About

Sharlene Santana is a Professor in the Department of Biology at the University of Washington. Her research aims to understand the mechanisms that underlie diversity in form, function, ecology, and the number of species. She integrates studies of morphology, behavior, ecology, and evolution within an evolutionary context to investigate phenotypic and lineage diversification. Her work is primarily focused on bats, which are one of the most ecologically and morphologically diverse lineages of mammals, providing a natural system to study patterns and mechanisms of diversification. Santana applies comparative, interdisciplinary approaches, involving data collection from free-ranging animals in the field, modern laboratory techniques, and quantitative tools, to test hypotheses about adaptation and drivers of diversification. She has received recognition for her mentorship, including the Outstanding Undergraduate Research Mentor Award at the University of Washington in 2025.

Research topics

• Ecology
• Evolutionary biology
• Biology
• Neuroscience
• Sociology
• Zoology
• Anatomy
• Genetics
• Anthropology
• Computational biology

Selected publications

• Data from: The ecology of attraction: Fruit traits and frugivore diversity in Neotropical <em>Piper</em>

  Open MIND · 2026-01-08

  dataset1st authorCorresponding

  Fruit traits can benefit plant reproduction by enhancing seed dispersal by mutualistic frugivores (e.g., seed dispersal syndromes), but identifying the role of fruit traits in mediating frugivory is challenging because they can serve multiple functions, and plant-frugivore interactions can vary spatially and are rarely exclusive. We use a community of neotropical Piper to test the hypothesis that fruit trait differences between ecotypes (forest, gap) either augment or constrain the diversity of interacting frugivores, with the broader goal of contributing to an understanding of how habitat, phylogeny, and ecological interactions influence fruit trait variation. Since bats are the primary seed dispersers of Piper in the community studied, we map the traits of Piper species consumed by bats and discuss how these may align with bat sensory modes and abilities to capture and consume fruit. We find differences in fruit traits between Piper ecotypes that are consistent with accessibility by different communities of frugivores. Gap Piper, which exhibits greater frugivore diversity (bats, birds, insects), has a significantly longer fruiting period, and their ripe fruits have a more chemically diverse fruit scent bouquet. Conversely, forest Piper is consumed only by bats, generally produces fruit in short peaks, and their fruit has a less diverse scent bouquet. Additionally, gap Piper fruits tend to be greener, softer, erect, and have smaller seeds, whereas forest Piper fruits span a wider range of colors, are harder and either erect or pendulous, and have larger seeds; however, these differences did not emerge as statistically significant in our analyses. Piper species present in bat diets are characterized by having longer fruiting phenologies, greener fruits, and volatile organic compounds in their fruit scent that have been previously demonstrated to be preferred by Piper specialist bats. Our results suggest that fruit traits in Piper may facilitate or constrain interactions with different frugivore communities across habitats. As most of the Piper species studied produce fruits that are visually inconspicuous, chemically complex, and consumed by bats, this system merits further study at a broader taxonomic scale to evaluate the possibility of a dispersal syndrome.

  DOI

• Ranges Network collected mammal trait measurements and summary from western North America

