Name: Cities As Interfaces of Zoonotic Hazard Emergence: Development of the New York City Tick and Wildlife Urban Surveillance System (Video) | JoVE Journal
Uploaded: 2026-03-10T13:30:00.000+00:00
Duration: 10 min 17 s
Description: Learn to establish an urban tick and wildlife surveillance system using camera trapping and tick collection across New York City greenspaces. This protocol enables monitoring of zoonotic disease risk in urban environments.

ai_agent_IP

ai_agent

maintenance

instantTrialDurationMinutes

isBiopharma

enableNewMyJoveSectionDesign

educationFeatures

seoMetaApi

ai_copilot_institution_blacklist

ai_chat_institution_blacklist

freeAccessWindow

ssoRoleCapture

scheduledMaintenanceBanner

joveLabsFeatures

disable_autoplay_for_bots

extended_trial_duration

5 minutes

This work was supported by the National Science Foundation Graduate Research Fellowship (grant #10328000) and by the National Science Foundation&#39;s Coupled Natural Human Systems 2/Dynamics of Integrated Socio-Environmental Systems (CNH2/DISES) program (Award #1924061). Funding was also provided by the Centers for Disease Control and Prevention in support of the Northeast Regional Center for Excellence in Vector-Borne Diseases: Teaching &#38; Evaluation Center (NEVBD-TEC) (Award #NU50CK000633). Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the Centers for Disease Control and Prevention, the Department of Health and Human Services, or the National Science Foundation.
We would also like to acknowledge the support provided by the Urban Wildlife Information Network, with special thanks to Dr. Seth Magle, Dr. Mason Fidino, and Kimberly Rivera in setting up our wildlife camera trapping transect. We thank the Eco-Epidemiology lab members and Columbia and Barnard College students who have assisted with advice, setup, fieldwork, tick identification, and/or photo tagging, including Dr. Meredith VanAcker, Dr. Nichar Gregory, Sung-Joo Lee, Alexis Angulo, Hanna Chen, Molly Durawa, Rebecca Garcia, Aidyn Levin, Jake Miller, Safa Muhammad, Devorah Gordin, and Adara Anisman. We additionally thank City of New York Parks &#38; Recreation, Nassau County, Town of North Hempstead, North Shore Land Alliance, New York State DEC, New York State Parks, the Green-Wood Cemetery, the Evergreens Cemetery, Maple Grove Cemetery, Old Westbury Gardens, SUNY Old Westbury, and the ecologically inclined golf course managers for access to conduct research in their greenspaces. This research was conducted on Native lands of the Lenape, Matinecock, Merrick, Nissaquogue, and Massapequas.

The authors do not have any conflicts of interest to declare.

Through the initiation of TWUSS, this protocol demonstrates how to design a paired tick and wildlife monitoring transect that captures an urbanization gradient. Representative results from this system have allowed for the identification of landscape scale and wildlife host drivers of tick hazard in urban areas25. Key tick host data from the paired wildlife camera design has also been used to identify areas within New York City that may be newly at an elevated risk25. This highlights the importance of continued monitoring of ticks and wildlife across cities as urbanization increases globally, emphasizing the need for standard surveillance design and methodologies70. Replication of the TWUSS across cities where tick-borne hazards are emerging would allow for a large-scale comparison of urban landscape metrics driving the distribution of ticks and wildlife hosts25,70.
The TWUSS transect spans across a variety of greenspace types, management practices, and land stewardship jurisdictions, with 20 different land management groups requiring wildlife camera placement and tick collection permissions. Thus, the TWUSS relies on the participation of diverse collaborators, and evidence suggests that local participant engagement can improve the long-term success of tick surveillance systems94,95 and wildlife and biodiversity conservation efforts, including in urban contexts96,97,98,99. Collaboration with the municipal parks and health departments has contributed to increased signage within greenspaces where blacklegged ticks were collected and targeted vector surveillance by the municipal health department where tick-borne hazards were newly identified.
With the extensive amount of data collected comes the large task of data processing, tick identification, and the identification of wildlife species photographed. Maintaining an organized shared project database (step 9.2) containing all project information and data has been key to the success of the TWUSS since project management responsibilities have changed hands over the course of its establishment. Additionally, tick classification and photographic data can be an effective way for junior researchers and volunteers to engage with the project and increase interest in both the wildlife species and tick hazards inhabiting their local greenspaces. Through the TWUSS, educational opportunities for students have been integrated into the project through participation in transect data processing. In the two years since the initiation of TWUSS, over 30 junior researchers (undergraduate and master&#39;s level students) have contributed to image classification and tick identification, and for many of them, this was their first research experience. In addition to creating opportunities for university students, several community engagement efforts have been conducted with the New York Academy of Sciences and the Girl Scouts. Through these outreach activities, active researchers involved in the transect are paired with community organizations to facilitate hands-on learning and excitement about local ticks and urban wildlife. Through these projects, youth volunteers have learned how to place wildlife cameras, collect data, and summarize observations.
In the future, there is a potential for additional NYC transects. As the TWUSS continues to grow, the sheer volume of image data collected through each wildlife camera can limit research progress due to the slow process of image classification100. Recent advances in technology, including the incorporation of machine learning algorithms to process wildlife camera data, can help reduce this time lag101. The TWUSS utilizes an online platform (step 8.1) that employs machine learning models for initial image classification and has streamlined management, analysis, and sharing of wildlife camera trap data from the TWUSS.
In addition to these positive outcomes, the TWUSS has dealt with challenges of urban fieldwork, such as vandalism or theft of wildlife cameras. In the first two years of data collection (April 2022-January 2024), four wildlife cameras were stolen, and three were damaged (i.e., fully or partly smashed), leading to a wildlife camera replacement rate of 2% per deployment, which is consistent with other urban wildlife studies58. Tailoring the wildlife camera lock box label (step 5.1.4) and carefully ensuring that wildlife cameras were placed (step 5.2.1) away from heavy human activity and trails were key steps for reducing theft and vandalism. Additional troubleshooting may be required in the future by moving the wildlife camera to a new placement tree or retiring a site if theft and vandalism become unmanageable.
Wildlife cameras such as those employed in the TWUSS are often optimized to capture medium and large mammals55, leaving a gap in surveillance of the small mammal and avian community. Small mammals are the dominant reservoir hosts for many tick-borne pathogens23 and therefore adding targeted live-trapping at a subset of sites across the urbanization gradient would allow for more comprehensive surveillance of the tick host and pathogen community. Similarly, while numerous bird species were detected in the captured images, wildlife cameras are unable to accurately monitor bird diversity, some of which are reservoir hosts for ticks102. Thus, it is also suggested to add avian point counts, mist netting, or passive audio surveillance.
In conclusion, the experience of designing and implementing the TWUSS highlights some of the challenges, opportunities, and best practices for developing a paired tick and wildlife urban ecology research project with a diverse group of city-wide participants. Each wildlife camera placed and tick collected in the TWUSS transect is the result of a relationship built and maintained between researchers and local parks department personnel, township leaders, nature preserve land managers, golf course groundskeepers, cemetery staff, and many other individuals with varying investment and interest in tick-borne hazards and urban wildlife. Building successful collaborative research relationships and participant engagement takes time and effort, which must be incorporated in the research design and implementation of urban ecology projects within cities96,103,104,105. As cities across the globe expand, collaborative projects like the TWUSS can help people respond to emerging zoonotic hazards while coexisting with wildlife in urban environments.

