Risk of tick-borne pathogen spillover into urban yards in New York City

doi:10.21203/rs.3.rs-1551007/v1

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Risk of tick-borne pathogen spillover into urban yards in New York City

https://doi.org/10.21203/rs.3.rs-1551007/v1

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BACKGROUND: The incidence of tick-borne disease has increased dramatically in recent decades, with urban areas increasingly recognised as high risk for exposure to infected ticks. Green spaces may play a key role in facilitating the invasion of ticks, hosts and pathogens into residential areas, particularly where they connect residential yards with larger natural areas (e.g. parks). However the factors mediating tick distribution across heterogeneous urban landscapes remains poorly characterized.

METHODS: Using generalized linear models in a multimodel inference framework with model averaging, we determined the residential yard- and local landscape-level features associated with the presence of three tick species of current and growing public health importance in residential yards across Staten Island, NY.

RESULTS: We found that the amount and configuration of canopy cover immediately surrounding residential yards strongly predicted the presence of Ixodes scapularis and Amblyomma americanum, but not that of Haemaphysalis longicornis. Within yards, we found limited evidence for a protective effect of fencing against I. scapularis and A. americanum, but not H. longicornis. For all species, the presence of log and brush piles strongly increased the odds of finding ticks in yards.

CONCLUSIONS: We highlight considerable risk of tick exposure in residential yards in Staten Island and identify both yard- and landscape-level features associated with their distribution. In particular, the significance of log and brush piles for all three species supports recommendations for yard management as a means of reducing contact with ticks.

Ixodes

Haemaphysalis

Amblyomma

urban tick-borne disease

landscape metrics

Within the past two decades, reported cases of tick-borne disease in humans have increased over two-fold in the United States (1) with over 20 recognized human illnesses associated with ticks nationally (CDC(2). In the Northeast, Lyme disease, caused predominantly by the bacterium Borrelia burgdorferi sensu stricto (3) accounts for the majority of disease burden (CDC 2017), and its expansion has been associated with the geographic spread of the primary vector, Ixodes scapularis (4–9). I. scapularis is also a vector of multiple other pathogens of concern, including Babesia microti (10), Anaplasma phagocytophilum (11), and Borrelia miyamotoi (12), which are also spreading (13, 14). More recently, the lone star tick (Amblyomma americanum), which has been previously considered a nuisance species and most abundant in the southern US, has been spreading northward (15). This species is associated with transmission of Ehrlichia chaffeensis, the agent of human monocytic ehrlichiosis (16), Rickettsia rickettsii Wolbach, the etiological agent of Rocky Mountain spotted fever and Francisella tularensis McCoy, the agent of tularemia (17). In some emerging areas in the northeastern United States, A. americanum has surpassed I. scapularis as the most commonly reported human-biting tick (18) Finally, the Asian longhorned tick (Haemaphysalis longicornis), a recent invader to the United States (19–21) has been reported parasitizing humans (22, 23). Populations of H. longicornis have been recorded in the Northeast for the first time in 2018 (21, 24). In its native range, this species is a vector for a suite of human pathogens, including severe fever with thrombocytopenia syndrome virus (SFTSV; (25)) and Japanese spotted fever (26).

The absence of human vaccines for endemic and emerging tick-borne pathogens in the United States has led to a focus on individual preventative measures to reduce tick encounters (27). This includes recommendations for altering two key components of infection risk: the acarological hazard (defined as the density of pathogen-infected nymphs) and human exposure behaviors (28). In suburban residential yards, frequently cited risk factors for the acarological hazard include proximity to woodland, lack of fencing, log and brush piles in the yard, bird-feeders and cat ownership (29–33), which enhance hosts and tick off-host survival. The acarological hazard is determined by complex interplay between the local abiotic conditions conducive to tick persistence (34, 35) and the presence and abundance of hosts that support tick populations and pathogen persistence (36–38). As ixodid ticks spend the majority of their lives off-host, local abiotic conditions are critical for determining local tick survival, development and activity (39, 40). Thermal and desiccation tolerance of different tick species determines their habitat niche (Needham & Teel 1991) and in turn impact their host niche breadth through mediating exposure to hosts (41). The high sensitivity of I. scapularis to desiccation means that this species is typically associated with forests where leaf litter and high canopy cover produce high humidity conditions conducive to host-seeking and tick survival (Mathisson et al. 2020). In contrast, other tick vector species such as A. americanum, Dermacentor variabilis and H. longicornis have wider tolerances for microhabitat conditions and can occupy grassland habitats in addition to forested sites, as well as ecotonal habitats subject to human disturbance (Sonenshine and Stout 1968, Sonenshine and Mather 1994, Childs and Paddock 2003, Stein et al. 2008, Fryxell et al. 2015, Sonenshine 2018, Stafford III et al. 2018, Simpson et al. 2019).

