Improving area of occupancy estimates for parapatric species using distribution models and support vector machines

As geographic range estimates for the IUCN Red List guide conservation actions, accuracy and ecological realism are crucial. IUCN’s extent of occurrence (EOO) is the general region including the species’ range, while area of occupancy (AOO) is the subset of EOO occupied by the species. Data‐poor species with incomplete sampling present particular difficulties, but species distribution models (SDMs) can be used to predict suitable areas. Nevertheless, SDMs typically employ abiotic variables (i.e., climate) and do not explicitly account for biotic interactions that can impose range constraints. We sought to improve range estimates for data‐poor, parapatric species by masking out areas under inferred competitive exclusion. We did so for two South American spiny pocket mice: Heteromys australis (Least Concern) and Heteromys teleus (Vulnerable due to especially poor sampling), whose ranges appear restricted by competition. For both species, we estimated EOO using SDMs and AOO with four approaches: occupied grid cells, abiotic SDM prediction, and this prediction masked by approximations of the areas occupied by each species’ congener. We made the masks using support vector machines (SVMs) fit with two data types: occurrence coordinates alone; and coordinates along with SDM predictions of suitability. Given the uncertainty in calculating AOO for low‐data species, we made estimates for the lower and upper bounds for AOO, but only make recommendations for H. teleus as its full known range was considered. The SVM approaches (especially the second one) had lower classification error and made more ecologically realistic delineations of the contact zone. For H. teleus, the lower AOO bound (a strongly biased underestimate) corresponded to Endangered (occupied grid cells), while the upper bounds (other approaches) led to Near Threatened. As we currently lack data to determine the species’ true occupancy within the post‐processed SDM prediction, we recommend that an updated listing for H. teleus include these bounds for AOO. This study advances methods for estimating the upper bound of AOO and highlights the need for better ways to produce unbiased estimates of lower bounds. More generally, the SVM approaches for post‐processing SDM predictions hold promise for improving range estimates for other uses in biogeography and conservation

An empirical evaluation of camera trap study design: How many, how long and when?

Abstract Camera traps deployed in grids or stratified random designs are a well‐established survey tool for wildlife but there has been little evaluation of study design parameters. We used an empirical subsampling approach involving 2,225 camera deployments run at 41 study areas around the world to evaluate three aspects of camera trap study design (number of sites, duration and season of sampling) and their influence on the estimation of three ecological metrics (species richness, occupancy and detection rate) for mammals. We found that 25–35 camera sites were needed for precise estimates of species richness, depending on scale of the study. The precision of species‐level estimates of occupancy (ψ) was highly sensitive to occupancy level, with 0.75) species, but more than 150 camera sites likely needed for rare (ψ < 0.25) species. Species detection rates were more difficult to estimate precisely at the grid level due to spatial heterogeneity, presumably driven by unaccounted habitat variability factors within the study area. Running a camera at a site for 2 weeks was most efficient for detecting new species, but 3–4 weeks were needed for precise estimates of local detection rate, with no gains in precision observed after 1 month. Metrics for all mammal communities were sensitive to seasonality, with 37%–50% of the species at the sites we examined fluctuating significantly in their occupancy or detection rates over the year. This effect was more pronounced in temperate sites, where seasonally sensitive species varied in relative abundance by an average factor of 4–5, and some species were completely absent in one season due to hibernation or migration. We recommend the following guidelines to efficiently obtain precise estimates of species richness, occupancy and detection rates with camera trap arrays: run each camera for 3–5 weeks across 40–60 sites per array. We recommend comparisons of detection rates be model based and include local covariates to help account for small‐scale variation. Furthermore, comparisons across study areas or times must account for seasonality, which could have strong impacts on mammal communities in both tropical and temperate sites

Leveraging natural history biorepositories as a global, decentralized, pathogen surveillance network