The majority of the world's human population lives in urban areas, which are steadily growing in both population and geographic footprint1. While urban expansion is one of the leading threats to global biodiversity1,2,3, urban areas are also increasingly managed to provide habitat for wildlife via urban greening, as natural habitats are altered and fragmented4,5,6,7. The combination of dense human populations and wildlife in cities can lead to both positive (e.g., feeling connected to nature)8 and detrimental (e.g., zoonotic disease transmission)9 human-wildlife interactions. Historically, wildlife in urban systems has been understudied10, creating a critical gap for understanding wildlife persistence, zoonotic disease risk11, and human-wildlife coexistence12 in a changing world.
The Northeastern United States (U.S.) is intensely urbanized13 and a hotspot for blacklegged ticks (Ixodes scapularis) and tick-borne disease emergence14, with spatial differences in risk across the region15. Tick-borne diseases, such as Lyme disease, are the most reported vector-borne disease in the U.S., with nearly 500,000 cases a year14. In New York state, blacklegged ticks transmit several pathogens of public health concern, including Borrelia burgdorferi (Lyme disease agent), Anaplasma phagocytophilum (Anaplasmosis agent), Babesia microti (Babesiosis agent), and Powassan virus (agent of Powassan virus disease)16. In addition to the blacklegged tick, the longhorned tick (Haemaphysalis longicornis) has rapidly become established in the Northeast after its recent introduction17,18, and other native tick vectors such as the lone star tick (Amblyomma americanum), which can cause alpha-gal syndrome19, are increasing in population and expanding their range northward20,21, presenting new public health challenges.
Though urban tick-borne hazards are increasing15,22, most tick research has focused on rural and suburban areas22,23,24, creating critical gaps in understanding tick-borne pathogen ecology across urbanization gradients and in urban greenspaces15,25. Active surveillance of ticks, wildlife hosts, and host predators is necessary to inform evidence-based local management practices26.
New York City (NYC) has the largest human population of any city in the U.S.13. In 2020, an estimated &gt;8.4 million people lived in NYC's five boroughs13, and &gt;20.1 million people in the wider metropolitan area13. Though NYC has a higher average housing density (approximately 4700 housing units per km2) than any other city in the U.S.13, the city also contains over 29,000 acres (14% of land cover) of parklands stewarded by the NYC Department of Parks and Recreation27, which provides important habitat and refugia for hundreds of wildlife species28,29. The city is located along the Atlantic Flyway, a migration path for millions of birds30,31, and has recently been recolonized by several meso-mammal species, notably including the eastern coyote (Canis latrans x Canis lycaon)32, striped skunk (Mephitis mephitis)33, and white-tailed deer (Odocoileus virginianus)34.
As NYC invests in regreening35, the frequency of human-wildlife interactions is likely to increase36. While increases in urban biodiversity can be a sign of a healthy ecosystem37 and can restore a sense of nature to urban lives38, interactions with urban wildlife can also result in the transmission of zoonotic pathogens11,39. Urban-adapted mammals such as the common raccoon (Procyon lotor) are species of interest in this regard since they can be more tolerant of urbanization than other larger mammals40,41 and may mediate transmission of zoonotic pathogens in urban areas42. White-tailed deer are an additional species of interest, as their presence in urban greenspaces has been found to increase tick abundance and drive Lyme disease risk25,43.
Within the city limits of NYC, there is a robust vector surveillance program run by the Department of Health; however, ticks are not surveyed as extensively across the city's most urban greenspaces44,45,46. Adjacent county-level tick surveillance efforts vary in intensity47,48, making cross-municipal boundary comparisons difficult. There have been multiple studies of urban tick-borne hazards17,18,43,46,49 and wildlife33,34,50,51 within city limits, providing valuable insight on how ticks, tick-borne pathogens, and wildlife may respond to and utilize urban environments. However, the effects of urbanization on tick-borne hazards and wildlife communities extend beyond a city's core across urbanization gradients15,52,53,54. To the best of the authors' knowledge, there are no urban tick surveillance systems in place to monitor how ticks and their wildlife hosts emerge in environments across urbanization gradients.To help New Yorkers respond to emerging tick-borne hazards and coexist with wildlife, a diverse group of NYC partners worked to develop a Tick and Wildlife Urban Surveillance System (TWUSS) across an urbanization gradient from NYC through Long Island, NY.
<img alt="Urbanization impact analysis diagram; tick collection, wildlife monitoring, data reporting process." class="xfigimg" src="/files/ftp_upload/69755/69755fig1.jpg" data-altupdatedat="1787025648000" data-alt="Figure 1"> 
Figure 1. Steps for designing, implementing, and maintaining the Tick and Wildlife Urban Surveillance System. Feedback arrows are shown between potential greenspace selection, site permission, and selection of tick collection and wildlife camera sites. Figure created with BioRender. <a href="https://www.jove.com/files/ftp_upload/69755/69755fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Prior to establishing the TWUSS, an advisory committee was formed comprising key wildlife management partners and tick-borne hazard ecology experts who provided insights and feedback on the transect design (Figure 1). Monthly meetings were held to establish project goals, agreeing that the primary scientific goal was to include sites spanning a gradient of urbanization, habitat connectivity, and patch size. The inclusion of multiple advisors incorporated deep local knowledge of the area. For example, feedback between partners helped plan for the possibility of the network capturing the dynamics of coyote expansion and spread on Long Island32. Partner feedback also facilitated inclusion of sites where managers were known to be willing to provide access, community groups could help with wildlife cameras and/or photo tagging, and team members had personal ties that enabled site access.
Wildlife camera traps (also called trail or game cameras; hereafter termed wildlife cameras) have been widely adopted as a non-invasive and relatively low-cost method for collecting data on wildlife, transforming how wildlife studies are routinely performed55,56. Wildlife cameras enable the collection of large amounts of imagery data that would not be possible through traditional field observation methods57. The Urban Wildlife Information Network (UWIN) developed a wildlife camera trapping transect design, which is shared among member partner cities globally to monitor local wildlife distributions across urban gradients58. The UWIN transect design was adapted, and wildlife cameras were paired with tick collections in the creation of the TWUSS (Figure 1). "Tick drags" were used for tick sampling as a standard method for estimating tick densities59. The nymphal life stage of ticks was targeted for sampling, as nymphal ticks are the predominant vector of tick-borne pathogens to humans. Tick sampling was therefore conducted from the end of May through the end of July, in alignment with peak nymphal I. scapularis tick activity in the Northeast US60.
For a city partner to join the UWIN network, the main requirement is to establish at least one transect that can accommodate 25-30 wildlife camera station sites58; there is no minimum length requirement, nor does the transect need to follow a straight line. For the UWIN network requirements, wildlife camera stations can be established in any area that wildlife use, including backyards58. However, the TWUSS focuses on public or semi-public greenspaces, including city parks, cemeteries, forest preserves, golf courses, or large gardens for wildlife camera placements to capture the distribution of more sensitive target species, such as white-tailed deer and skunks6. The NYC transect is 55 km in length and 4 km wide, broken into ten 5 km-long segments. Four wildlife camera stations were established in each segment in 2022 for a total of 40 wildlife sampling locations spanning Brooklyn through Nassau County, NY. In 2023, an additional segment was added in Staten Island, NY. For tick collections, golf courses were excluded due to their use of insecticides and active tick management, which may interfere with the data61, resulting in 29 of the wildlife camera sites also becoming sites for tick collections.
The TWUSS differs from other tick surveillance systems in the urbanization gradient transect design and the pairing of standardized tick and wildlife data collection methods26,62,63. Many governmental and non-governmental institutions are engaged in tick surveillance64. However, most tick surveillance efforts have a limited objective of detecting tick presence and rely on passive, unstandardized surveillance methods62,63,65. Passive surveillance methods (i.e., tick reports on community science platforms) are advantageous over active surveillance methods in their low cost and relative effort to maintain62,66 but do not allow for a deep understanding of spatial and temporal trends63. In 2020, the Centers for Disease Control and Prevention (CDC) published a comprehensive guide to active tick surveillance that includes detailed directions for conducting tick drags, yet contains only general guidance on site selection59, a critical step for evaluating tick hazard67. Additionally, current tick surveillance methods rarely include paired wildlife monitoring, despite tick ecology being tightly linked to their wildlife hosts68,69. The Urban Wildlife Institute has made great efforts to standardize wildlife monitoring across urbanization gradients through the establishment of the UWIN58, and there is potential for expanding such networks to center One Health goals70. By combining standardized methods of tick collection and wildlife monitoring across an urbanization gradient, the TWUSS allows for meaningful comparisons of tick and wildlife host population dynamics where active surveillance efforts have been limited22.
The TWUSS design is most suitable in urban areas where wildlife, ticks, and the pathogens they transmit are present, range expanding, or emerging64,70. A key limitation to note includes the dependence of partner permissions and the large effort required to maintain the transect. Here, we present the process for establishing a transdisciplinary collaboration to design, implement, and maintain a paired wildlife camera trapping and tick surveillance system.