Ixodid tick long-distance dispersal is mediated entirely by hosts (42), such that the community of hosts and the impact of landscape on host behavior can profoundly shape tick distribution (43, 44). Tick populations in urban parks and natural areas form metapopulations, i.e. connected subpopulations that are reliant on other subpopulations for persistence (45, 46). The extent to which patches are functionally connected by the movement of hosts through suitable habitats determine whether populations can persist in these patches. Abiotic and yard-specific features acting as attractants or barriers then determine whether hosts can transport ticks into yards. Thus, differences in the host and habitat associations of different tick species may produce relationships between acarological hazard and habitat that varies across spatial scales (30, 31, 47, 48), necessitating a combined focal and landscape-level approach. In this study, we seek to elucidate the drivers of tick distribution across an urban landscape, focusing on a newly emerging area for TBD; Staten Island, NYC. We take a multi-scale approach to investigate the associations between landscape heterogeneity and yard features, and the occurrence of three ticks of public health concern: I. scapularis, A. americanum, and H. longicornis. We hypothesize that the risk of TBD is hierarchically structured around the parks, with parks acting as source of ticks into yards.

2.1 Study design and sites

Staten Island, NY is one of five New York City boroughs, spanning 156 km² and it is the least-populated borough with 468,730 individuals(49, 50). The island is composed of heterogeneous neighborhoods, with differences in housing structure and demographic and socioeconomic composition, with 18% of the total area covered by urban parks(49). In 2017 we assessed the tick populations in NYC parks and the highest tick burden was found on SI parks (43). The rate of locally-acquired Lyme disease cases increased from 4 to 25 per 100,000 between 2000 and 2016(51). Staten Island presents a network of discrete patches of urban forests (parks) of different sizes, distributed across a range of development of low, medium, and high intensity, representing varying levels of connectivity to host movement.

In this heterogeneous urban matrix, we defined ‘ecological neighborhood’ (52) which are defined by the ecological process of interest, considering the time scale appropriate to that process and the organism's activity or influence during that period(53–55). Deer are key agents in structuring tick populations in parks and surrounding neighborhoods, and thus these ecological neighborhoods encompassed a core park and the surrounding residential areas within 500m from the park edges (Fig. 1a). This distance is consistent with the deer home-range size (43-158ha or 370-700m radius)(56), P. leucopus average dispersal distance of 500m (57) and is also consistent with estimated human walkable distance used in urban design (400m)(58). The neighborhoods were selected along a south-north gradient on Staten Island and we concentrated our efforts in neighborhoods with the highest number of reported Lyme disease cases in the 2010–2016 period, which also overlapped with the highest tick densities found in parks in 2017 (43) (Fig. 1b). Sampling occurred across the entire island but was concentrated in the mid- and south-mid sections due to a greater availability of park-adjacent houses.

Within these ecological neighborhoods residents were actively recruited using a random cluster sampling strategy. Starting from randomly selected points, we followed a line transect until recruiting 10 to 15 houses per cluster. We also passively recruited residents through a combination of targeted advertising in newspapers and online platforms. House visits were conducted from May through July 2018, 2019 and 2021. At each property, we recorded yard-level features that could determine the presence of ticks but attracting or deterring hosts or modify the microhabitat for the ticks, such as the presence of log or brush piles, woodchips or gravel at the edge of the property, vegetable or flower gardens, and birdfeeders. We also collected data on fencing around yards, recording fence type, whether yards were completely or partially fenced, and estimated fence heights.