The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) pandemic reveals a major gap in global biosecurity infrastructure: a lack of publicly available biological samples representative across space, time, and taxonomic diversity. The shortfall, in this case for vertebrates, prevents accurate and rapid identification and monitoring of emerging pathogens and their reservoir host(s) and precludes extended investigation of ecological, evolutionary, and environmental associations that lead to human infection or spillover. Natural history museum biorepositories form the backbone of a critically needed, decentralized, global network for zoonotic pathogen surveillance, yet this infrastructure remains marginally developed, underutilized, underfunded, and disconnected from public health initiatives. Proactive detection and mitigation for emerging infectious diseases (EIDs) requires expanded biodiversity infrastructure and training (particularly in biodiverse and lower income countries) and new communication pipelines that connect biorepositories and biomedical communities. To this end, we highlight a novel adaptation of Project ECHO’s virtual community of practice model: Museums and Emerging Pathogens in the Americas (MEPA). MEPA is a virtual network aimed at fostering communication, coordination, and collaborative problem-solving among pathogen researchers, public health officials, and biorepositories in the Americas. MEPA now acts as a model of effective international, interdisciplinary collaboration that can and should be replicated in other biodiversity hotspots. We encourage deposition of wildlife specimens and associated data with public biorepositories, regardless of original collection purpose, and urge biorepositories to embrace new specimen sources, types, and uses to maximize strategic growth and utility for EID research. Taxonomically, geographically, and temporally deep biorepository archives serve as the foundation of a proactive and increasingly predictive approach to zoonotic spillover, risk assessment, and threat mitigation

Expert range maps of global mammal distributions harmonised to three taxonomic authorities