  Zenodo (CERN European Organization for Nuclear Research) · 2026-01-09

  datasetOpen access

  Over the past two decades, digitization of U.S. vertebrate collections has opened critical temporal, spatial, and taxonomic information from specimens, stimulating new levels of data sharing and significantly advancing biodiversity knowledge. However, still missing are the specimen-level trait data essential for establishing faunal baselines and informing predictions of global change response. We propose the Ranges Digitization Network (Ranges), which will close this gap by mobilizing an estimated >5 million trait measurements from 1.2 million specimens from Western North America (WNA). WNA is a region of high temperate mammal diversity and a natural laboratory for studying speciation, phenotypic diversification, biogeographic principles, and community assembly over deep and shallow timescales. To build capacity for transformative research in these areas, Ranges brings together a digitization network of 19 institutions, expands informatics tools for trait extraction and standardization from specimen records, and immediately (in Year 1) provisions specimen-linked trait data in usable form via existing portals. The specific goals of Ranges will be to: 1) Digitize traits from 1.2 million mammal specimens, append these to digital records, and publish openly in community repositories; 2) digitize, georeference, and mobilize four regionally significant mammal collections, making their data research-ready for the first time; and 3) develop dense, intraspecific-level 3D image resources for >3,500 mammal specimens to facilitate acquisition of complex internal traits that can be linked to complimentary, specimen-level datasets existing in an extended specimen network. The Ranges Project Ranges is an NSF-funded project that seeks to digitize traits from over one million mammal specimens from 19 natural history museums, with a focus on western North America. The project will allow researchers to build better baselines for biodiversity and improve predictions of how mammals respond to changing environments to address major digitization challenges, expand the utility of specimens and use them to create new scientific knowledge. Website: https://ranges-network.org/ Give Credit Where Credit is Due Please cite this Zenodo resource AND any additional DOIs from individual institutional downloads. Ranges Baseline Sources The Ranges Baseline Trait Collection contains 19 datasets downloaded from the Global Biodiversity Information Facility (GBIF) in January/February of 2024. The institutions represented are all participants in the Ranges Network. The purpose of this dataset is (1) to provide a baseline of the traits digitized by the 19 Ranges institutional participants prior to the start of the Ranges digitization effort, and (2) to update the FuTRES traits index to include occurrenceIDs for each record in the FuTRES traits portal (https://futres-data-interface.netlify.app/). Source Institutions (for a complete list of citations see the document, SourceDataset_DOIs.tsv.: Date Institution DOI 20240227 ASU, Arizona State University https://doi.org/10.15468/dl.pztqzk 20240110 CAS, California Academy of Sciences https://doi.org/10.15468/dl.897qq5 20240227 CSULB, California State University, Long Beach https://doi.org/10.15468/dl.cfh4uq 20240110 DMNS, Denver Museum of Nature and Science https://doi.org/10.15468/dl.v5y2pr 20240227 FMNH, Field Museum of Natural History https://doi.org/10.15468/dl.gzuptm 20240110 HSUVM, California Polytechnic Univ. Humboldt, Vertebrate Museum https://doi.org/10.15468/dl.tyds38 20240227 KU, Univ. of Kansas Natural History Museum https://doi.org/10.15468/dl.7g7j8m 20240227 LACM, Natural History Museum of LA County https://doi.org/10.15468/dl.9hrst8 20240225 MSB, Univ. of New Mexico, Museum of Southwestern Biology https://doi.org/10.15468/dl.5wwfu9 20240225 MVZ, Univ. of California Berkeley, Museum of Vertebrate Zoology https://doi.org/10.15468/dl.5mbsge 20240110 TCWC, Texas A&M Univ., Biodiversity Research & Teaching Collection https://doi.org/10.15468/dl.26eh3k 20240110 TTU, Texas Tech University https://doi.org/10.15468/dl.45wsbt 20240227 UAM, University of Alaska, Museum of the North https://doi.org/10.15468/dl.8c95qk 20240225 UMMZ, University of Michigan, Museum of Zoology https://doi.org/10.15468/dl.w7a35w 20240225 UMNH, Natural History Museum of Utah https://doi.org/10.15468/dl.2erdam 20240227 UMZM, Univ. of Montana, Philip L. Wright Zoological Museum https://doi.org/10.15468/dl.jevgd8 20240225 UNR, University of Nevada Museum of Natural History https://doi.org/10.15468/dl.fbcvye 20240225 UWBM, Univ. of Washington, Burke Museum https://doi.org/10.15468/dl.s5u9z5 20240227 UWYMV, University of Wyoming Museum of Vertebrates https://doi.org/10.15468/dl.9wsn76 Traits represented in the Ranges Baseline The Ranges Baseline dataset contains measurements for 56 specific mammal traits. A complete list of the traits is available in the attached file, Ranges Baseline Trait List.pdf. Statistics for the Ranges Baseline Record set info: 1,849,448 records 1,779,593 occurrenceIDs 19 institutions Measurements across all traits (total): 2,941,833 Taxonomic Representation*: Percent of Extracted Digital Traits by Mammalian Order - See attached file, Graphic_ Percent of Extracted Digital Traits by Mammalian Order.pdf. Each layer of the plot depicts cumulative percent of traits available per taxonomic Order. The outer layer shows values for all traits, and the inner two layers show values for just morphological and reproductive traits, respectively. Only data that were mappable to a formerly or currently recognized mammalian order were considered. Measurements per trait (summary): mean 52,532.73 median 3401.5 range 0 - 437,752 Measurements per trait (all, ordered) (6 columns): Trait Total Meas. Trait Total Meas. Trait Total Meas. hind_foot_length_mm 437752 pregnancy_state 14085 mammary_count 266 total_length_mm 409444 tragus_length_mm 13370 tail_length_ambiguous 234 tail_length_mm 391253 hind_foot_length_units_inferred 12320 ear_length_estimated 123 body_mass_grams 388288 embryo_size_length_mm 9541 ovary_length_mm 84 ear_length_mm 344047 embryo_count_right 7624 hind_foot_length_estimated 81 hind_foot_length_includes 335367 embryo_count_left 7417 ovary_width_mm 79 ear_length_measured_from 233196 placental_scar_count 7187 placental_scars_side_1 49 total_length_units_inferred 42786 forearm_length_units_inferred 3685 placental_scars_both_sides 47 tail_length_units_inferred 35685 placental_scars_right_side 3652 placental_scars_side_2 47 placental_scars_present 31274 embryo_size_units_inferred 3151 ovary_description 9 ear_length_units_inferred 27396 placental_scars_left_side 3066 forearm_length_estimated 5 total_length_ambiguous 23875 body_mass_estimated 1857 embryo_count_males 5 vagina_state 22708 total_length_estimated 1357 tragus_length_estimated 3 body_mass_ambiguous 22277 tail_length_estimated 1267 embryo_count_females 3 body_mass_units_inferred 22013 mammary_state 1113 embryo_count_side_1 2 lactation_state 21317 tragus_length_units_inferred 932 embryo_count_side_2 2 forearm_length_mm 21257 embryo_size_width_mm 299 ovary_size_units_inferred 1 embryo_count 20585 gonad_length_mm 273 gonad_size_units_inferred 0 ear_length_ambiguous 17808 gonad_width_mm 269 Measurements per institution (all, ordered): institutionCode total measurements institutionCode total measurements MSB 1095463 CAS 37027 UAM 542499 HSU 34556 UWBM 354348 UNR 15002 MVZ 203000 ASU 3304 TTU 189785 UWYMV 1284 LACM 136305 CSULB 0 DMNS 106071 FMNH 0 UMNH 96830 TCWC 0 KU 69774 UMMZ 0 UMZM 56585 Statistics Per Institution A complete tally of the number of trait measurements by institution per trait type is available in the attached file, Ranges Baseline Traits: Measurements per Trait by Institution.pdf. *Disclaimer Some taxonomic names in this collection may be assigned to non-mammalian taxa or may no longer be valid. All data are presented as downloaded from GBIF.org. Users of this data are responsible for their own data quality and completeness checking. The Ranges Network is not responsible for the quality and completeness of these data.