Ticks were sampled from April to July in 2018, 2019 and 2021 by dragging a 1x1m corduroy cloth at ground level along vegetation within each property and at each property edge. Each property was sampled in any given year, and the total area sampled ranged from 10-400m, which was proportional to the size of each residential yard. The transects dragged were located at the edges of the property and around the house (Supplementary. Figure 1). At 10m intervals, ticks were counted and collected into 1.5ml snap-cap microcentrifuge tubs containing 70% ethanol (59, 60). We also used flags (1m x .5m corduroy attached to the end of a 1 m pole) to collect ticks within dense shrubs and ornamentals, and recorded the flagging time. Ticks were identified to species using established keys (61, 62)

2.2 Land cover and Landscape-level metric calculation

We characterized land cover and landscape-level features surrounding each residential yard, we combined 2017 4-band ortho-imagery, which uses high-resolution color infrared imagery to classify landcover at a 60cm resolution (www.earthdefine.com/landcover), with a 1m Canopy Height Model (CHM) dataset, which uses Light Detection and Ranging (LIDAR) data to estimate the elevation of vegetation and buildings.

The resulting landcover dataset encompassed seven land classes: grass (0-1ft), shrub (< 6ft), low canopy (≤60, high canopy (> 60ft), bare soil, water and impervious surface. Canopy classes were designated using the median tree height as a cut-off point. We created buffers of 25, 50, 100 and 200m around each residential property sampled and calculated the area and proportion of each landcover type, excluding water bodies, within each radii.

We used the “landscapemetrics” package in R (63), which uses a drop-in replacement for FRAGSTASTS (64) to extract 16 class-level metrics representing the spatial distribution and pattern of both canopy classes combined (as the focal class; Table 1), including shape, area, edge and aggregation metric categories. We scaled and centered all class-level landscape metrics covariates for analysis and tested for collinearity in predictor variables using the corrplot function (65). Due to high correlation among variables at both the landscape and class scales, we performed standardized Principal Component Analysis (‘PCA’ function, FactoMineR package; (66)) to reduce the dimensionality of the dataset. Varimax rotation method was used to derive orthogonal principal components, and the first two dimensions were used as variables in the statistical model.

Table 1

Class metrics used to describe patterns of canopy cover (focal class, combining high and low canopy cover) around residential yards.
Category	Acronym	Metric name	Description
Aggregation	COHESION	Patch cohesion index	Connectedness of patches
	ENN_MN	Mean of euclidean nearest-neighbor distance	Mean edge to edge distance to the nearest neighboring patch of the same type.
	NP	Number of patches	Number of patches
	CLUMPY	Clumpiness index	Proportional deviation of the proportion of like adjacencies involving the focal class from that expected under a spatially random distribution.
	nLSI	Normalized landscape shape index	Ratio of the actual edge length of focal class in relation to the hypothetical range of possible edge lengths of the focal class (min/max).
	AI	Aggregation index	Percentage of neighboring pixel, being the same land cover class, based on single-count method
	IJI	Interspersion and juxtaposition index	Measure of evenness of patch adjacencies, equals 100 for even and approaches 0 for uneven adjacencies
	MESH	Effective mesh size	Relative measure of patch structure based on probability that two randomly chosen points will be located in same patch.
Area and edge	TE	Total edge	Total length (m) of all edges between focal class and all other classes
	ED	Edge density	Sum of all edges of focal class in relation to landscape area
	LPI	Largest patch index	Percentage of landscape covered by corresponding largest patch of each class.
	GYRATE_MN	Mean radius of gyration	Mean distance from each cell to the patch centroid.
Shape	CONTIG_MN	Mean of contiguity index	Spatial connectedness of cells in patches.
Core area	TCA	Total core area	Sum of all core areas of all patches belonging to focal class
	CPLAND	Core area percentage of landscape	Percentage of core area of focal class in relation to the total landscape area
	NDCA	Number of disjunct core areas	Number of cells of focal class without neighbors with a different value other than itself.