Aim: Comprehensive, global information on species' occurrences is an essential biodiversity variable and central to a range of applications in ecology, evolution, biogeography and conservation. Expert range maps often represent a species' only available distributional information and play an increasing role in conservation assessments and macroecology. We provide global range maps for the native ranges of all extant mammal species harmonised to the taxonomy of the Mammal Diversity Database (MDD) mobilised from two sources, the Handbook of the Mammals of the World (HMW) and the Illustrated Checklist of the Mammals of the World (CMW). Location: Global. Taxon: All extant mammal species. Methods: Range maps were digitally interpreted, georeferenced, error-checked and subsequently taxonomically aligned between the HMW (6253 species), the CMW (6431 species) and the MDD taxonomies (6362 species). Results: Range maps can be evaluated and visualised in an online map browser at Map of Life (mol.org) and accessed for individual or batch download for non-commercial use. Main conclusion: Expert maps of species' global distributions are limited in their spatial detail and temporal specificity, but form a useful basis for broad-scale characterizations and model-based integration with other data. We provide georeferenced range maps for the native ranges of all extant mammal species as shapefiles, with species-level metadata and source information packaged together in geodatabase format. Across the three taxonomic sources our maps entail, there are 1784 taxonomic name differences compared to the maps currently available on the IUCN Red List website. The expert maps provided here are harmonised to the MDD taxonomic authority and linked to a community of online tools that will enable transparent future updates and version control.Fil: Marsh, Charles J.. Yale University; Estados UnidosFil: Sica, Yanina. Yale University; Estados UnidosFil: Burguin, Connor. University of New Mexico; Estados UnidosFil: Dorman, Wendy A.. University of Yale; Estados UnidosFil: Anderson, Robert C.. University of Yale; Estados UnidosFil: del Toro Mijares, Isabel. University of Yale; Estados UnidosFil: Vigneron, Jessica G.. University of Yale; Estados UnidosFil: Barve, Vijay. University Of Florida. Florida Museum Of History; Estados UnidosFil: Dombrowik, Victoria L.. University of Yale; Estados UnidosFil: Duong, Michelle. University of Yale; Estados UnidosFil: Guralnick, Robert. University Of Florida. Florida Museum Of History; Estados UnidosFil: Hart, Julie A.. University of Yale; Estados UnidosFil: Maypole, J. Krish. University of Yale; Estados UnidosFil: McCall, Kira. University of Yale; Estados UnidosFil: Ranipeta, Ajay. University of Yale; Estados UnidosFil: Schuerkmann, Anna. University of Yale; Estados UnidosFil: Torselli, Michael A.. University of Yale; Estados UnidosFil: Lacher, Thomas. Texas A&M University; Estados UnidosFil: Wilson, Don E.. National Museum of Natural History; Estados UnidosFil: Abba, Agustin Manuel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Centro de Estudios Parasitológicos y de Vectores. Universidad Nacional de La Plata. Facultad de Ciencias Naturales y Museo. Centro de Estudios Parasitológicos y de Vectores; ArgentinaFil: Aguirre, Luis F.. Universidad Mayor de San Simón; BoliviaFil: Arroyo Cabrales, Joaquín. Instituto Nacional de Antropología E Historia, Mexico; MéxicoFil: Astúa, Diego. Universidade Federal de Pernambuco; BrasilFil: Baker, Andrew M.. Queensland University of Technology; Australia. Queensland Museum; AustraliaFil: Braulik, Gill. University of St. Andrews; Reino UnidoFil: Braun, Janet K.. Oklahoma State University; Estados UnidosFil: Brito, Jorge. Instituto Nacional de Biodiversidad; EcuadorFil: Busher, Peter E.. Boston University; Estados UnidosFil: Burneo, Santiago F.. Pontificia Universidad Católica del Ecuador; EcuadorFil: Camacho, M. Alejandra. Pontificia Universidad Católica del Ecuador; EcuadorFil: de Almeida Chiquito, Elisandra. Universidade Federal do Espírito Santo; BrasilFil: Cook, Joseph A.. University of New Mexico; Estados