  PublisherDOI

• Dietary specializations are captured by jaw muscle proportions in mammals

  bioRxiv (Cold Spring Harbor Laboratory) · 2026-05-21

  articleOpen access

  Mammalian diet and feeding ecology are often reflected by craniofacial skeleton specializations, but feeding requires skeletal actuation by a complex suite of muscles with varying sizes, lines of action, and mechanical function. While muscles play a critical role in feeding mechanics, and hence diet, it remains unclear how well variation in jaw muscle morphology predicts diet in mammals. We quantified the evolutionary interplay between mammalian muscle morphology and diet using a large and taxonomically broad sample. We measured the relative proportions and putative force production capacity, quantified as muscle physiological cross-sectional area (PCSA), for the major adductor complexes, along with a key jaw depressor, in 91 mammalian species (30 chiropterans, 33 primates, and 28 ungulates, carnivorans, rodents, and marsupials). We recovered clear dietary signals for several muscle complexes, with the medial pterygoid (larger in herbivores) and temporalis (larger in carnivores) performing best as dietary predictors. The medial pterygoid is particularly relevant for the mechanical innovation in mammals of moving the mandible along non-orthal, medio-lateral trajectories during mastication. Our findings underscore the intuitive, yet previously unquantified, importance of muscles in the evolution of mandibular roll, yaw, and lateral translation, all mammalian hallmarks of processing diverse types of food.

  PublisherDOI

• The ecology of attraction: Fruit traits and frugivore diversity in neotropical <i>Piper</i>

  Functional Ecology · 2026-01-27

  articleOpen access1st authorCorresponding

  Abstract Fruit traits can benefit plant reproduction by enhancing seed dispersal by mutualistic frugivores (e.g. seed dispersal syndromes), but identifying the role of specific fruit traits in mediating frugivory is challenging because these traits can serve multiple functions, and plant–frugivore interactions can vary spatially and are rarely exclusive. We use a community of neotropical Piper to test the hypothesis that fruit trait differences between ecotypes (forest, gap) either augment or constrain the diversity of interacting frugivores, with the broader goal of contributing to an understanding of how fruit trait variation is influenced by habitat, phylogeny, and ecological interactions. Since bats are the primary seed dispersers of Piper in the community studied, we map the traits of Piper species consumed by bats and discuss how these may align with bat sensory modes and abilities to capture and consume fruit. We find differences in fruit traits between Piper ecotypes that are consistent with accessibility by different communities of frugivores. Gap Piper , which exhibit greater frugivore diversity (bats, birds, insects), have significantly longer fruiting periods and their ripe fruits have a more chemically diverse fruit scent bouquet. Conversely, forest Piper are consumed only by bats, generally produce fruits in short peaks, and their fruits have a less diverse scent bouquet. Additionally, gap Piper fruits tend to be greener, softer, erect and have smaller seeds, whereas forest Piper fruits span a wider range of colours, are harder and either erect or pendulous, and have larger seeds; however, these differences did not emerge as statistically significant in our analyses. Piper species present in bat diets are characterized by having longer fruiting phenologies, greener fruits, and volatile organic compounds in their fruit scent that have been previously demonstrated to be preferred by Piper specialist bats. Our results suggest that fruit traits in Piper may facilitate or constrain interactions with different frugivore communities across habitats. As most of the Piper species studied produce fruits that are visually inconspicuous, chemically complex, and consumed by bats, this system merits further study at a broader taxonomic scale to evaluate the possibility of a dispersal syndrome. Read the free Plain Language Summary for this article on the Journal blog.

  PublisherDOI

• Convergent mechanisms, divergent strategies: a comparison of nectar intake between a generalist and a specialist bat species

