Spatial Analysis of Socioeconomic Factors at Found Locations of Orphaned Mammals in Champaign County

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Spatial Analysis of Socioeconomic Factors at Found Locations of Orphaned Mammals in Champaign County

https://doi.org/10.21203/rs.3.rs-2472281/v1

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Young mammals make up a significant portion of animals brought into wildlife rehabilitation centers. Unfortunately, some of these “orphaned” individuals are inappropriately removed from their native habitat. As such, there is a need for educational community outreach centered on the proper identification of orphaned wildlife that benefit from human intervention, while maximizing limited resources of rehabilitation facilities. This study evaluated found locations of common mammalian orphans presenting from Champaign County to the University of Illinois Wildlife Medical Clinic (WMC) between 2015-2020. The found locations of the orphans (99 Virginia opossums, 80 common raccoons, 441 eastern gray squirrels, 602 eastern cottontails) were mapped using Geographic Information System (GIS) and overlaid with maps of human population density, income, and education level for analysis. The four species were most commonly found in areas with high population density (p < 0.001) and middle family income (p < 0.001). Opossums, squirrels, and cottontails were found more frequently in areas of high education levels (p < 0.001), while there was no significant difference in education level for found raccoons.

common raccoon

eastern cottontail

eastern gray squirrel

GIS

socioeconomic

Virginia opossum

Neonatal animals feature heavily in admissions to wildlife rehabilitation centers. Orphaned young animals typically make up the largest proportion of cases seen by individual facilities (Long et al. 2020, Mullineaux 2014). Some of these animals are misidentified as orphans and mistakenly taken from their mothers, which is a common problem among wildlife care facilities (Tseng 2011, Found an Animal… c2020, Found an Orphaned… c2021, Mass.gov c2021). This creates an issue for both the animals brought in and the facilities housing them. Healthy, young wildlife receive more optimal care with their mothers than they would under human care, and these animals can miss out on learning experiences needed to survive in the wild, such as forming social groups and successfully avoiding predators (Guy et al. 2013, Robertson and Harris 1995). Additionally, wildlife clinics tend to have limited funds, so there is a need to reduce the intake of unnecessary patients to adequately allocate resources to animals truly in need (Santos et al. 2018, Orphaned and… c2021, Sleeman 2008).

These issues highlight an opportunity for conservation education centered around the proper identification of orphaned mammals that require professional intervention. In this study, we elected to focus on orphaned mammals and identify the areas in Champaign County where these animals were most frequently found and subsequently brought into the University of Illinois Wildlife Medical Clinic (WMC). We then aimed to compare the orphans’ found locations with socioeconomic factors to characterize the human population that most often brings these animals to the WMC. This comparison will help identify areas to target for outreach and the background of the people most likely to bring in orphaned mammals.

The most abundant mammalian species brought into the WMC are the eastern cottontail (Sylvilagus floridanus, SYFL), eastern gray squirrel (Sciurus carolinensis, SCCA), Virginia opossum (Didelphis virginiana, DIVI), and common raccoon (Procyon lotor, PRLO). These four species were chosen as a representative sample of orphaned mammals brought to the WMC from Champaign County, Illinois. We made predictions for the human socioeconomic factors most associated with where the animals were most commonly found based on their natural history.

Both the common raccoon and the Virginia opossum are omnivorous, opportunistic species with broad dietary and habitat requirements (Chapman and Feldhamer 1982, McKinney 2002). These species can often be found in edge habitats with a mixture of farmland and woodland, but they are less common in grasslands or agricultural areas due to the reduction in trees and sources of shelter (Mammals n.d.). Raccoons and opossums are generalist mesopredators that respond positively to anthropogenic resources (McKinney 2002, Prange and Gehrt 2004). Urban raccoons and opossums have adapted their diets to take advantage of newly available foods (Outdoor Illinois… 2019). Both species are tolerant of fragmentation and human presence and, therefore, can commonly be found in urban and suburban areas (Crooks 2002, Mammals n.d.). Raccoons specifically have been found to reach higher densities in urban areas than rural areas (Hoffmann and Gottschang 1977, Rosatte et al. 1991, Prange and Gehrt 2004). Thus, it was expected that both opossums and raccoons would be more commonly found in areas of high human population in Champaign County because they are adapted to the human population, can successfully use resources available in human areas, and tend to be more abundant in urban habitats.