UnidosFil: Cuéllar Soto, Erika. Sultan Qaboos University; OmánFil: Davenport, Tim R. B.. Wildlife Conservation Society; TanzaniaFil: Denys, Christiane. Muséum National d'Histoire Naturelle; FranciaFil: Dickman, Christopher R.. The University Of Sydney; AustraliaFil: Eldridge, Mark D. B.. Australian Museum; AustraliaFil: Fernandez Duque, Eduardo. University of Yale; Estados UnidosFil: Francis, Charles M.. Environment And Climate Change Canada; CanadáFil: Frankham, Greta. Australian Museum; AustraliaFil: Freitas, Thales. Universidade Federal do Rio Grande do Sul; BrasilFil: Friend, J. Anthony. Conservation And Attractions; AustraliaFil: Giannini, Norberto Pedro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico - Tucumán. Unidad Ejecutora Lillo; ArgentinaFil: Gursky-Doyen, Sharon. Texas A&M University; Estados UnidosFil: Hackländer, Klaus. Universitat Fur Bodenkultur Wien; AustriaFil: Hawkins, Melissa. National Museum of Natural History; Estados UnidosFil: Helgen, Kristofer M.. Australian Museum; AustraliaFil: Heritage, Steven. University of Duke; Estados UnidosFil: Hinckley, Arlo. Consejo Superior de Investigaciones Científicas. Estación Biológica de Doñana; EspañaFil: Holden, Mary. American Museum of Natural History; Estados UnidosFil: Holekamp, Kay E.. Michigan State University; Estados UnidosFil: Humle, Tatyana. University Of Kent; Reino UnidoFil: Ibáñez Ulargui, Carlos. Consejo Superior de Investigaciones Científicas. Estación Biológica de Doñana; EspañaFil: Jackson, Stephen M.. Australian Museum; AustraliaFil: Janecka, Mary. University of Pittsburgh at Johnstown; Estados Unidos. University of Pittsburgh; Estados UnidosFil: Jenkins, Paula. Natural History Museum; Reino UnidoFil: Juste, Javier. Consejo Superior de Investigaciones Científicas. Estación Biológica de Doñana; EspañaFil: Leite, Yuri L. R.. Universidade Federal do Espírito Santo; BrasilFil: Novaes, Roberto Leonan M.. Universidade Federal do Rio de Janeiro; BrasilFil: Lim, Burton K.. Royal Ontario Museum; CanadáFil: Maisels, Fiona G.. Wildlife Conservation Society; Estados UnidosFil: Mares, Michael A.. Oklahoma State University; Estados UnidosFil: Marsh, Helene. James Cook University; AustraliaFil: Mattioli, Stefano. Università degli Studi di Siena; ItaliaFil: Morton, F. Blake. University of Hull; Reino UnidoFil: Ojeda, Agustina Alejandra. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto Argentino de Investigaciones de las Zonas Áridas. Provincia de Mendoza. Instituto Argentino de Investigaciones de las Zonas Áridas. Universidad Nacional de Cuyo. Instituto Argentino de Investigaciones de las Zonas Áridas; ArgentinaFil: Ordóñez Garza, Nicté. Instituto Nacional de Biodiversidad; EcuadorFil: Pardiñas, Ulises Francisco J.. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Centro Nacional Patagónico. Instituto de Diversidad y Evolución Austral; ArgentinaFil: Pavan, Mariana. Universidade de Sao Paulo; BrasilFil: Riley, Erin P.. San Diego State University; Estados UnidosFil: Rubenstein, Daniel I.. University of Princeton; Estados UnidosFil: Ruelas, Dennisse. Museo de Historia Natural, Lima; PerúFil: Schai-Braun, Stéphanie. Universitat Fur Bodenkultur Wien; AustriaFil: Schank, Cody J.. University of Texas at Austin; Estados UnidosFil: Shenbrot, Georgy. Ben Gurion University of the Negev; IsraelFil: Solari, Sergio. Universidad de Antioquia; ColombiaFil: Superina, Mariella. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto de Medicina y Biología Experimental de Cuyo; ArgentinaFil: Tsang, Susan. American Museum of Natural History; Estados UnidosFil: Van Cakenberghe, Victor. Universiteit Antwerp; BélgicaFil: Veron, Geraldine. Université Pierre et Marie Curie; FranciaFil: Wallis, Janette. Kasokwa-kityedo Forest Project; UgandaFil: Whittaker, Danielle. Michigan State University; Estados UnidosFil: Wells, Rod. Flinders University.; AustraliaFil: Wittemyer, George. State University of Colorado - Fort Collins; Estados UnidosFil: Woinarski, John. Charles Darwin University; AustraliaFil: Upham, Nathan S.. University of Yale; Estados UnidosFil: Jetz, Walter. University of Yale; Estados Unido