<table><tbody><tr><td>1x1 m white corduroy or flannel drag cloth</td><td>Ecoloogy Supplies</td><td>0015T</td><td>Can purchase pre-made or construct following directions outlined by the CDC 2020 or Salomon et al. 2020.</td></tr><tr><td>32+ GB SD memory card</td><td>SanDisk</td><td>SDSDXXO-032G-GN4IN</td><td>For photo storage</td></tr><tr><td>Batteries (most cameras require AA)</td><td>Duracell</td><td>MN1500B20&amp;nbsp;</td><td>For powering cameras</td></tr><tr><td>Camera adjustable locking cable</td><td>Moultrie (Python brand)</td><td>MCA-21667</td><td>For securing cameras</td></tr><tr><td>Camera security box</td><td>Browning</td><td>BTC-SB-SM</td><td>For securing cameras</td></tr><tr><td>Camera trap mounting strap</td><td>Moultrie</td><td>MCA-12667</td><td>For placing cameras</td></tr><tr><td>Clear plastic packing tape</td><td>NA</td><td>NA</td><td>For labeling widlife camera lock boxes and for collection of large amount of larva. Any brand works.</td></tr><tr><td>Dissecting microscope</td><td>AmScope</td><td>SM-1BSX-64S</td><td>For identifying ticks</td></tr><tr><td>DNA/RNA Sheild</td><td>Zymo research</td><td>R1100-250</td><td>For tick storage</td></tr><tr><td>Eppendorf Safe-Lock Tubes 1.5 mL - Microtube</td><td>Fisher scientific</td><td>05-402-94</td><td>1.5 ml vials for tick storage</td></tr><tr><td>Ethanol 200 proof</td><td>Thermo scientific</td><td>T038181000</td><td>For tick storage</td></tr><tr><td>Fanny pack</td><td>NA</td><td>NA</td><td>For holding / organizing tick collection materials while in the field. Any brand works.</td></tr><tr><td>Fine tipped permanent markers</td><td>Staedtler</td><td>55580-PK10</td><td>For labeling tick tubes</td></tr><tr><td>Forceps</td><td>Fisher scientific</td><td>12-000-121</td><td>For collecting ticks off the cloth and identifying under a microscope&amp;nbsp;</td></tr><tr><td>GIS Software</td><td>NA</td><td>NA</td><td>ArcGIS, QGIS, or R could be used depending on user preference</td></tr><tr><td>Kestrel 3000 Weather Meter</td><td>Kestrel Meters</td><td>830</td><td>For measuring climate / weather conditions</td></tr><tr><td>Motion-triggered camera trap (Browning strike force pro recommended)</td><td>Browning</td><td>BTC-5PX-1080</td><td>For photographing wildlife&amp;nbsp;</td></tr><tr><td>Writing utensil (pen or pencil)</td><td>NA</td><td>NA</td><td>For filling out datasheets. Any brand works.</td></tr></tbody></table>