  Journal of Experimental Biology · 2026-03-01 · 2 citations

  articleOpen access

  Nectar-feeding bats exhibit a range of specialized adaptations that allow them to extract nectar from flowers efficiently. These adaptations include tongue morphological traits and feeding strategies that reflect varying degrees of specialization to nectarivory. While some aspects of the drinking mechanics of highly specialized nectar bats have been studied, little is known about the feeding behaviors of non-specialized species such as Phyllostomus discolor. This study compares the nectar extraction behaviors of P. discolor and the specialized Anoura geoffroyi, examining morphological and biomechanical adaptations that affect feeding efficiency and foraging strategies. We used electron microscopy to study the lingual surface, and high-speed videography to analyze tongue kinematics and feeding efficiency. Both bat species possess hair-like papillae that form a brush-like tongue surface, and both extract nectar using a lapping mechanism; however, they exhibited notable behavioral and biomechanical differences resulting in variation in feeding efficiency. Phyllostomus discolor has a shorter, less flexible tongue than A. geoffroyi, but exhibits similar licking frequencies. Unlike A. geoffroyi, which performs brief hover-feeding bouts, P. discolor perches on the inflorescences, drinks for longer, and extracts more nectar per visit. However, P. discolor exhibited lower feeding efficiency, likely due to its reduced tongue protrusion distance and shorter, less abundant papillae. These findings reveal convergence in general feeding mechanism, i.e. brush-tongue lapping, but highlight divergence in morphological and behavioral traits that affect feeding kinematics and efficiency. Our study illuminates how foraging strategy and tongue morphology affect drinking efficiency, pointing to evolutionary pathways that promote niche differentiation within nectar-feeding bat communities.

  PublisherOA PDFDOI

• Ranges Network collected mammal trait measurements and summary from western North America