The population of eastern gray squirrels in Illinois has decreased due to the loss of mature wooded habitats, but they are still abundant throughout the state (Mammals n.d.). Gray squirrels are typically found in dense woods, but they have adapted to urban areas and are also frequently cited as nuisances in urban situations (Chapman and Feldhamer 1982, Illinois Natural… c2021). Although they can be found in both rural and urban areas, landscapes in Illinois must be at least 20 percent forested to possess gray squirrels (Nixon et al. 1978). In previous studies, gray squirrels were most abundant in areas with extensive canopy cover (Bonnington et al. 2013, Williamson 1983). The greater canopy cover allows increased protection from predators while the seed-bearing tree species provide an important food source (Williamson 1983). Moreover, a study looking at radio tracking of gray squirrels in an urban forest found that they consistently avoided developed and open grassy portions of the habitat (Tounzen et al. 2012).

Eastern cottontails inhabit a wide variety of diverse locations including disturbed, successional, and transitional habitats (Flux and Chapman 1990). Despite the loss of habitat to agriculture and development, cottontails remain abundant in North America (Chapman et al. 1982). Predation is a factor that limits populations in fragmented landscapes (Smith and Litvaitis 2000). Therefore, cottontails are commonly found in open spaces near woody cover, but they can be found in urban areas with brush piles, shrubs, or tall grasses nearby where they can take cover from predators and inclement weather (Mammals n.d.). In terms of foraging, cottontail rabbits have higher foraging activity the closer they are to cover, generally preferring to forage under the cover of shrubs compared to trees (Abu Baker et al. 2015). Cottontails may be excluded from urban areas that lack low scrub understory, weed patches, and brush piles for refuge sites, or areas that have fences blocking escape routes (Abu Baker et al. 2015).

Forest cover is generally inversely related to higher human population and housing density (Mockrin et al. 2019, Heynen and Lindsey 2003, Troy et al. 2007, Luck et al. 2009). Thus, we would expect that eastern gray squirrels and eastern cottontails would be more abundant in areas of a lower population due to the increased availability of trees and brush, as well as the decreased abundance of predators. Based on most of the available literature on the natural history and habitat of these animals, both eastern gray squirrels and eastern cottontails were expected to be more abundant in lower human population areas of Champaign County.

Education attainment and income are related to human population density. Population decline is associated with increases in income inequality (Butler et al. 2020). Rural regions throughout the United States have consistently experienced high poverty rates related to geographical isolation and lack of economic opportunities (George 2014). In Illinois, rural areas have a higher poverty rate, lower education, and higher unemployment rate than urban areas (Selected Social… 2018). Therefore, areas with lower population density would be expected to have lower socioeconomic status with lower income and lower education levels.

We formulated hypotheses based on the backgrounds of the four species of focus and their relationship to human population density. Based on the expected human population density each species would be found at, we made predictions for the expected income and education levels that would correlate with population. We hypothesized that Virginia opossums and common raccoons would be found in areas of high human population, high income, and high education, while eastern gray squirrels and eastern cottontails would be found in areas of low human population, low income, and low education. Our study objective was to evaluate the impact of human population density, education level, and wildlife habitat on the possibility of each orphaned species to be brought into the wildlife clinic.