cities as interfaces of zoonotic hazard emergence: development of the new york city tick and wildlife urban surveillance system

Cities As Interfaces of Zoonotic Hazard Emergence: Development of the New York City Tick and Wildlife Urban Surveillance System

Tick-borne hazards in urban landscapes are increasing, yet critical gaps remain in understanding human tick-borne disease risk across urbanization gradients. With the loss of natural habitat globally, urban greenspaces can provide a critical habitat for wildlife with varying consequences for tick-borne disease emergence. The New York City metropolitan area has the highest human population density and geographic footprint of any city in the United States, while containing a vast network of greenspaces. These urban greenspaces vary in surrounding land cover and connectivity, providing important refugia and habitat for hundreds of urban wildlife species, which have been historically understudied. To the best of the authors' knowledge, there are no urban tick surveillance systems in place to monitor how ticks and their wildlife hosts respond to and emerge in urban environments. To address this gap, a long-term urban tick and wildlife surveillance system was developed across an urbanization gradient from New York City through Long Island, NY. Here, we describe the process of working with a diverse group of partners to design, implement, and maintain a paired wildlife camera trapping and tick surveillance system. The process began by the formation of an advisory committee of tick-borne disease experts and wildlife management partners who provided insights and feedback to collaboratively work through the following steps: 1) initial urbanization gradient visualization and delineation, 2) potential greenspace selection, 3) site permissions, 4) wildlife camera and tick collection site selection, 5) wildlife camera placement, 6) tick collections, 7) tick identification, 8) wildlife camera photo identification, and 9) transect maintenance and reporting outcomes. As New York City and other cities across the globe continue to grow, collaborative projects such as the Tick and Wildlife Urban Surveillance System can generate knowledge that will help people respond to emerging zoonotic hazards in urban environments.