  Zenodo (CERN European Organization for Nuclear Research) · 2026-01-09

  datasetOpen access

  Over the past two decades, digitization of U.S. vertebrate collections has opened critical temporal, spatial, and taxonomic information from specimens, stimulating new levels of data sharing and significantly advancing biodiversity knowledge. However, still missing are the specimen-level trait data essential for establishing faunal baselines and informing predictions of global change response. We propose the Ranges Digitization Network (Ranges), which will close this gap by mobilizing an estimated >5 million trait measurements from 1.2 million specimens from Western North America (WNA). WNA is a region of high temperate mammal diversity and a natural laboratory for studying speciation, phenotypic diversification, biogeographic principles, and community assembly over deep and shallow timescales. To build capacity for transformative research in these areas, Ranges brings together a digitization network of 19 institutions, expands informatics tools for trait extraction and standardization from specimen records, and immediately (in Year 1) provisions specimen-linked trait data in usable form via existing portals. The specific goals of Ranges will be to: 1) Digitize traits from 1.2 million mammal specimens, append these to digital records, and publish openly in community repositories; 2) digitize, georeference, and mobilize four regionally significant mammal collections, making their data research-ready for the first time; and 3) develop dense, intraspecific-level 3D image resources for >3,500 mammal specimens to facilitate acquisition of complex internal traits that can be linked to complimentary, specimen-level datasets existing in an extended specimen network. The Ranges Project Ranges is an NSF-funded project that seeks to digitize traits from over one million mammal specimens from 19 natural history museums, with a focus on western North America. The project will allow researchers to build better baselines for biodiversity and improve predictions of how mammals respond to changing environments to address major digitization challenges, expand the utility of specimens and use them to create new scientific knowledge. Website: https://ranges-network.org/ Give Credit Where Credit is Due Please cite this Zenodo resource AND any additional DOIs from individual institutional downloads. Ranges Baseline Sources The Ranges Baseline Trait Collection contains 19 datasets downloaded from the Global Biodiversity Information Facility (GBIF) in January/February of 2024. The institutions represented are all participants in the Ranges Network. The purpose of this dataset is (1) to provide a baseline of the traits digitized by the 19 Ranges institutional participants prior to the start of the Ranges digitization effort, and (2) to update the FuTRES traits index to include occurrenceIDs for each record in the FuTRES traits portal (https://futres-data-interface.netlify.app/). Source Institutions (for a complete list of citations see the document, SourceDataset_DOIs.tsv.: Date Institution DOI 20240227 ASU, Arizona State University https://doi.org/10.15468/dl.pztqzk 20240110 CAS, California Academy of Sciences https://doi.org/10.15468/dl.897qq5 20240227 CSULB, California State University, Long Beach https://doi.org/10.15468/dl.cfh4uq 20240110 DMNS, Denver Museum of Nature and Science https://doi.org/10.15468/dl.v5y2pr 20240227 FMNH, Field Museum of Natural History https://doi.org/10.15468/dl.gzuptm 20240110 HSUVM, California Polytechnic Univ. Humboldt, Vertebrate Museum https://doi.org/10.15468/dl.tyds38 20240227 KU, Univ. of Kansas Natural History Museum https://doi.org/10.15468/dl.7g7j8m 20240227 LACM, Natural History Museum of LA County https://doi.org/10.15468/dl.9hrst8 20240225 MSB, Univ. of New Mexico, Museum of Southwestern Biology https://doi.org/10.15468/dl.5wwfu9 20240225 MVZ, Univ. of California Berkeley, Museum of Vertebrate Zoology https://doi.org/10.15468/dl.5mbsge 20240110 TCWC, Texas A&M Univ., Biodiversity Research & Teaching Collection