2.3 Edge classification

We created a new vegetation contrast raster layer using the Canopy Height Model layer by estimating the height difference between any given pixel and its surrounding 8 neighboring pixels. The values of this layer were classified in four categories: “No edge”, “1-3m difference in vegetation type”, “3-9m difference in vegetation type”, “>10m difference in vegetation type”, “Edge between vegetation and non-vegetated surface (e.g., impervious surface, barren land, water)”. To characterize the type of edge in each property we generated an edge index by creating 1 and 2m buffers around the property boundary and extracting the area of landcover classes in each, the area of each type of category of the vegetation contrast layer, and the mean and standard error of the non-classified vegetation contrast layer. We also estimated the edge length (i.e., the perimeter of the property).

We used PCA to summarize the dimensionality of edge characteristics (edge length, vegetation contrast and landcover types), followed by a hierarchical cluster analysis (complete linkage) using the first two dimensions of the PCA. Hierarchical clustering identified three edge types, with edges defined as permeable (i.e. mostly vegetated), semi-permeable and low permeability (mostly impervious surfaces), based on the degree of contrast between landcover types.

2.3 Statistical analyses

All statistical analyses were conducted with R (67). We used generalized linear models (GLMs) with a binomial distribution and a log link function to assess the yard- and landscape-level features associated with the probability of nymph presence in yards, for each of the tick species collected. Given that urban yards on Staten Island are small, we hypothesized that ticks found in yards were mostly carried by hosts from nearby green spaces, and that the biotic and abiotic conditions in most yards are not sufficient to maintain large tick populations, and thus, depend on the constant introduction from nearby parks. As a result, tick densities were expected to be typically small on Staten Island yards and preclude any analyses beyond the presence/absence of ticks.

Yard- and landscape-level features were initially explored in separate models, including separate landscape-level models for each buffer radii size around yards. Akaike information criterion (AIC) was used to identify the most parsimonious model explaining variation in the presence of each tick species in yards from all possible combinations of explanatory variables (model selection using the ‘dredge’ function in the R package MuMIn); (68). Variables significant in the yard- and landscape-level models were included in the global model. To account for model selection uncertainty, multimodel inference was used to quantitatively rank the best fit models, where models with an AIC difference (∆AIC) < 2 were designated as having similar support to the best model. Odds ratios and their 95% confidence intervals were calculated from model averaged coefficients for each explanatory variable.

3.1 Residential yard surveys

From April to September in 2018, 2019 and 2021,we visited a total of 1988 houses. Receptivity to door to door recruitment was high although 56% of the houses were closed (i.e., householders were not home at the time or did not open the door): among those who opened the door during our visit (n = 857), 72% of residents approached willing to participate in the study. An average of 29 (± 1.5) houses participated in two years, and 8 houses participating in all three years. A total of 529 unique yards were surveyed for ticks across Staten Island. We observed considerable variation in yard features between sites, with fully enclosed fencing being the most common, followed by water sources (e.g. swimming pools), and vegetable or flower gardens (Table 2).

Table 2

Association of specific residential yard features with presence of ticks (*Amblyomma americanum*, *Haemaphysalis longicornis*, or *Ixodes scapularis*).
Yard feature	Number (%) of residential yards with the feature	Number (%) of yards with the feature present from which ticks were collected
Log or brush pile	144 (27%)	73 (51%)
Woodchips or gravel	130 (25%)	45 (36%)
Vegetable or flower garden	274 (52%)	84 (31%)
Birdfeeder	83(16%)	26 (32%)
Chicken coop	6 (2%)	0 (NA)
Children’s play equipment	86 (16%)	24 (29%)
Outdoor seating in lawn	133 (25%)	44 (34%)
Compost bin*	25 (10%)	19 (37%)
Trashcan in yard*	114 (45%)	33 (29%)
Food or shelter for cats*	32 (12%)	17 (53%)
Fully enclosed fencing	302 (68%)	71 (24%)
Water source*	142 (54%)	36 (26%
* Sampled in 2021 only

