Modeling Myotis Management: Saving Our Wildlife and Ecosystems

Shreya Charndrasekhar and Preethila Zaman | SQ Vol. 20 Research Features (2022-2023)

Although you would not guess from their fuzzy, fanged appearance alone, bats play an important role in the maintenance of a variety of environments, including natural and human-managed ecosystems. For example, bats provide the United States agricultural industry $22.9 billion per year in pest control: their diet of primarily insects helps protect crops and control the population of potential disease-carrying vectors like mosquitos, bolstering public health.1 Unfortunately, human activities that induce habitat loss, degradation, and contamination are threatening bat species around the world.2 This ecological harm, known as anthropogenic disturbance, necessitates developing strategies to monitor bat populations and improve conservation efforts.

Developing strategies to monitor bat populations is challenging due to their small size and nocturnal habits. To address these issues, current research focuses on developing models that allow wildlife managers to anticipate the presence of bat species at particular locations. These models, known as predictive occurrence models, are created using known ecological factors like prey abundance and plant growth that influence the distribution of particular species, and can be used to improve species monitoring and conservation efforts. Tazlina Dentinger, Richard Klein, Brandi Sanchez, and Ari Brisco Schofield are undergraduates at the University of California that worked to improve such models for bats. 

The research team consisted of individuals with diverse skills and a passion for ecological conservation. Schofield is an ecology major at UC San Diego with experience working with nonprofits in wildlife management with the San Diego Habitat Conservancy. When asked about how the study was developed, Schofield remarked, “I think bats are cool, and we had the equipment to study them. While we already knew how vital their work is [ecologically], we didn’t understand the immense monetary value they have for humans [before embarking on this study].” The team hopes to contribute to science education, and conduct research that will form the basis of conservation efforts and inform policy, propelling these researchers to improving predictive occurrence models for bats.

To develop predictive occurrence models for a particular organism, it is important to determine its classification to help focus monitoring efforts. There are seven levels of organism classification in modern biology. From smallest to largest, they are as follows: species, genus, family, and order.3 This study’s research team was interested in the ecology and behavior of bat species in the genus Myotis. More commonly known as mouse-eared bats, this genus contains approximately 90 unique species.4 Myotis species are crucial to study due to their ecological diversity and abundance. Moreover, because of their widespread geographical distribution, they have a large contribution to the ecological services that bats provide. The focus of this study, Yuma myotis, are most commonly found in riverine areas.

One ecological factor that predictive occurrence models may utilize is the prey distribution for the species of interest. Myotis species are relatively small, and their diets are composed of a variety of insects. Like most bat species, Myotis use echolocation to navigate their environment and forage for insects: after emitting calls to the environment, bats listen for echos that return from nearby insects to locate them.5 There are also various foraging strategies used by Myotis species that are specific to their location, structure, and diet. For example, the Yuma myotis (Myotis yumanensis) primarily trawl, or fly a few centimeters over the surface of the water and catch insects with their hind feet.6

Knowledge of Yuma myotis’ foraging locations, behaviors, and diet are crucial to developing predictive occurrence models as they will help identify their whereabouts. To better understand these factors, researchers sought to determine if Yuma myotis populations vary in response to aerial insect availability and stream width. They hypothesized there will be a positive correlation between bat calls and the abundance of aerial insects, as well as between bat calls and areas with wider stream widths, since Yuma myotis would be more likely to forage in areas that have high densities of prey and have more space for trawling.

To test this hypothesis, Sanchez and Klein, who have a particular passion for ecological fieldwork, proposed that the team conduct a study in the riverine habitats at the Landels-Hill Big Creek Reserve, Big Sur, CA, USA. Schofield described the long hours the team spent together in the field as a thrilling experience. Big Creek Reserve has one of the sharpest elevation changes from coast to inland and, with beautiful views of a variety of habitats all in one place. To the right was a view of the coast, and to the left were hills and valleys where ethereal redwoods towered undisturbed. Their studies were conducted by the creeks at the bottom of the reserve. As the team learned to listen for bat calls they also interacted with insects and other wildlife in the area. As Klein has interests in animal behavior and wildlife ecology, he was quite active during this component of research. The lush reserve hosts a great diversity of plant communities and brush that grow alongside the streams that provide foraging habitats for various insectivorous bat species, and the riverine areas were crawling with insects that are common prey for Yuma myotis. 

The research team observed five riverine sites across the reserve, each with variable ecological characteristics to investigate how Yuma myotis habitat use varies with the habitat characteristics. At each site, three parameters were recorded: width of the stream, number of bat calls, and the number of aerial insects present from each order. Bat calls, an indicator of the abundance of Yuma myotis foraging at each site, were recorded and identified. To identify the presence of a correlation between the bats and specific prey, researchers recorded the numbers of aerial insects in each biological order present at each site. In particular, insects from the orders Diptera, Ephemeroptera, Trichoptera, Lepidoptera, and Coleoptera were studied.

To examine the distribution of aerial insect prey at each of the habitat sites in the area, researchers set up a quadrat near the edge of the stream. A quadrat is a set grid-like system of squares placed in a habitat of interest to record and study the abundance of an organism. Since these insects are known to be attracted to light, researchers first shone white and ultraviolet light on the sheet to allow insects to gather for 20 minutes. Following, the researchers counted the insects for 5 minutes and identified the orders they belonged to, then turned off the light for 5 minutes, and then brushed the insects off the sheet to let them scatter. This process was carried out four times each night during the two-hour observation window, creating four half-hour time intervals. The process of insect analysis took a couple of tries to perfect and a lot of collaboration and patience was required for the procedure, however, it was a great learning and team-bonding experience.

