microendemics, face an out-
sized threat in the face of disturbance to their habitats and
climate change. (Harvey et al. 2011; Mammola et al. 2018).
With limited individuals to sample, it is a challenge both
Hugh G. Steiner
hgsteiner@wisc.edu
1 Department of Integrative Biology, University of Wisconsin-
Madison, Madison, WI, USA
2 The National Natural History Collections, The Hebrew
University of Jerusalem, Edmond J. Safra Campus, Givat
Ram, Jerusalem 9190401, Israel
3 Department of Ecology, Evolution & Behavior, Edmond J.
Safra Campus, Jerusalem, Israel
4 Department of Biology, Kean University, Union, NJ, USA
5 Department of Systems Biology, Harvard Medical School,
Harvard University, Boston, MA, USA
6 Zoology Museum, University of Wisconsin-Madison,
Madison, WI, USA
Abstract
The biota of cave habitats faces heightened conservation risks, due to geographic isolation and high levels of endemism.
Molecular datasets, in tandem with ecological surveys, have the potential to precisely delimit the nature of cave endemism
and identify conservation priorities for microendemic species. Here, we sequenced ultraconserved elements of Tegenaria
within, and at the entrances of, 25 cave sites to test phylogenetic relationships, combined with an unsupervised machine
learning approach for detecting species. Our analyses identied clear and well-supported genetic breaks in the dataset
that accorded closely with morphologically diagnosable units. Through these analyses, we also detected some previously
unidentied, potential cryptic morphospecies. We then performed conservation assessments for seven troglobitic Israeli
species of this genus and determined ve of these to be critically endangered.
Keywords Machine learning · Phylogenomics · Conservation · Ultraconserved elements (UCEs) · Endemism
Received: 18 September 2023 / Accepted: 26 July 2024 / Published online: 3 August 2024
© The Author(s), under exclusive licence to Springer Nature B.V. 2024
Machine learning approaches to assess microendemicity and
conservation risk in cave-dwelling arachnofauna
Hugh G.Steiner1· ShlomiAharon2,3· JesúsBallesteros4· GuilhermeGainett5· EfratGavish-Regev3·
Prashant P.Sharma1,6
1 3
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