The transect line was situated to capture an urbanization gradient spanning three counties from New York City through Long Island, NY (Figure 2).
<img alt="Urban analysis maps; diagrams of impervious surfaces, housing density, tree canopy, transect data." class="xfigimg" src="/files/ftp_upload/69755/69755fig2.jpg" data-altupdatedat="1787025648000" data-alt="Figure 2"> 
Figure 2. Maps of the transect over the urbanization gradient. (A) Urbanization intensity represented by NLCD percent impervious surface. (B) Housing density based on census block units. (C) Percent tree canopy cover derived from NLCD data. (D) Satellite imagery showing the transect used for tick collections and wildlife camera deployment. <a href="https://www.jove.com/files/ftp_upload/69755/69755fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
All wildlife camera and tick collection sites for the NYC transect (n = 44 and n = 38, respectively) were placed in public parks (n = 21, n = 23), golf courses (n = 6, n = 0), cemeteries (n = 5, n = 3), nature preserves (n = 9, n = 9), and public gardens (n = 3, n = 3) (Table 1).
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Greenspace type</td>
			<td>Number of camera trap sites</td>
			<td>Number of tick collection sites</td>
		</tr>
		<tr>
			<td>Public park</td>
			<td>21</td>
			<td>23</td>
		</tr>
		<tr>
			<td>Nature preserve</td>
			<td>9</td>
			<td>9</td>
		</tr>
		<tr>
			<td>Public garden</td>
			<td>3</td>
			<td>3</td>
		</tr>
		<tr>
			<td>Cemetery</td>
			<td>5</td>
			<td>3</td>
		</tr>
		<tr>
			<td>Golf Course</td>
			<td>6</td>
			<td>0</td>
		</tr>
		<tr>
			<td>Total sites</td>
			<td>44</td>
			<td>38</td>
		</tr>
	</tbody>
</table>
Table 1: Summary of the number of wildlife camera and tick collection sites by greenspace type.
The calculation of percent tree canopy cover, percent impervious surface, and housing density within a 2 km radius of each wildlife camera placement site showed that the wildlife camera placement within each transect segment effectively captured the mean and spread (four sites) in urban metrics along the urbanization gradient from NYC through Long Island, NY (Figure 5). Since two densely forested sites and two lightly forested sites were selected in each transect segment, variation in percent tree canopy cover within 100 m of each wildlife camera showed that local habitat quality around each wildlife camera did not differ greatly (low R2) across the urbanization gradient (Figure 5D).
<img alt="Land use analysis showing transect segment graphs; impervious surface, housing density, tree canopy." class="xfigimg" src="/files/ftp_upload/69755/69755fig5.jpg" data-altupdatedat="1787025648000" data-alt="Figure 5"> 
Figure 5. Environmental characteristics around wildlife cameras across the transect. Four wildlife cameras are placed within each 5 km transect segment (1-10) from West to East, and the (A) percent impervious surface cover, (B) housing density, and (C) tree canopy cover are shown within a 2 km radius of each wildlife camera trap. (D) The tree canopy cover within a 100 m radius of the wildlife camera placement represents the diversity of the microhabitat quality within the greenspaces chosen. <a href="https://www.jove.com/files/ftp_upload/69755/69755fig5large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Since the initiation of the TWUSS system in spring 2022 through summer 2023, field data collection resulted in 4,928 wildlife camera trapping days and collection of 3,421 nymphal blacklegged ticks, 575 nymphal lone star ticks, and 329 nymphal longhorned ticks across all sites (Table 2).
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Nymphal tick species</td>
			<td>Total number collected</td>
		</tr>
		<tr>
			<td>Amblyomma americanum (Lone star)</td>
			<td>575</td>
		</tr>
		<tr>
			<td>Haemaphysalis longicornis (Long horned)</td>
			<td>329</td>
		</tr>
		<tr>
			<td>Ixodes scapularis (Blacklegged)</td>
			<td>3421</td>
		</tr>
		<tr>
			<td>All nymphal ticks</td>
			<td>4325</td>
		</tr>
	</tbody>
</table>
Table 2: Summary of nymphal tick collections by species across all 38 sites and two sampling years (2022-2023).
From the wildlife camera photos, 34 unique species were identified across all cameras (see Supplementary File 3 for full species list), including 16 mammalian species and 16 bird species (Figure 6). Wildlife cameras were configured to target meso-mammalian activity, and the following seven meso-mammal species were observed: Eastern coyote (Canis latrans var.), red fox (Vulpes vulpes), raccoon (Procyon lotor), Virginia opossum (Didelphis virginiana), striped skunk (Mephitis mephitis), white-tailed deer (Odocoileus virginianus), and groundhog (Marmota monax).
<img alt="Animal categorization diagram; includes meso-mammals, birds, small mammals, domestic animals." class="xfigimg" src="/files/ftp_upload/69755/69755fig6.jpg" data-altupdatedat="1787025648000" data-alt="Figure 6"> 
