Global patterns of reported
human-wildlife interactions in areas of land-use change
Reilly T. Jackson1, Tamika J. Lunn1,
Nathaniel Mull1, Maureen R.
McClung2, Kristian M. Forbes1
Department of Biological Sciences, University of Arkansas,
Fayetteville, Arkansas, USA 72701
Department of Biology and Health Sciences, Hendrix College, Conway,
Arkansas, USA 72032
Abstract
Emerging infectious diseases are one of the greatest and most pertinent
threats to human health and security. Land-use changes, which are
increasing globally, are a key driver of emerging infectious disease
outbreaks; they increase contact between wildlife and humans and create
opportunities for transmission of pathogens between them. While there is
great emphasis to characterize the circumstances underlying disease
outbreaks in search of mitigation strategies, a global synthesis of
documented human-wildlife interactions in the context of land-use change
has not been previously done. We conducted a scoping literature review
to identify the geographic, taxonomic, and land-use change focus of
reported interactions between wildlife, humans, and domestic animals,
and discuss the implications of our results in the context of
understanding high-risk settings for pathogen exposure. From 529
included articles, we show that
human-wildlife interactions are most often reported in Asia and Africa
and are most associated with agriculturalization on a global scale
except in North America and Oceania, where urbanization was more
commonly associated with interactions. Humans and domestic animals
interacted with 1021 species of wildlife, including various amphibians,
birds, mammals, and reptiles.
Interactions with mammals were
reported most often, mostly with species in the orders Artiodactyla,
Carnivora, Primates, and Rodentia. The type of interactions, and thus
potential for cross-species pathogen transmission, varied significantly
among continent and wildlife taxa. Our review highlights increased human
risks for wildlife pathogen exposure in areas of Africa and Asia with
high rates of agriculturalization, and from even-toed ungulates,
carnivores, primates, and rodents on a global scale. Further, we
identified important gaps in knowledge, such as a lack of documented
human interactions with wildlife in central and southwestern Asia and
northern Africa and a surprising lack of documented human interactions
with bats globally, despite their high number of synanthropic species
and role as hosts of zoonoses.
Keywords: agriculturalization, human-wildlife contact, land restoration,
pathogen surveillance, spillover, urbanization, zoonoses
Running head: Human-wildlife contact under land-use change
Contact author:
Reilly T Jackson
Department of Biological Sciences, University of Arkansas
850 West Dickson Street, Fayetteville, USA 72701
Email: rtj006@uark.edu,
phone: +1 479-575-6701
Introduction
Emerging infectious diseases (EIDs) are an increasing threat to global
human health and security, as evidenced by the current monkeypox virus
outbreak and COVID-19 pandemic (Daszak et al. 2000, Morens and Fauci
2013, Wang et al. 2022, Zumla et al. 2022). Most EIDs are zoonotic in
origin, reaching human populations via transmission from wildlife and
domestic animal host species (spillover), with the majority initiating
in wildlife hosts (Jones et al. 2008). Once in human populations,
zoonotic diseases have the potential to spread efficiently due to high
population densities and connectedness in contemporary globalized
societies, hindering the ease and potential efficacy of downstream
mitigation efforts (Coltart et al. 2017).
A major research priority is to understand the circumstances by which
wildlife pathogens are transmitted to humans (Plowright and Hudson
2021). Exposure to pathogens at the human-wildlife interface is the
obligatory first step in zoonotic spillover and is driven by multiple
ecological mechanisms, including the distribution and abundance of
reservoir host species, the prevalence and intensity of infection within
reservoir hosts, and the persistence of pathogens once outside the host
(Plowright et al. 2017, Wilkinson et al. 2018). Human exposure to
wildlife pathogens can occur via direct (e.g., contact with wildlife
through butchering and consumption) and indirect (e.g., contact with
excreted pathogens in environments where human and wildlife activities
overlap) mechanisms (Wolfe et al. 2005, Magouras et al. 2020).