Expert range maps of global mammal distributions harmonised to three taxonomic authorities

AimComprehensive, global information on species' occurrences is an essential biodiversity variable and central to a range of applications in ecology, evolution, biogeography and conservation. Expert range maps often represent a species' only available distributional information and play an increasing role in conservation assessments and macroecology. We provide global range maps for the native ranges of all extant mammal species harmonised to the taxonomy of the Mammal Diversity Database (MDD) mobilised from two sources, the Handbook of the Mammals of the World (HMW) and the Illustrated Checklist of the Mammals of the World (CMW).LocationGlobal.TaxonAll extant mammal species.MethodsRange maps were digitally interpreted, georeferenced, error-checked and subsequently taxonomically aligned between the HMW (6253 species), the CMW (6431 species) and the MDD taxonomies (6362 species).ResultsRange maps can be evaluated and visualised in an online map browser at Map of Life (mol.org) and accessed for individual or batch download for non-commercial use.Main conclusionExpert maps of species' global distributions are limited in their spatial detail and temporal specificity, but form a useful basis for broad-scale characterizations and model-based integration with other data. We provide georeferenced range maps for the native ranges of all extant mammal species as shapefiles, with species-level metadata and source information packaged together in geodatabase format. Across the three taxonomic sources our maps entail, there are 1784 taxonomic name differences compared to the maps currently available on the IUCN Red List website. The expert maps provided here are harmonised to the MDD taxonomic authority and linked to a community of online tools that will enable transparent future updates and version control

Zonas prioritarias de investigación en base a análisis de sesgos de muestreo en la colección de mamíferos de QCAZ

La colección de mamíferos del QCAZ presenta sesgos relacionados con el esfuerzo de muestreo que indican una tendencia a realizar colecciones en zonas de fácil acceso. Se encontraron diferencias altamente significativas entre la cantidad de registros colectados en el Sistema Nacional de Áreas Protegidas y fuera de éste. Además, 20 de las 35 áreas del SNAP carecen de colecciones. Tomando en cuenta estos sesgos, puede ser difícil, e incluso erróneo, obtener estimaciones de biodiversidad reales y asignar áreas prioritarias de conservación. La proyección e interpolación de los puntos de recolección en mapas generados por SIG permitió mostrar vacíos en las colecciones, de manera que además de las áreas del SNAP, se proponen zonas prioritarias de colecció