Figure 6. A collection of representative images captured across the transect, including meso-mammals, small mammals, birds, domestic animals, and humans. <a href="https://www.jove.com/files/ftp_upload/69755/69755fig6large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Through implementation of the TWUSS protocol, the first coupled urban tick-hazard and wildlife monitoring transect has been established across an urbanization gradient. A previously published study derived from this surveillance system linked the urbanization metrics across the gradient, keystone tick host (white-tailed deer) data from the wildlife cameras, and tick collection data to identify cascading effects of urbanization on wildlife host occupancy and tick hazard intensity25. This study found that increased greenspace connectivity, increased tree canopy cover, and decreased impervious surface hierarchically increased the probability of white-tailed deer occupancy, which significantly predicted the abundance of nymphal I. scapularis ticks within each greenspace (Figure 7, Table 3)25.
<img alt="Functional connectivity and deer occupancy graphs; threshold, tick count correlation in ecological study." class="xfigimg" src="/files/ftp_upload/69755/69755fig7.jpg" data-altupdatedat="1787025648000" data-alt="Figure 7"> 
Figure 7. Representative model plot results from the paired wildlife camera and tick collection transect design. (A) Functional connectivity as the strongest predictor of white-tailed deer occupancy probability. (B) Number of nymphal blacklegged ticks as a significant function of white-tailed deer occupancy. For plot A, the occupancy model was generated using photos of white-tailed deer from wildlife cameras set in 2022-2023. For plot B, points indicate field-collected numbers of ticks in 2022-2023 per 1600 m2 of sampling, and the regression line and standard error represent the negative binomial model prediction. The y-axis is limited to &lt;300 ticks for visualization. Figure adapted and reproduced with permission from Lilly et al. 202525 <a href="https://www.jove.com/files/ftp_upload/69755/69755fig7large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td colspan="3">Model type</td>
			<td colspan="2">Response variable</td>
			<td colspan="2">Model component</td>
			<td>Estimate</td>
			<td>SE</td>
			<td>z</td>
			<td>P( &gt;|z|)</td>
		</tr>
		<tr>
			<td colspan="3">Single-season occupancy model</td>
			<td colspan="2">Deer occupancy</td>
			<td colspan="2">(Intercept)</td>
			<td>3.04</td>
			<td>1.79</td>
			<td>1.7</td>
			<td>0.09</td>
		</tr>
		<tr>
			<td colspan="3"></td>
			<td colspan="2"></td>
			<td colspan="2">Functional connectivity</td>
			<td>11.09</td>
			<td>4.08</td>
			<td>2.72</td>
			<td>&lt; 0.01</td>
		</tr>
		<tr>
			<td colspan="3">Single-season occupancy model</td>
			<td colspan="2">Deer occupancy</td>
			<td colspan="2">(Intercept)</td>
			<td>−1.53</td>
			<td>1.04</td>
			<td>−1.48</td>
			<td>0.14</td>
		</tr>
		<tr>
			<td colspan="3"></td>
			<td colspan="2"></td>
			<td colspan="2">Percent tree Canopy Cover</td>
			<td>6.15</td>
			<td>2.27</td>
			<td>2.7</td>
			<td>&lt; 0.01</td>
		</tr>
		<tr>
			<td colspan="3">Single-season occupancy model</td>
			<td colspan="2">Deer occupancy</td>
			<td colspan="2">(Intercept)</td>
			<td>−1.26</td>
			<td>0.91</td>
			<td>−1.39</td>
			<td>0.16</td>
		</tr>
		<tr>
			<td colspan="3"></td>
			<td colspan="2"></td>
			<td colspan="2">Percent impervious surface</td>
			<td>−5.85</td>
			<td>2.24</td>
			<td>−2.62</td>
			<td>&lt; 0.01</td>
		</tr>
		<tr>
			<td colspan="3">Negative binomial generalized linear mixed effect model. Site and year were included as random effects. </td>
			<td colspan="2">Nymphal blacklegged tick abundance</td>
			<td colspan="2">(Intercept)</td>
			<td>−9.04</td>
			<td>0.81</td>
			<td>−11.17</td>
			<td>&lt; 0.001</td>
		</tr>
		<tr>
			<td colspan="3"></td>
			<td colspan="2"></td>
			<td colspan="2">Deer occupancy</td>
			<td>4.43</td>
			<td>0.57</td>
			<td>7.79</td>
			<td>&lt; 0.001</td>
		</tr>
		<tr>
			<td colspan="3"></td>
			<td colspan="2"></td>
			<td colspan="2">Within greenspace percent tree canopy cover</td>
			<td>0.03</td>
			<td>0.01</td>
			<td>2.33</td>
			<td>0.02</td>
		</tr>
	</tbody>
</table>
Table 3: Representative model output results from the paired wildlife camera and tick collection transect design. Occupancy model results showing top predictors of deer occupancy; negative binomial generalized linear mixed effects model results showing nymphal blacklegged tick abundance as a function of both deer occupancy and percent tree canopy cover within the greenspace. Table adapted and reproduced with permission from Lilly et al. 202525
Supplementary File 1. Example wildlife camera lock box label. <a href="https://www.jove.com/files/ftp_upload/69755/Supplementary%20File%201.docx" target="_blank">Please click here to download this File.</a>
Supplementary File 2. Example tick collection datasheet. <a href="https://www.jove.com/files/ftp_upload/69755/Supplementary%20File%202.docx" target="_blank">Please click here to download this File.</a>
Supplementary File 3. List of 34 unique species captured by the NYC Wildlife Transect wildlife cameras and identified visually by researchers. <a href="https://www.jove.com/files/ftp_upload/69755/Supplementary%20File%203.docx" target="_blank">Please click here to download this File.</a>

