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Abstract BackgroundKangaroo rats are small mammals that are among the most abundant vertebrates in many terrestrial ecosystems in Western North America and are considered both keystone species and ecosystem engineers, providing numerous linkages between other species as both consumers and resources. However, there are challenges to studying the behavior and activity of these species due to the difficulty of observing large numbers of individuals that are small, secretive, and nocturnal. Our goal was to develop an integrated approach of miniaturized animal-borne accelerometry and radiotelemetry to classify the cryptic behavior and activity cycles of kangaroo rats and test hypotheses of how their behavior is influenced by light cycles, moonlight, and weather. MethodsWe provide a proof-of-concept approach to effectively quantify behavioral patterns of small bodied (< 50 g), nocturnal, and terrestrial free-ranging mammals using large acceleration datasets by combining low-mass, miniaturized animal-borne accelerometers with radiotelemetry and advanced machine learning techniques. We developed a method of attachment and retrieval for deploying accelerometers, a non-disruptive method of gathering observational validation datasets for acceleration data on free-ranging nocturnal small mammals, and used these techniques on Merriam’s kangaroo rats to analyze how behavioral patterns relate to abiotic factors. ResultsWe found that Merriam’s kangaroo rats are only active during the nighttime phases of the diel cycle and are particularly active during later light phases of the night (i.e., late night, morning twilight, and dawn). We found no reduction in activity or foraging associated with moonlight, indicating that kangaroo rats are actually more lunarphilic than lunarphobic. We also found that kangaroo rats increased foraging effort on more humid nights, most likely as a mechanism to avoid cutaneous water loss. ConclusionsSmall mammals are often integral to ecosystem functionality, as many of these species are highly abundant ecosystem engineers driving linkages in energy flow and nutrient transfer across trophic levels. Our work represents the first continuous detailed quantitative description of fine-scale behavioral activity budgets in kangaroo rats, and lays out a general framework for how to use miniaturized biologging devices on small and nocturnal mammals to examine behavioral responses to environmental factors. 

Abstract Predation is a fundamental selective pressure on animal morphology, as morphology is directly linked with physical performance and evasion. Bipedal heteromyid rodents, which are characterized by unique morphological traits such as enlarged hindlimbs, appear to be more successful than sympatric quadrupedal rodents at escaping predators such as snakes and owls, but no studies have directly compared the escape performance of bipedal and quadrupedal rodents. We used simulated predator attacks to compare the evasive jumping ability of bipedal kangaroo rats (Dipodomys) to that of three quadrupedal rodent groups—pocket mice (Chaetodipus), woodrats (Neotoma), and ground squirrels (Otospermophilus). Jumping performance of pocket mice was remarkably similar to that of kangaroo rats, which may be driven by their shared anatomical features (such as enlarged hindlimb muscles) and facilitated by their relatively small body size. Woodrats and ground squirrels, in contrast, almost never jumped as a startle response, and they took longer to perform evasive escape maneuvers than the heteromyid species (kangaroo rats and pocket mice). Among the heteromyids, take‐off velocity was the only jump performance metric that differed significantly between species. These results support the idea that bipedal body plans facilitate vertical leaping in larger‐bodied rodents as a means of predator escape and that vertical leaping likely translates to better evasion success. 

Abstract Predators must contend with numerous challenges to successfully find and subjugate prey. Complex traits related to hunting are partially controlled by a large number of co‐evolved genes, which may be disrupted in hybrids. Accordingly, research on the feeding ecology of animals in hybrid zones has shown that hybrids sometimes exhibit transgressive or novel behaviors, yet for many taxa, empirical studies of predation and diet across hybrid zones are lacking. We undertook the first such field study for a hybrid zone between two snake species, the Mojave rattlesnake (Crotalus scutulatus) and the prairie rattlesnake (Crotalus viridis). Specifically, we leveraged established field methods to quantify the hunting behaviors of animals, their prey communities, and the diet of individuals across the hybrid zone in southwestern New Mexico, USA. We found that, even though hybrids had significantly lower body condition indices than snakes from either parental group, hybrids were generally similar to non‐hybrids in hunting behavior, prey encounter rates, and predatory attack and success. We also found that, compared toC. scutulatus,C. viridiswas significantly more active while hunting at night and abandoned ambush sites earlier in the morning, and hybrids tended to be moreviridis‐like in this respect. Prey availability was similar across the study sites, including within the hybrid zone, with kangaroo rats (Dipodomysspp.) as the most common small mammal, both in habitat surveys and the frequency of encounters with hunting rattlesnakes. Analysis of prey remains in stomachs and feces also showed broad similarity in diets, with all snakes preying primarily on small mammals and secondarily on lizards. Taken together, our results suggest that the significantly lower body condition of hybrids does not appear to be driven by differences in their hunting behavior or diet and may instead relate to metabolic efficiency or other physiological traits we have not yet identified. 

Abstract Emerging infectious diseases are increasingly recognized as a significant threat to global biodiversity conservation. Elucidating the relationship between pathogens and the host microbiome could lead to novel approaches for mitigating disease impacts. Pathogens can alter the host microbiome by inducing dysbiosis, an ecological state characterized by a reduction in bacterial alpha diversity, an increase in pathobionts, or a shift in beta diversity. We used the snake fungal disease (SFD; ophidiomycosis), system to examine how an emerging pathogen may induce dysbiosis across two experimental scales. We used quantitative polymerase chain reaction, bacterial amplicon sequencing, and a deep learning neural network to characterize the skin microbiome of free‐ranging snakes across a broad phylogenetic and spatial extent. Habitat suitability models were used to find variables associated with fungal presence on the landscape. We also conducted a laboratory study of northern watersnakes to examine temporal changes in the skin microbiome following inoculation withOphidiomyces ophidiicola. Patterns characteristic of dysbiosis were found at both scales, as were nonlinear changes in alpha and alterations in beta diversity, although structural‐level and dispersion changes differed between field and laboratory contexts. The neural network was far more accurate (99.8% positive predictive value [PPV]) in predicting disease state than other analytic techniques (36.4% PPV). The genusPseudomonaswas characteristic of disease‐negative microbiomes, whereas, positive snakes were characterized by the pathobiontsChryseobacterium,Paracoccus, andSphingobacterium. Geographic regions suitable forO. ophidiicolahad high pathogen loads (>0.66 maximum sensitivity + specificity). We found that pathogen‐induced dysbiosis of the microbiome followed predictable trends, that disease state could be classified with neural network analyses, and that habitat suitability models predicted habitat for the SFD pathogen.