https://doi.org/10.15468/dl.26eh3k 20240110 TTU, Texas Tech University https://doi.org/10.15468/dl.45wsbt 20240227 UAM, University of Alaska, Museum of the North https://doi.org/10.15468/dl.8c95qk 20240225 UMMZ, University of Michigan, Museum of Zoology https://doi.org/10.15468/dl.w7a35w 20240225 UMNH, Natural History Museum of Utah https://doi.org/10.15468/dl.2erdam 20240227 UMZM, Univ. of Montana, Philip L. Wright Zoological Museum https://doi.org/10.15468/dl.jevgd8 20240225 UNR, University of Nevada Museum of Natural History https://doi.org/10.15468/dl.fbcvye 20240225 UWBM, Univ. of Washington, Burke Museum https://doi.org/10.15468/dl.s5u9z5 20240227 UWYMV, University of Wyoming Museum of Vertebrates https://doi.org/10.15468/dl.9wsn76 Traits represented in the Ranges Baseline The Ranges Baseline dataset contains measurements for 56 specific mammal traits. A complete list of the traits is available in the attached file, Ranges Baseline Trait List.pdf. Statistics for the Ranges Baseline Record set info: 1,849,448 records 1,779,593 occurrenceIDs 19 institutions Measurements across all traits (total): 2,941,833 Taxonomic Representation*: Percent of Extracted Digital Traits by Mammalian Order - See attached file, Graphic_ Percent of Extracted Digital Traits by Mammalian Order.pdf. Each layer of the plot depicts cumulative percent of traits available per taxonomic Order. The outer layer shows values for all traits, and the inner two layers show values for just morphological and reproductive traits, respectively. Only data that were mappable to a formerly or currently recognized mammalian order were considered. Measurements per trait (summary): mean 52,532.73 median 3401.5 range 0 - 437,752 Measurements per trait (all, ordered) (6 columns): Trait Total Meas. Trait Total Meas. Trait Total Meas. hind_foot_length_mm 437752 pregnancy_state 14085 mammary_count 266 total_length_mm 409444 tragus_length_mm 13370 tail_length_ambiguous 234 tail_length_mm 391253 hind_foot_length_units_inferred 12320 ear_length_estimated 123 body_mass_grams 388288 embryo_size_length_mm 9541 ovary_length_mm 84 ear_length_mm 344047 embryo_count_right 7624 hind_foot_length_estimated 81 hind_foot_length_includes 335367 embryo_count_left 7417 ovary_width_mm 79 ear_length_measured_from 233196 placental_scar_count 7187 placental_scars_side_1 49 total_length_units_inferred 42786 forearm_length_units_inferred 3685 placental_scars_both_sides 47 tail_length_units_inferred 35685 placental_scars_right_side 3652 placental_scars_side_2 47 placental_scars_present 31274 embryo_size_units_inferred 3151 ovary_description 9 ear_length_units_inferred 27396 placental_scars_left_side 3066 forearm_length_estimated 5 total_length_ambiguous 23875 body_mass_estimated 1857 embryo_count_males 5 vagina_state 22708 total_length_estimated 1357 tragus_length_estimated 3 body_mass_ambiguous 22277 tail_length_estimated 1267 embryo_count_females 3 body_mass_units_inferred 22013 mammary_state 1113 embryo_count_side_1 2 lactation_state 21317 tragus_length_units_inferred 932 embryo_count_side_2 2 forearm_length_mm 21257 embryo_size_width_mm 299 ovary_size_units_inferred 1 embryo_count 20585 gonad_length_mm 273 gonad_size_units_inferred 0 ear_length_ambiguous 17808 gonad_width_mm 269 Measurements per institution (all, ordered): institutionCode total measurements institutionCode total measurements MSB 1095463 CAS 37027 UAM 542499 HSU 34556 UWBM 354348 UNR 15002 MVZ 203000 ASU 3304 TTU 189785 UWYMV 1284 LACM 136305 CSULB 0 DMNS 106071 FMNH 0 UMNH 96830 TCWC 0 KU 69774 UMMZ 0 UMZM 56585 Statistics Per Institution A complete tally of the number of trait measurements by institution per trait type is available in the attached file, Ranges Baseline Traits: Measurements per Trait by Institution.pdf. *Disclaimer Some taxonomic names in this collection may be assigned to non-mammalian taxa or may no longer be valid. All data are presented as downloaded from GBIF.org. Users of this data are responsible for their own data quality and completeness checking. The Ranges Network is not responsible for the quality and completeness of these data.