Since 2012, WMC patient data has been recorded in the RaptorMed database, an electronic medical record system (RaptorMed.com LLC 2011). We conducted a search of all mammalian cases admitted to the WMC categorized as “orphaned.” We identified the most abundant species of mammalian neonates brought into the University of Illinois WMC as SYFL, SCCA, DIVI, and PRLO. A literature search was performed and focused on their habitats and interactions with human populations. Key terms included “natural history,” “habitat,” “population density,” “human interaction,” “food sources,” and “foraging behavior.” Additionally, we researched correlations between human population density, income, and education level to determine what predictions we would make in our hypotheses. Overall, more than fifty articles met our search criteria, and the relevant information was used as literature background.

The inclusion criteria for population data was orphaned subadult animals of the four focus species with a found address that was within Champaign County. From the preliminary dataset of WMC records, we narrowed the inclusion to patients admitted between 2015–2020 and exported the pertinent data (patient ID, admission date, species, age at admission, reason for admission, found address) into Microsoft Excel (Microsoft Excel 2016). From that dataset, we eliminated cases that were not applicable to our study including other species, adults, and those with no address or an address outside of Champaign County. Finally, we removed duplicate case accessions of groups of animals brought in together. This created a distribution of case locations rather than total patients, which would eliminate skewing in areas where bigger groups of animals were found rather than many separate case instances. Our final RaptorMed dataset consisted of 1,222 cases (99 DIVI, 80 PRLO, 441 SCCA, and 602 SYFL).

Utilizing the RaptorMed postal address data, we converted the data to latitude and longitude GPS points using an online geocoding platform (Geocodio c2014-2021). After the first run, any addresses that were given an accuracy rating of less than 1.0 were searched and cleaned to create a more accurate GPS position. A second run of the data provided a set of GPS points used in the final dataset for the GIS map. We used the ArcGIS program (ArcGIS Pro 2.8, Esri, Redlands, CA) to create a GIS map of our GPS points and their corresponding unique patient ID number labels.

To add additional layers, we used information from the U.S. Census Bureau American Community Survey 5-year estimates data profile for 2014–2018 and decennial census data from the 2010 census to create maps of the socioeconomic information in Champaign County (2014–2018 ACS… 2019, Decennial Census… 2011). The maps included block groups or tracts of population density (persons/sq km), median family income, and percentage of people aged 25 and older with a Bachelor's degree or higher. We overlaid our layer of GPS points onto those additional layers to identify the distribution of points with respect to each layer. Data was extracted from ArcGIS detailing how the location of each GPS point correlated to the specific measurements of population density, income, and education. The results were exported onto a Microsoft Excel file and separated by species (Microsoft Excel 2016).

Before proceeding, we completed a thorough literature search to identify suitable parameters that could be used as cutoffs for high and low in each socioeconomic category. In accordance with the U.S. Census Bureau information, the USDA defines an urbanized area as having a population density of at least 1,000 persons per square mile (What is rural 2019). By converting this to metric units, we used 386 persons per square kilometer as the cutoff between high and low population density in our study. According to information obtained from the American Trends Panel (ATP) created by the Pew Research Center about defining 2018 family income tiers, low income families are defined as having incomes less than $40,100 and high income families have incomes at $120,400 or above (Social and Demographic Trends Project 2020). These amounts served as our cutoffs for income levels, where anything below $40,100 is considered low income, anything between $40,100 and $120,400 is considered middle income, and anything at or above $120,400 is considered high income. Finally, the U.S. Census Bureau reported that in 2019 the percentage of people in the nation aged 25 and older with a bachelor’s degree or higher was 36% (U.S. Census… 2020). This served as our cutoff for education level wherein anything at or above 36% would be considered high education and anything below 36% would be considered low education.

Using these established parameters, we categorized our data into each category within each species. The information obtained from these categorizations was used for statistical analysis with Stata software (Stata 16.1, StataCorp LLC, College Station, TX). We elected to run a two-sample test of proportions because we are interested in the difference between the high, low, and potentially middle groups for each category in each species. For each run, the null hypothesis (H₀) was: p1 = p2. If the Z test was significant, we reject the null hypothesis and accept the alternative hypothesis (Ha): p1 ≠ p2. Using the results from the statistical analysis, we determined which case clusters were significantly different and identified whether more cases fell into the high, low, or potentially middle category for each. Significant results were analyzed to determine if the category with the most cases was consistent with the predicted socioeconomic hypothesis for that species, or if it contradicted our expectation.