There is a critical need to monitor how ticks and their wildlife hosts respond to urbanization. This protocol outlines the steps for developing a standardized Tick and Wildlife Urban Surveillance System that will enable adaptive management in response to emerging tick-borne hazards in urban environments.

Tick-borne hazards in urban landscapes are increasing, yet critical gaps remain in understanding human tick-borne disease risk across urbanization ...

We investigate how urbanization and wildlife drive tick-borne hazards to understand pathogen transmission dynamics and public health risk. This protocol will fill a gap in our understanding of the role of wildlife in tick-borne diseases. Both of these fields have very specific standardized methodologies, which have not been so far connected in such a way that we can understand how wildlife communities impact tickborne diseases in urban areas.After selecting the urban wildlife and tick surveillance sites, organize all the required equipment and ensure that there are enough wildlife cameras, memory cards, locks, and lock boxes available for each deployment. Prepare enough fresh batteries, ensuring that each wildlife camera has a complete set of new batteries installed. Most wildlife cameras require six to eight AA batteries.For each upcoming wildlife camera deployment, confirm that all data from the previous season have been downloaded from the memory cards and properly saved. Create a label that includes a brief project description and contact information such as a permanent project email address. Print the label and attach it securely to the top of each wildlife camera lockbox using clear packing tape.Label each wildlife camera with a unique camera ID number. Then label an SD card with the same camera id, ideally associating it with the site name. Set the correct date and time on each wildlife camera trap.Adjust the settings on each wildlife camera to use multi-shot mode with three photos, a 30 second delay between captures, a 24 hour operating period, and a minimum image resolution of 16 megapixels. During the site visit, select trees within 50 meters of the randomly generated point that are suitable for wildlife camera placement. After selecting a suitable tree for camera placement, remove any large grass blades, leaves, or objects that could obstruct the camera's field of view and interfere with its sensors which have a detection distance of up to 80 feet.This reduces false triggers caused by wind movement. Place the wildlife camera at knee height approximately 50 centimeters in a location that is hidden from trails, angle the camera away from areas of human traffic, such as trails or walkways to minimize human detections. Avoid placing the camera facing directly east or west to prevent sun glare.Then check that the wildlife camera is aligned parallel to the ground. Deploy wildlife cameras at each selected location for at least 28 days, ensuring that deployments occur during January, April, July, and October to capture seasonal variation in wildlife habitat use and occupancy. Prepare all the required materials for the tick drag procedure.While wearing disposable laboratory gloves such as latex or nitro gloves, fill two to three vials per plant transect with a minimum of 500 microliters of either 85%ethanol or RNA DNA shield. Next, print enough data sheets to correspond to each planned transect. Place all tick collection materials including forceps, permanent markers, prepared vials, clear plastic tape for collecting abundant larvae, and a writing utensil for note taking into a fanny pack for field use.Prepare one fanny pack per field data collector. Estimate each field data collector's step length by having them walk a 10 meter transect three times, and then calculate the average step length. For personal safety During tick collection, prepare light colored long sleeved clothing such as white coveralls to improve the visibility of ticks on personnel.At least 24 hours before field tick collection, treat clothing and shoes with insecticide products containing 0.5%permethrin and let them dry. Before arriving at the field site, tuck pants into socks to prevent tick entry. Allocate 30 minutes to one hour to complete each 100 meter transect, adjusting the time as needed based on tick density.Record the details including the time of day and weather conditions such as temperature, wind, and relative humidity using a weather meter. Note the latitude and longitude coordinates at the start of the transect using a phone or GPS device with at least 10 meter accuracy. Next, spread a one by one meter white corduroy or flannel drag cloth across the ground, ensuring that it is completely unfolded and maximally in contact with the forest floor or lawn.Slowly walk a distance of 10 meters while dragging the cloth behind, then stop to inspect the cloth for ticks. Examine the cloth carefully for ticks by placing it under a light, scanning systematically from left to right and top to bottom and watching for any movement. Use forceps to collect all visible ticks and place them into 1.5 Milliliter vials pre-filled with 85%ethanol or RNA DNA shield.Use a separate vial for each 100 meter transect and label it with a unique transect number. If abundant larvae are found on the cloth, collect them using clear packing tape. At every 10 meter interval along the transect record the number of ticks collected on the data sheet.Record the dominant vegetation type in each 10 meter segment such as leaf litter, unmaintained herbaceous vegetation, or maintained grass. Finally, at the end of the 100 meter transect, inspect both sides of the drag cloth again for ticks and record the end time and GPS coordinates before moving to the next transect. Identify and record the species and life stage of all collected ticks following taxonomical keys for common ticks.The Urbanization Gradient Transect was designed to span three counties from New York City through Long Island in New York. All wildlife camera and tick collection sites were placed in public parks, golf courses, cemeteries, nature preserves, and public gardens. Across the urbanization gradient, the selected wildlife camera sites captured the mean and spread of impervious surface cover, housing density, and tree canopy cover within a two kilometer radius.Tree canopy cover within 100 meters of wildlife cameras showed variation within each transect segment indicating similar local habitat quality across the transect. Field data collection over two sampling years resulted in 4, 928 Wildlife camera trapping days and captured 3, 421 nymphal Blacklegged ticks, 575 nymphal Lonestar ticks, and 329 nymphal Longhorned ticks. Wildlife cameras recorded 34 unique species, including 16 mammals and 16 birds with seven meso mammals consistently observed across sites.Functional connectivity was the strongest predictor of white-tailed deer occupancy probability, showing a steep increase in predicted occupancy above a threshold value. Higher white-tailed deer occupancy significantly predicted a higher abundance of nymphal Blacklegged ticks. This protocol allows researchers to simultaneously study tick-borne hazards and wildlife host dynamics across an urbanization gradient.A key challenge to consider with our protocol is the large effort required to maintain the transect and the dependence on partner engagement. After establishment of the transect, additional factors such as social or abiotic could be evaluated to assess the influence on tick-borne hazards and wildlife.