Most yards contained some form of fencing (84%), most had only the backyard completely fenced (58%), followed by partial fencing (32%) and few houses had both front and backyards completely fenced (10%). For analyses, we combined yards that had both front and backyards being completely fenced with those having only the backyard completely fenced into a “fully enclosed fencing” category (68%). Fence types included chain link, wooden picket, full panel, farm and aluminum fences, and ranged from 0.3 -2.5m (mean = 1.60 m, SD = 0.35) tall. We categorized the fences into three height categories for analysis based on an assumed effect on deer movement (69): no fence, non-deer-proof fence (< 1.8m) and deer-proof fence (> 1.8m).

The proportion of yards containing at least one tick of any species was consistently ~ 30% (range = 29–35%) over the three years (Fig. 2). However, the prevalence of each of the tick species in yards and their spatial distribution varied across years, with I. scapularis dominating the urban tick community in 2018 (35% houses), and H. longicornis most frequently observed in 2019 and 2021 (25% and 26% of houses, respectively; Fig. 2).

3.2 Property edges – classification and relationship to block type levels

Yards with permeable edges were most common (n = 266), followed by semi-permeable (n = 195) and impermeable (n = 66) edge types. The distribution of edges varied across ecological neighborhoods, with yards in the northernmost sites (e.g. Clove Lakes) being dominated by impermeable edge types and those in the mid-island being largely semi-permeable and permeable.

3.3 Land cover and landscape metric multivariate classification of urban yards

The proportion of impervious surface, low and high canopy cover were similar across all buffer radii, together comprising over 80% of the area surrounding yards (Table 3).

Table 3

Proportion of landcover classes in buffer radii around yards sampled.
Landcover class	25m (Mean ±SD)	50m (Mean ±SD)	100m (Mean ±SD)	200m (Mean ±SD)
High canopy	0.22 (0.18)	0.24 (0.18)	0.30 (0.18)	0.35 (0.18)
Low canopy	0.29 (0.10)	0.28 (0.08)	0.27 (0.07)	0.26 (0.06)
Shrub	0.05 (0.02)	0.05 (0.01)	0.05 (0.01)	0.04 (0.01)
Grass	0.08 (0.05)	0.08 (0.04)	0.07 (0.03)	0.07 (0.03)
Impervious surface	0.35 (0.15)	0.33 (0.14)	0.30 (0.13)	0.27 (0.13)
Barren soil	0.008 (0.02)	0.01 (0.02)	0.007 (0.01)	0.006 (0.01)

For the landscape metric PCA analysis, the two first components combined explained between 56% and 70.1% of variation in original landscape metrics, with increasing radii explaining greater proportions of variation. For all radii, increasing values of PC1 (the first dimension of the PCA analysis) were positively correlated with area of high canopy cover and high values for aggregation, area and edge metrics (Fig. 3). Thus, yards with high PC1 loadings contained large, compact, and connected patches of canopy within the surrounding buffer area (Fig. 3). Increasing values of PC2 (the second dimension of the PCA analysis) were negatively correlated with total edge, number of disjunct core areas, and number of patches (Table 2), indicative of smaller, disaggregated patches of canopy.

3.5 Yard and landscape-level features associated with tick presence

The probability of detecting each of the three tick species was associated with different yard- and landscape-level features and was in all cases best explained by models containing parameters from both yard- and landscape- scales. All species showed significant associations with year, with the probability of finding I. scapularis decreasing over the sampling period, whereas those for A. americanum and H. longicornis increasing an average of 3- and 9-fold, respectively (Table 4).