  PublisherDOI

• A novel semantic theory of the assembly rules of interaction networks

  2025-11-26

  articleOpen access

  In the web of life, every interaction between species tells a story of cooperation, conflict, or chance. For centuries, ecologists have charted these stories to better understand phenomena such as pollination and disease. We have been using the lens of network science to distill them into topological patterns such as nestedness or modularity. Yet, like in the old buddhist parable of the blind monks and the elephant, these separate views have often missed the bigger picture. In this Perspective, we review the history of topological studies of interaction networks, challenge the “black-and-white” paradigm, and point out a way forward: the Integrative Theory of Interaction Networks (ITIN). This theory unveils the logic of how interaction networks assemble through sequential, rule-bound processes shaped mainly by resource dissimilarity and interaction type. We show that compound topologies are likely the norm in function-oriented, taxonomically-inclusive, well-sampled networks. ITIN is deductive, predictive, and testable, mapping from first principles to real-world systems, thus helping us grasp “the whole elephant”. As ecologists confront the polycrisis, from irreparable biodiversity loss to global health threats, ITIN offers a coherent framework to understand, predict, and restore the threads of the web of life.

  PublisherOA PDFDOI

• Frugivore Traits Predict Plant–Frugivore Interactions Using Generalized Joint Attribute Modeling

  Ecology and Evolution · 2025-01-01 · 1 citations

  articleOpen access

  ABSTRACT Under an adaptive hypothesis, the reciprocal influence between mutualistic plants and frugivores is expected to result in suites of matching frugivore and plant traits that structure fruit consumption. Recent work has suggested fruit traits can represent adaptations to broad groups of functionally similar frugivores, but the role of frugivore traits and within‐species variation in structuring fruit consumption is less understood. To address these knowledge gaps, we assess the presence of reciprocal trait matching for the mutualistic ecological network comprising of Carollia bats that feed on and disperse Piper seeds. We used generalized joint attribute modeling (GJAM), a Bayesian modeling approach that simultaneously accounts for multiple sources of variance across trait types. In support of frugivore adaptation to their dietary composition and suggesting niche partitioning among Carollia bats, we find differential consumption of a suite of Piper species influenced by bat traits such as body size; however, the Piper morphological traits considered had no effect on bat consumption. Slow evolutionary rates, dispersal by other vertebrates, and unexamined fruit traits, such as Piper chemical bouquets, may explain the lack of association between bat Piper consumption and fruit morphological traits. We have identified a potential asymmetric influence of frugivore traits on plant–frugivore interactions, providing a template for future trait analyses of plant–animal networks. As intraspecific trait variation is rarely included in studies on trait matching, this paper contributes to closing that important knowledge gap.