Throughout the duration of the study, researchers recorded a total of 1,639 bat calls, of which 812 calls were automatically classified by the Echo Meter Touch 2 Bat Detector to their respective species. The remainder of the calls were manually identified by the researchers, to the furthest classification level possible. In total, 1,620 of the calls were identified as belonging to various species within the Myotis genus. Of the calls that the application automatically identified to the species level, approximately 80% of the 812 calls belonged to Yuma myotises. Dentinger, who has a particular interest in statistical analysis, was crucial in assisting the team to analyze the relationships between the calls and ecological factors, primarily using linear regressions and t-tests.

Researchers found that the number of Myotis calls increased with the number of aerial insects. Myotis calls were highly correlated with the overall abundance of aerial insects found in the same period. A marginal difference was observed between the sites for the number of myotis calls and the number of aerial insects present, indicating that prey availability may be used ubiquitously in developing predictive occurrence models for Myotis species. 

Their results also showed there was a positive correlation between calls and dipteran abundance, but not between calls and lepidopteran abundance despite there being more lepidopterans overall. While this may indicate Yuma myotis have a general dietary preference for dipterans over lepidopterans, this trend may also be related to seasonal dietary preferences that shift throughout the year as availability of prey changes. To control for these factors, the researchers suggest that future studies include more sites across a larger area and over multiple seasons.

In contrast, there was no observed relationship between stream width and total abundance of aerial insects and stream width and total abundance of Myotis calls. This suggests that Myotis prey are not particularly guided by the physical features of the stream present in the habitat, and that direct Myotis habitat use may more likely be guided by the availability of prey rather than by habitat features. 

The relationship between Myotis bat calls and aerial insect abundance promises a potential strategy to indirectly measure locations where bats are likely to forage and target specific areas for more direct bat population monitoring. This contribution to developing bat predictive occurrence models can better equip wildlife experts in their land management and conservation efforts, which are more necessary than ever as anthropogenic activities continue to put stress on bat populations. Moreover, it is critical to realize that better monitoring of bats not only improves their population outcomes but human outcomes as well: with healthier bat populations, more pest control can be provided,  translating to improved agricultural yields and ultimately, bolstered public health. 

Regarding their research, the team hopes that if they can show how valuable bats are economically, humans will be incentivized to develop better conservation policies and change their ways. Beyond change at the policy-level, they also hope their research can induce change on a personal level. Shofield discussed in an interview that “unfortunately, bats get a bad reputation,” elaborating that many people have negative views on bats because of their role as vectors of viruses like rabies and more recently, COVID-19.7 However, with more research and education, they hope people can develop a more informed image of bats so that we interact with them in a healthier, kinder way. Above all, they hope a heightened awareness of how interconnected ecosystems are can create a more caring, thoughtful, and compassionate attitude on a large scale that will inform the way we navigate our shared, precious world.

 

References

  1. Brigham, R. & Kalko, Elizabeth & Jones, Gareth & Parsons, Stuart & Limpens, Herman. (2004). Bat Echolocation Research. Tools, Techniques, and Analysis.
  2. R. M. Brigham and others, Variation in Habitat Use and Prey Selection by Yuma Bats, Myotis yumanensis, Journal of Mammalogy, Volume 73, Issue 3, 21 August 1992, Pages 640–645, https://doi.org/10.2307/1382036
  3. Encyclopædia Britannica, inc. (n.d.). biological classification. Encyclopædia Britannica. https://kids.britannica.com/students/article/biological-classification/611149
  4. Fenton, M & Bogdanowicz, Wieslaw. (2011). Relationships between external morphology and foraging behaviour: Bats in the genus Myotis. Canadian Journal of Zoology. 80. 1004-1013. 10.1139/z02-083.
  5. U.S. Department of the Interior. (n.d.). Echolocation. National Parks Service. https://www.nps.gov/subjects/bats/echolocation.htm
  6. Katherine M. Gorman, Elaine L. Barr, Lindsay Ries, Tomás Nocera, W. Mark Ford. (2021). Bat activity patterns relative to temporal and weather effects in a temperate coastal environment, Global Ecology and Conservation, Volume 30, October 2021, https://doi.org/10.1016/j.gecco.2021.e01769.
  7. Lu M, Wang X, Ye H, Wang H, Qiu S, Zhang H, Liu Y, Luo J, Feng J. Does public fear that bats spread COVID-19 jeopardize bat conservation? Biol Conserv. February 2021. https://doi.org/10.1016%2Fj.biocon.2021.108952 

 

Sources

  1. https://www.researchgate.net/publication/281210205_Bat_Echolocation_Research_Tools_Techniques_and_Analysis
  2. https://academic.oup.com/jmammal/article-abstract/73/3/640/871561  
  3. https://kids.britannica.com/students/article/biological-classification/611149 
  4. https://www.researchgate.net/publication/237972220_Relationships_between_external_morphology_and_foraging_behaviour_Bats_in_the_genus_Myotis 
  5. https://www.nps.gov/subjects/bats/echolocation.htm 
  6. https://www.sciencedirect.com/science/article/pii/S235198942100319X 
  7. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7837179/