cities-as-interfaces-zoonotic-hazard-emergence-development-new-york

Research

JoVE Journal

Biology

Lyme disease research studies often require generation of ticks infected with the pathogen Borrelia burgdorferi, a process that typically takes several weeks.  Here we demonstrate a microinjection-based tick infection procedure that can be accomplished within hours.  We also demonstrate an immunofluorescence method for in situ localization of B. burgdorferi within ticks.

Methods for Rapid Transfer and Localization of Lyme Disease Pathogens Within the Tick Gut

A method to preserve, detect and sequence RNA from Avian Influenza Viruses was validated and extended using natural faecal samples from birds. This technique removes the necessity of maintaining a cool chain and handling of infectious viruses and can be applied in a 96-well high-throughput setup.

Avian Influenza Surveillance with FTA  Cards: Field Methods, Biosafety, and Transportation Issues Solved

The collection of infected tick hemolymph, salivary glands, and saliva is important to study how tick-borne pathogens cause disease.  In this protocol we demonstrate how to collect hemolymph and salivary glands from feeding Ixodes scapularis nymphs.  We also demonstrate saliva collection from female I. scapularis adults.

Saliva, Salivary Gland, and Hemolymph Collection from Ixodes scapularis Ticks

Lyme disease is the most commonly-reported vector-borne disease in North America.  The causative agent, Borrelia burgdorferi is a spirochete bacterium transmitted by Ixodid ticks.  Transmission and detection of infection in animal models is optimized by the use of tick feeding, which we describe here.

Feeding of Ticks on Animals for Transmission and Xenodiagnosis in Lyme Disease Research

Here, we present a protocol to spatially and temporally assess the presence of viable microbiota in tick guts using a modified whole-mount in situ hybridization approach.

Visualization of Microbiota in Tick Guts by Whole-mount In Situ Hybridization

Here we present a next-generation sequencing protocol for 16S rRNA sequencing which enables identification and characterization of microbial communities within vectors. This method involves DNA extraction, amplification and barcoding of samples through PCR, sequencing on a flow-cell, and bioinformatics to match sequence data to phylogenetic information.

Tick Microbiome Characterization by Next-Generation 16S rRNA Amplicon Sequencing

The direct rapid immunohistochemical test (DRIT) offers a World Organization for Animal Health and World Health Organization (OIE/WHO) recognized alternative to the direct fluorescent antibody (DFA) test for rabies diagnosis. This test allows for field-based applications that can be performed in approximately 1 h upon brain impressions using light microscopy.

Enhanced Rabies Surveillance Using a Direct Rapid Immunohistochemical Test

The presented protocol can determine the vector competence of Aedes aegypti mosquito populations for a given virus, such as Zika, in a containment setting.

Vector Competence Analyses on Aedes aegypti Mosquitoes using Zika Virus

Presented here is a method to blood feed ticks in vitro via an artificial membrane system to allow for partial or full engorgement of a variety of tick life stages.

Tick Artificial Membrane Feeding for Ixodes scapularis

This article presents the methodology for exposing humans to larval Ixodes scapularis for clinical research. The technique is relatively simple, tolerable by the research volunteers, and can be modified according to experimental needs. Such research involving human subjects must be conducted under clinical study protocols approved by the appropriate regulatory authorities.

A Model for Experimental Exposure of Humans to Larval Ixodes scapularis Ticks

Here, we present a protocol to collect saliva, haemolymph, salivary glands, and midgut, following the dissection of an individual fed tick, to study tissue-specific localization of tick-borne pathogens to advance understanding of vector competence and pathogen-microbiome interactions.

Extraction of Saliva, Haemolymph, Salivary Glands, and Midgut from Individual Ticks (Acari: Ixodidae)

Learn to establish an urban tick and wildlife surveillance system using camera trapping and tick collection across New York City greenspaces. This protocol enables monitoring of zoonotic disease risk in urban environments.

Cities As Interfaces of Zoonotic Hazard Emergence: Development of the New York City Tick and Wildlife Urban Surveillance System

Explore More Videos