A full layout of the case distribution within Champaign County was observed (Fig. 1). Each corresponding socioeconomic layer was evaluated for the general layout of case distribution in regards to population density, income, and education within the county (Fig. 2, Fig. 3, Fig. 4).

Virginia opossums were found most frequently in areas of high population density (p < 0.001, Table 1), middle income (p < 0.001, Table 2), and high education (p < 0.001, Table 3). Common raccoons were also found most commonly in areas of high population density (p < 0.001, Table 1) and middle income (p < 0.001, Table 2), but there was no significant difference between the number of raccoons found in high versus low education areas (Table III).

Eastern gray squirrels and eastern cottontails were both found most frequently in areas of high population (p < 0.001, Table 1), middle income (p < 0.001, Table 2), and high education (p < 0.001, Table 3).

Table 1

Two-sample test of proportions showing distribution of cases found in areas of high population density versus low population density. N = total reports, n = number in a category, percentage (%) = n/N*100
Animal	N	Population Density (people per sq/km)	n	95% Confidence Interval (%)	P-value
DIVI	99	> 386 persons	91 (91.92%)	86.55–97.29	< 0.001
PRLO	80	> 386 persons	66 (82.50%)	74.17–90.83	< 0.001
SCCA	441	> 386 persons	417 (94.56%)	92.44–96.68	< 0.001
SYFL	602	> 386 persons	542 (90.03%)	87.64–92.43	< 0.001

Table 2

Two-sample test of proportions showing the distribution of cases found in areas of middle income versus areas of low and high income. N = total reports, n = number in a category, percentage (%) = n/N*100
Animal	N	Income ($) Middle	n	95% Confidence Interval (%)	P-value vs. Low	P-value vs. High
DIVI	99	40,100–120,400	84 (84.85%)	76.20-90.74	< 0.001	< 0.001
PRLO	80	40,100–120,400	69 (86.25%)	76.60-92.32	< 0.001	< 0.001
SCCA	441	40,100–120,400	348 (78.91%)	74.84–82.48	< 0.001	< 0.001
SYFL	602	40,100–120,400	522 (86.71%)	83.75–89.20	< 0.001	< 0.001

Table 3

Two-sample test of proportions showing the distribution of cases found in areas of high education versus low education. N = total reports, n = number in a category, percentage (%) = n/N*100
Animal	N	Education (% BS or above)	n	95% Confidence Interval (%)	P-value
DIVI	99	> 36	68 (68.69%)	59.55–77.82	< 0.001
PRLO	80	0–36	42 (52.50%)	41.56–63.44	0.527
PRLO	80	> 36	38 (47.50%)	36.56–58.44	0.527
SCCA	441	> 36	323 (73.24%)	69.11–77.37	< 0.001
SYFL	602	> 36	390 (64.78%)	61.14–68.76	< 0.001

Our investigation revealed that, as predicted, Virginia opossums and common raccoons were found most frequently in areas of high population density. Additionally, the opossums were found in the expected corresponding higher education areas, while raccoon education results were not significant. This could be due to the small sample size, as there were only 80 cases for that species. However, it could potentially mean that common raccoons are found equally distributed among higher and lower education areas.

Unlike our initial prediction, eastern gray squirrels and eastern cottontails were also found most frequently in areas of high population. Although the life history and much of the previous research suggests that populations would be higher in rural environments, these species are still habituated to human presence and can thrive in urban areas (Hunt et al. 2013, Abu Baker 2015, Engel et al. 2020, Bateman and Fleming 2014, Cooper et al. 2008, Engelhardt and Weladji 2011). One study even found that eastern gray squirrels were more abundant in urbanized areas with abundant tree cover as opposed to areas less affected by urbanization with comparable tree cover (Engel et al. 2020). Additionally, even if the species populations are not necessarily most abundant in higher human population density areas, the rate of encounters between humans and animals may be higher when there are more people around for the wildlife to come in contact with. Interactions between humans and wildlife are increasing as developments expand into nature (Ditchkoff et al. 2006).