A taxonomic revision of the Yasuni Round-eared bat, Lophostoma yasuni (Chiroptera: Phyllostomidae)

Camacho, M. Alejandra, Chávez, Daniel, Burneo, Santiago F. (2016): A taxonomic revision of the Yasuni Round-eared bat, Lophostoma yasuni (Chiroptera: Phyllostomidae). Zootaxa 4114 (3): 246-260, DOI: http://doi.org/10.11646/zootaxa.4114.3.

Lophostoma carrikeri Allen 1910

&lt;i&gt;Lophostoma carrikeri&lt;/i&gt; (Allen, 1910) &lt;p&gt;Carriker&rsquo;s Round-eared Bat Figure 4&lt;/p&gt; &lt;p&gt; &lt;i&gt;Chrotopterus carrikeri&lt;/i&gt; Allen, 1910:147; type locality &ldquo;Rio Mocho&rdquo; Bol&iacute;var, Venezuela. &lt;i&gt;Tonatia carrikeri&lt;/i&gt; Goodwin, 1942:207; name combination.&lt;/p&gt; &lt;p&gt; &lt;i&gt;Lophostoma carrikeri&lt;/i&gt;: Lee, Hoofer, and Van Den Bussche, 2002:55; first use of current name combination.&lt;/p&gt; &lt;p&gt; &lt;i&gt;Lophostoma yasuni&lt;/i&gt; Fonseca and Pinto, 2004:1; type locality &ldquo;vicinity of the Yasun&iacute; Research Station (00&deg;30&rsquo;S, 75&deg;55&rsquo;W, 220 m), Yasun&iacute; National Park and Biosphere Reserve, Province of Orellana, Ecuador.&rdquo;&lt;/p&gt; &lt;p&gt; &lt;i&gt;L&lt;/i&gt; [&lt;i&gt;ophostoma&lt;/i&gt;]. &lt;i&gt;yasuni&lt;/i&gt;: Tirira, 2007: 278 name combination.&lt;/p&gt; &lt;p&gt; &lt;b&gt;Distribution.&lt;/b&gt; &lt;i&gt;Lophostoma carrikeri&lt;/i&gt; is restricted to South America, known from Brazil, French Guiana, Suriname, Guyana, Venezuela, Colombia, Ecuador, Peru, and Bolivia (Figure 5).&lt;/p&gt; &lt;p&gt; &lt;b&gt;Emended diagnosis.&lt;/b&gt; &lt;i&gt;Lophostoma carrikeri&lt;/i&gt; is a medium size round-eared bat (FA 42.2&ndash;47.7 mm, GLS 23.0&ndash; 26.6 mm; CCL 19.0&ndash; 21.3 mm). &lt;i&gt;L. carrikeri&lt;/i&gt; is larger than &lt;i&gt;L. brasiliense&lt;/i&gt; and &lt;i&gt;L. schulzi&lt;/i&gt;, but smaller than &lt;i&gt;L. evotis&lt;/i&gt;, &lt;i&gt;L. occidentalis&lt;/i&gt;, and &lt;i&gt;L. silvicolum&lt;/i&gt;. All measurements overlap with those of &lt;i&gt;L. kalkoae&lt;/i&gt; and &lt;i&gt;L. schulzi&lt;/i&gt; (Table 2). Craniodental measurements including variation from recently recorded specimens are presented in Table 5.&lt;/p&gt; &lt;p&gt; &lt;i&gt;L. carrikeri&lt;/i&gt; is easily identified by its plain white ventral fur from the throat through the abdomen, bordered along the flanks by the gray-brown dorsal fur (Figure 4). Dorsal pelage is long and tricolored, with pale to whitish tips. Nose-leaf, chin, and base of the ears are blackish brown. These characters are shared with &lt;i&gt;L. kalkoae&lt;/i&gt; although the latter has dark brown gular fur, whereas in &lt;i&gt;L. carrikeri&lt;/i&gt; this region is pale to whitish. &lt;i&gt;L. carrikeri&lt;/i&gt; lacks the white to pale post-auricular patches connected to the chest by a band of pale hairs present in &lt;i&gt;L. kalkoae&lt;/i&gt;, &lt;i&gt;L. occidentalis&lt;/i&gt;, and &lt;i&gt;L. evotis&lt;/i&gt;. Ears are light brown to blackish with or without a whitish narrow margin. Proximal third of the dorsal surface of the forearm is sparsely haired; ventrally, forearm and adjacent membrane covered with short grayish hairs.&lt;/p&gt; &lt;p&gt; Skull length ranges from 23.0 to 26.6 mm (Table 5). The skull is constricted postorbitally and is slightly concave in the orbital region; sagittal crests may vary, from well-developed in adult males to moderately developed or absent in females and young males (Allen 1910; Goodwin 1942; McCarthy &lt;i&gt;et al.&lt;/i&gt; 1992). Lateral development of the mastoid region is moderate. Short palatal length with posterior margin aligned with second molars. Upper medial incisors well developed and convergent. Shallow indentation on the lingual cingulum of the upper canine. P3 well developed. Posterior lingual cusp on P4 cingulum is weakly developed. M1 and M2 parastyles are absent. Lingual cingulum on both M1 and M2 is also absent. p3 well developed and in line with toothrow.&lt;/p&gt; &lt;p&gt; &lt;b&gt;Comparisons.&lt;/b&gt; Relative to other &lt;i&gt;Lophostoma&lt;/i&gt; species, &lt;i&gt;L. carrikeri&lt;/i&gt; and &lt;i&gt;L. kalkoae&lt;/i&gt; can be easily distinguished by the presence of pure white fur from the throat to the lower abdomen, bordered on the sides of the body by brown hair. These species are not known to occur in sympatry, but comparisons are made in case white-bellied &lt;i&gt;Lophostoma&lt;/i&gt; are discovered on the western versant of the Andes in South America.Molecular evidence shows that many Neotropical bats from Central America appear more closely related to samples from the western versant of the Andes than to those from the eastern versant (Patterson &lt;i&gt;et al.