  PublisherOA PDFDOI

• Extending mammal specimens with their essential phenotypic traits

  Journal of Mammalogy · 2025-07-26 · 2 citations

  articleOpen access

  Natural history collections are repositories of biodiversity specimens that provide critical infrastructure for studies of mammals. Over the past 3 decades, digitization of collections has opened up the temporal and spatial properties of specimens, stimulating new data sharing, use, and training across the biodiversity sciences. These digital records are the cornerstones of an "extended specimen network," in which the diverse data derived from specimens become digital, linked, and openly accessible for science and policy. However, still missing from most digital occurrences of mammals are their morphological, reproductive, and life-history traits. Unlocking this information will advance mammalogy, establish richer faunal baselines in an era of rapid environmental change, and contextualize other types of specimen-derived information toward new knowledge and discovery. Here, we present the Ranges Digitization Network (Ranges), a community effort to digitize specimen-level traits from all terrestrial mammals of western North America, append them to digital records, publish them openly in community repositories, and make them interoperable with complimentary data streams. Ranges is a consortium of 23 institutions with an initial focus on non-marine mammal species (both native and introduced) occurring in western Canada, the western United States, and Mexico. The project will establish trait data standards and informatics workflows that can be extended to other regions, taxa, and traits. Reconnecting mammalogists, museum professionals, and researchers for a new era of collections digitization will catalyze advances in mammalogy and create a community-curated trait resource for training and engagement with global conservation initiatives.

  PublisherDOI

• Superstable lipid vacuoles endow cartilage with its shape and biomechanics

  Science · 2025-01-09 · 22 citations

  articleOpen access

  Conventionally, the size, shape, and biomechanics of cartilages are determined by their voluminous extracellular matrix. By contrast, we found that multiple murine cartilages consist of lipid-filled cells called lipochondrocytes. Despite resembling adipocytes, lipochondrocytes were molecularly distinct and produced lipids exclusively through de novo lipogenesis. Consequently, lipochondrocytes grew uniform lipid droplets that resisted systemic lipid surges and did not enlarge upon obesity. Lipochondrocytes also lacked lipid mobilization factors, which enabled exceptional vacuole stability and protected cartilage from shrinking upon starvation. Lipid droplets modulated lipocartilage biomechanics by decreasing the tissue's stiffness, strength, and resilience. Lipochondrocytes were found in multiple mammals, including humans, but not in nonmammalian tetrapods. Thus, analogous to bubble wrap, superstable lipid vacuoles confer skeletal tissue with cartilage-like properties without "packing foam-like" extracellular matrix.

  PublisherOA PDFDOI

Recent grants

• Collaborative Research: Chance or necessity? Adaptive vs. non adaptive evolution in plant-frugivore interactions

  NSF · $550k · 2015–2020

• Macroevolutionary Analyses of Cranial Morphology and Function in Mammals

  NSF · $673k · 2016–2020

• Meeting: A Bigger Picture: Organismal Function at the Nexus of Development, Ecology, and Evolution; Society for Integrative and Comparative Biology; Portland, Oregon; January 2016

  NSF · $15k · 2015–2016

• Collaborative research: Understanding the role of developmental bias in the morphological diversification of bat molars

  NSF · $545k · 2020–2026

• Collaborative Research: The role of multifunctionality in the evolution of cranial morphological diversity in bats

  NSF · $800k · 2022–2026

Frequent coauthors

• Laurel R. Yohe

  Planetary Science Institute

  48 shared

• Leith B. Leiser‐Miller

  University of Washington

  43 shared

• Chris J. Law

  Burke Museum of Natural History and Culture

  40 shared

• Kathryn E. Stanchak

  University of Washington

  26 shared

• Liliana M. Dávalos

  Stony Brook University

  25 shared

• Abigail Curtis

  University of Washington

  25 shared

• Jessica H. Arbour

  Middle Tennessee State University

  24 shared

• Zofia A. Kaliszewska

  University of Washington

  23 shared

Labs

• Santana LabPI

Awards & honors

• Outstanding Undergraduate Research Mentor Award, University…