Another unexpected result was squirrels and cottontails being found most frequently in high education areas. However, since these species were found more commonly in areas of high population, this higher education finding does line up with the prediction that higher education levels would correlate with higher population density. Moreover, Champaign County contains institutions of higher education, such as the University of Illinois, which may contribute to an increase in residents with higher educational attainment around those areas.

All four species were found most commonly in areas of middle-income level, as opposed to the expected high or low. Most of the United States falls in the middle class (Most Americans… 2021). Champaign County is consistent with that, and there are more areas of middle income than there are of high or low income (Fig. 2). Therefore, it is unsurprising that the four species overall were found more often in middle-income areas because they make up most of the county.

Using records, we were able to identify socioeconomic trends for the population of people that most commonly presents mammalian orphans to the WMC. This information will provide relevant background for the development of effective outreach programs to educate finders on the proper identification of orphaned wildlife in need of human care. It is necessary to increase public understanding of the biology of neonatal wildlife, how to discern whether a young animal is legitimately orphaned, and what resources to contact for assistance and professional intervention as needed. The identification of human-wildlife interaction trends and subsequent development of public outreach campaigns have previously been shown to positively contribute to successful wildlife population recovery and maintenance (Long et al. 2020, Young et al. 2011). Using the information from this study to develop informed public outreach will contribute to solving the issues with orphan misidentification and the overpopulation of wildlife rehabilitation centers.

Limitations of this study include the retrospective analysis of records, which does not provide as valuable information as studies conducted in a prospective manner that can be designed to avoid bias and confounding. Within our study population, there is the risk of selection bias based on the finders who bring animals in and provide found address information to use. The size of our population may also be a limiting factor, especially with raccoons (N = 80) and opossums (N = 99). Additionally, there are other ways to choose cut-off levels for our pre-established criteria of high, middle, and low. The results may differ depending on how the groups were divided and which cut-off was used.

While this analysis and our study are most directly applicable to the region of the Wildlife Medical Clinic, this type of information can be applied to other regions looking to improve informed outreach as well. The type of data in this study was readily available through our previous medical records, and other wildlife institutions can access electronic medical records to locate information on finder address and animal age in a similar manner. It was also advantageous to have a collaboration between veterinary clinical medicine and epidemiology in this study; other rehabilitators interested in doing similar research can look into working with epidemiologists for a more successful group effort.

Next steps for this area of research include analyzing other relevant variables for regions where orphaned wildlife are most commonly found. There is currently another ongoing study related to the environmental variables associated with found locations of orphaned wildlife in Champaign County that will help characterize the environments that have a greater incidence of human-wildlife interaction. Future directions for this research also include investigations into environmental and socioeconomic variables related to found locations of avian subadult species, as birds make up a substantial group of cases brought into rehabilitation facilities.

Acknowledgements

No external funding was required for this research and no additional acknowledgements of individuals or groups are indidcated.

Ethics approval: No ethics approvals were needed for human or animal research as this was retrospective in nature and did not meet the criteria for IACUC or IRB review.

Competing Interests: The authors have no relevant financial or non-financial interests to disclose.

Funding: The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

Authors contributions: The study conception and design were developed by Colleen Elzinga, Stephanie Heniff, and William Sander. Material preparation and data collection were performed by Colleen Elzinga and Stephanie Heniff. The analysis of the data was performed by Csaba Varga and William Brown. The first draft of the manuscript was written by Colleen Elzinga and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Availability of Data: If interested in seeing the complete datasets, please contact the corresponding author, William Sander, at [email protected].

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Spatial Analysis of Socioeconomic Factors at Found Locations of Orphaned Mammals in Champaign County

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