&lt;/i&gt; 1992; Hoffmann &amp; Baker 2001, 2003; Hoffmann &lt;i&gt;et al.&lt;/i&gt; 2003; Fonseca &lt;i&gt;et al.&lt;/i&gt; 2007; Larsen &lt;i&gt;et al.&lt;/i&gt; 2007).&lt;/p&gt; &lt;p&gt; Despite the fact that all linear measurements of &lt;i&gt;L. kalkoae&lt;/i&gt; overlap with those of &lt;i&gt;L. carrikeri&lt;/i&gt;, some features differentiate these species. Gular fur is dark brown in &lt;i&gt;L. kalkoae&lt;/i&gt; but pale to whitish in &lt;i&gt;L. carrikeri&lt;/i&gt;. &lt;i&gt;L. kalkoae&lt;/i&gt; has pale gray post-auricular patches, absent in &lt;i&gt;L. carrikeri.&lt;/i&gt; Dorsal surface of the proximal third of the forearm in &lt;i&gt;L. carrikeri&lt;/i&gt; is sparsely covered with hair, and rather naked in &lt;i&gt;L. kalkoae&lt;/i&gt;.&lt;/p&gt; &lt;p&gt; In most features, the skull of &lt;i&gt;L. carrikeri&lt;/i&gt; resembles that of &lt;i&gt;L. kalkoae.&lt;/i&gt; Both species show slender rostra with a postorbital constriction. Lateral mastoid process is moderately developed in &lt;i&gt;L. carrikeri&lt;/i&gt;, but less developed in &lt;i&gt;L. kalkoae&lt;/i&gt;. Being larger, &lt;i&gt;L. carrikeri&lt;/i&gt; may show more robust rostra and well developed lateral mastoid processes than &lt;i&gt;L. kalkoae&lt;/i&gt;. &lt;i&gt;L. carrikeri&lt;/i&gt; presents smaller incisors than &lt;i&gt;L. kalkoae&lt;/i&gt;, resembling them in shape and direction. &lt;i&gt;L. carrikeri&lt;/i&gt; shows a weak indentation on the lingual cingulum of the upper canine, deeply marked in &lt;i&gt;L. kalkoae&lt;/i&gt;. P3 is short and less developed in &lt;i&gt;L. carrikeri&lt;/i&gt;, whereas tall and well developed in &lt;i&gt;L. kalkoae&lt;/i&gt;. Posterior lingual cusp on the cingulum of P4 less developed in &lt;i&gt;L. carrikeri&lt;/i&gt;. &lt;i&gt;L. carrikeri&lt;/i&gt; lacks a lingual cingulum in M1, unlike &lt;i&gt;L. kalkoae&lt;/i&gt;. Other dental characters are equivalent in size and shape with those of &lt;i&gt;L. kalkoae&lt;/i&gt;.&lt;/p&gt; &lt;p&gt; &lt;b&gt;Natural history.&lt;/b&gt; No amendments are needed regarding the natural history of &lt;i&gt;L. carrikeri&lt;/i&gt;. This species has been associated with mesic and riparian forests in lowlands (McCarthy &amp; Handley Jr 1988) and has been captured in Igap&oacute;, Varzea, semideciduous savanna, and dry forest in the Amazon Basin (McCarthy &amp; Handley Jr 1988; Gribel &amp; Taddei 1989; Bernard &amp; Fenton 2002; Sampaio &lt;i&gt;et al.&lt;/i&gt; 2003; Castro-Arellano &lt;i&gt;et al.&lt;/i&gt; 2007; Gregorin &lt;i&gt;et al.&lt;/i&gt; 2008). Also, Zort&eacute;a &lt;i&gt;et al.&lt;/i&gt; (2009) reported &lt;i&gt;L. carrikeri&lt;/i&gt; in a transitional locality between a semideciduous forest and a riparian forest in the Cerrado Brazil. Apparently, the species prefers undisturbed forests. In Ecuador, &lt;i&gt;L. carrikeri&lt;/i&gt; has been captured in primary terra firme forests with an understory of mature woody and herbaceous vegetation (Camacho &lt;i&gt;et al.&lt;/i&gt; 2014) in the Yasuni National Park, as well as in primary and secondary tropical rainforest with an understory mainly of immature woody and herbaceous vegetation (Fonseca &amp; Pinto 2004).&lt;/p&gt; &lt;p&gt; &lt;i&gt;L. carrikeri&lt;/i&gt; may prefer hollowed termite nests as reported by Allen (1911) and McCarthy &lt;i&gt;et al.&lt;/i&gt; (1983). Specimens from Ecuador were captured in ground level mist nets. Neither new behavioral traits nor diet information is available. Three records of ectoparasitic arthropods were collected from a specimen of &lt;i&gt;Lophostoma carrikeri&lt;/i&gt; in Yasuni National Park, Ecuador, the first report of this species from Ecuador (Camacho &lt;i&gt;et al.&lt;/i&gt; 2014): &lt;i&gt;Stizostrebla longirostris&lt;/i&gt; Jobling, 1939; &lt;i&gt;Pseudostrebla sparsisetis&lt;/i&gt; Wenzel, 1976; and &lt;i&gt;Mastoptera&lt;/i&gt; sp. Two of these, &lt;i&gt;Stizostrebla longirostris&lt;/i&gt; and &lt;i&gt;Pseudostrebla sparsisetis&lt;/i&gt;, are exclusively known to parasitize &lt;i&gt;Lophostoma carrikeri.&lt;/i&gt;&lt;/p&gt;Published as part of &lt;i&gt;Camacho, M. Alejandra, Chávez, Daniel &amp; Burneo, Santiago F., 2016, A taxonomic revision of the Yasuni Round-eared bat, Lophostoma yasuni (Chiroptera: Phyllostomidae), pp. 246-260 in Zootaxa 4114 (3)&lt;/i&gt; on pages 254-258, DOI: 10.11646/zootaxa.4114.3.2, &lt;a href="http://zenodo.org/record/262170"&gt;http://zenodo.org/record/262170&lt;/a&gt