In the yard-level only model, log and brush piles significantly increased the odds of finding all three tick species (Table S2). This effect was particularly strong for H. longicornis and A. americanum, which were associated with 3.6- and 4-fold increases in the odds of tick presence, respectively. Woodchips and gravel at the edge of yard properties increased the probability of finding A. americanum (OR = 2.3, 95% CI[1.2–4.3], p = 0.03), but not the other species. Full fencing of any kind around the yards decreased the odds of I. scapularis being present by 0.43 (95% CI [0.22–0.87], p = 0.01), and of A. americanum by 0.47 (95% CI [0.23, 0.94], p = 0.03), but had no effect on H. longicornis (p = 0.10). The permeability of the property edge impacted the probability of finding A. americanum but not H. longicornis or I. scapularis, and was only significant for a permeable – semi-permeable edge contrast (OR = 0.47, 95% CI[0.23–0.96], p = 0.04).

In the landscape-level only model, the distribution of ticks was best determined by landscape metrics at different scales. PC1 calculated for the 100m buffer best predicted I. scapularis distribution (OR = 1.45, 95% CI[1.3, 1.7], p < 0.001), 50m buffer best predicted A. americanum (OR = 1.2, 95% CI[1.0-1.4], p = 0.01) and PC1 was not significant at any scale for H. longicornis; PC2 was not significant in any models.

In the global model, I. scapularis presence was best explained by PC1 at 100m, the presence of log or brush piles and sampling year. Log and brush piles and woodchips remained the strongest predictors of A. americanum presence in yards, followed by PC1 at 50m. Additionally, the density of A. americanum nymphs per 100m in nearby parks increased the odds of detecting A. americanum ticks in yards. Landscape metrics had no effect on the odds of detecting H. longicornis, which was associated with the presence of log and brush piles and the density of conspecific nymphs in the nearest park. Sampling year had the strongest effect on H. longicornis, with the odds of finding them in 2021 9-fold greater than in 2018. For all species, the presence of full fencing was retained in the models and had a negative association, however the effect was no longer significant when including the landscape-level metrics (Table 4).

Tick-borne diseases present an increasing threat in urban areas, where high human density can intensify human exposure to ticks given suitable habitat and host niches. We highlight considerable risk of tick exposure in residential yards on Staten Island, and demonstrate that the dynamics of three tick vector species in a highly fragmented urban environment are determined by both yard- and landscape-level features. In particular, we found that the distributions of the Lyme disease spirochetes vector, I. scapularis, and of A. americanum was largely determined by yard- and landscape- level factors, whereas H. longicornis was only impacted by yard-level features but not landscape level factors assessed in this study.

Host and habitat association may partially explain yard presence of the three tick species. Tick persistence depends on the local abiotic conditions being conducive to survival (4, 28, 34, 35). The finding that I. scapularis presence is most strongly associated with the amount of large, well connected patches of canopy cover in the surrounding landscape is in line with previous work linking the species to forest habitat and connectivity (43, 46, 70–74). Underlying this relationship is the sensitivity of I. scapularis to desiccation, which may constrain the tick to large, connected patches of canopy cover in urban areas, outside of which high impervious surface cover dramatically increases local temperatures and reduces saturation deficit (75, 76). Both A. americanum and H. longicornis are more tolerant to desiccation and heat stress (77–81), than I. scapularis, allowing them to persist in a range of habitats. In particular, H. longicornis can withstand temperatures up to 40˚C and severe dehydrating conditions under laboratory conditions (77, 82). Differences in the temperature and humidity tolerance of A. americanum and H. longicornis may explain the disparate distributions and landscape associations of the two tick species, despite exhibiting generalist host associations.

At the yard level, log and brush piles was consistently associated with the presence of all tick species. Log and brush piles may act as thermal refugia for ticks, allowing them to persist and to quest near open lawns where they would otherwise desiccate, and may also act as habitat for small-bodied hosts, such as mice, and dens for meso-mammals, such as raccoons (83, 84). Brush piles increase overwinter survival of white-footed mice (85), which act as important hosts for I. scapularis larvae, and the distribution of which may determine where fed larvae are deposited and emerge the following spring as nymphs. Landscaping tick control measures (e.g. clearing brush piles) have been found to increase risk of I. scapularis-associated disease (70) and to have no effect on Lyme disease cases (86). These different findings may reflect the discordant scales and metrics at which the two studies were conducted, the former being a meta-analysis of several studies that aggregated log and brush clearing into a property management risk category, and the latter being a neighborhood-matched case-control study. Our finding of higher probability of nymph presence with log and brush piles may or may not lead to increased human tick exposure and disease depending on human exposure behavior. Further work elucidating human behavior in similar urban settings would provide insights into the relative roles of the natural and human components of urban TBD risk.

As to host associations, adults of all tick species in the study are dependent on white-tailed deer as the primary reproductive stage host (10) and for movement through landscapes (43). However, A. americanum and particularly H. longicornis are also associated with mesomammal hosts, such as raccoons and opossums (21, 77, 87–89). Both nymphal stage A. americanum and H. longicornis have been found to exhibit generalist feeding behavior, feeding on a range of livestock, birds, and small mammal species (77, 90), which would allow them to feed on hosts less limited by landscape structure and fences than deer (e.g. squirrels and racoons; (91). The association between fencing and nymphal I. scapularis and A. americanum, but not H. longicornis, may thus reflect subtle differences in proportional host use between the three species. Differences in host use, and subsequently differences in the impact of the landscape on host movement (i.e. its functional connectivity (93)) may also explain the positive association between I. scapularis and A. americanum in residential yards and canopy cover and connectivity in surrounding yards, but not H. longicornis. Previous work in New York City found considerably greater burdens of immature stages of A. americanum and H. longicornis on raccoons than I. scapularis (89). Additionally, opossums were found to have highest infestation prevalence and intensity of immature H. longicornis than either I. scapularis or A. americanum, providing some support for differences in host use.

Finally, we identified an invasion front for H. longicornis, and potentially for A. americanum, which increased 9-fold and 3-fold, respectively, from 2018 to 2021. Whilst H. longicornis does not yet pose a significant public health risk in the Northeast US, its vector competence for several disease agents in its native range makes it a cause for concern. A. americanum was also more frequently observed over the sampling period, and is currently associated with several disease agents of humans (94, 95). Understanding the environmental factors associated with the distribution of these expanding tick populations, at appropriate spatial scales, is a critical first step towards guiding future policy regarding tick surveillance and management recommendations for individuals in high-risk areas.

Proximity to parks and the amount and aggregation of forest canopy immediately (50-100m) surrounding residential properties is a key risk factor for finding ticks in yards, particularly of the Lyme disease spirochetes vector, I. scapularis. However, complete fencing and removal of log and brush piles can mitigate landscape-mediated effects on the tick hazard by impeding host movement through yards and decreasing the amount of suitable habitat available for wildlife hosts and ticks.

Acknowledgements

We would like to acknowledge our field assistants (Catherine Grady, Myles Davis, Brigitte Franco, Lily Davenport, Robert Cassidy, Patrick Connelly, Torre Lavelle, Richie Konowal, Gabriela Galindo and Christina Ng) for providing research support. This publication was supported by the Cooperative Agreement Number U01CK000509-01 between the Centers for Disease Control and Prevention and Northeast Regional Center for Excellence in Vector Borne Diseases, and the National Science Foundation's Coupled Natural Human Systems 2/Dynamics of Integrated Socio-Environmental Systems (CNH2/DISES) program (Award #1924061). 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.

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You are reading this latest preprint version

Risk of tick-borne pathogen spillover into urban yards in New York City

Status:

Version 1

Abstract

Figures

1. Introduction

2. Methods

2.1 Study design and sites

2.2 Land cover and Landscape-level metric calculation

2.3 Edge classification

2.3 Statistical analyses

3. Results

3.1 Residential yard surveys

3.2 Property edges – classification and relationship to block type levels

3.3 Land cover and landscape metric multivariate classification of urban yards

3.5 Yard and landscape-level features associated with tick presence

4. Discussion

5. Conclusions

Declarations

Acknowledgements

References

Competing Interests

Supplementary Files

Status:

Version 1