Additionally, domestic animals, including pets and livestock, can serve
as intermediate (bridging) hosts between wildlife and humans, as
occurred with several recent high profile disease outbreaks (e.g., pigs
and horses for Nipah and Hendra viruses, respectively; Chua et al. 2000,
Playford et al. 2010).
One of the principal drivers of
human exposure to wildlife pathogens is land-use change (LUC), or
anthropogenically induced environmental changes (Foley et al. 2005,
Woolhouse and Gowtage-Sequeria 2005, Jones et al. 2008, Gottdenker et
al. 2014). Land-use change can impact the abundance and distribution of
wildlife and shape wildlife-pathogen interactions, collectively
increasing pathogen shedding by reservoir hosts and creating new contact
opportunities that facilitate intra- and interspecies pathogen spread
(Patz et al. 2004, Keesing et al. 2010, Jones et al. 2013, Faust et al.
2018, Mendoza et al. 2019, Carlson et al. 2022). For example, Nipah
virus emergence in Malaysia is believed to have occurred due toPteropus bats moving to roost and feed in orchards surrounding
pig farms following deforestation and El Niño-induced drought in their
habitat (Chua et al. 2002); Nipah virus was shed in bat saliva and
excreta, infecting pigs below, which in turn transmitted the virus to
humans. Land-use change has also been associated with spillover ofBorrelia burgdorferi (the causative agent of Lyme disease),
hantaviruses, ebolaviruses, and Hendra virus through effects on pathogen
exposure (Allan et al. 2003, Wolfe et al. 2007, Plowright et al. 2011,
Prist et al. 2017, Rulli et al. 2017).
Existing review articles have evaluated links between LUC and pathogen
spillover and emergence (Jones et al. 2013, Gottdenker et al. 2014,
Johnson et al. 2015). These reviews focus on zoonotic disease outbreaks
as a measure of spillover risk but do not evaluate exposure risk
specifically. This nuance is crucial because documented disease
outbreaks only capture a small fraction of total disease outbreaks
(e.g., Glennon et al. 2019), and miss the many exposure opportunities
that could have – but did not – lead to disease outbreaks (Plowright
et al. 2017). Given that LUC primarily increase pathogen spillover and
disease emergence in humans through effects on exposure risk,
mechanistic insights into how LUC has influenced zoonotic spillover can
be gained through evaluation of studies that link LUC with exposure. To
this end, we conducted a scoping, quantitative literature review that
characterizes the global breadth of studies that document human exposure
to wildlife in the context of LUC to: 1) identify the geographic,
taxonomic, and LUC focus of reported interactions; and 2) discuss and
compare our results with previously identified geographic and taxonomic
hotspots for spillover and emergence risk to highlight the most at risk
settings and identify research needs.
Methods
We conducted a scoping literature search in Web of Science in May 2022
to identify empirical articles that report on wildlife interactions with
humans and domestic animals in areas of LUC (Figure 1; a full
description of the search strategy is available in the Supplementary
Materials). To ensure that articles contained relevant information, we
applied the following criteria. First, studies had to report
human-wildlife interactions within the context of human-induced LUC that
is occurring or has occurred in the study area and describe the type of
modification. Second, studies had to identify the type of wildlife
involved to at least order level. Third, studies had to report the type
of human-wildlife interaction (direct, indirect, or domestic animal
contact, see below). Fourth, studies must have been based on empirical
data. We limited our scope to terrestrial and arboreal vertebrates since
they are the overwhelming reservoir source of zoonotic disease outbreaks
(Han et al. 2016, Olival et al. 2017).
For each included article, we extracted the following information: the
country where the interaction occurred, type of LUC, wildlife taxa
involved, domestic animals involved, type of interaction, and standard
journal article details (publication date, publishing journal). Land-use
change was categorized into five types (Foley et al. 2005): (1)
agriculturalization, (2) energy development, (3) land restoration, (4)
resource extraction, and (5) urbanization (full definitions of each
category are provided in the Supplementary Materials). Interactions
between humans and wildlife were categorized into three types: (1)
direct physical contact, such as humans touching or consuming wildlife
and their effluent, (2) indirect contact, such as when humans and
wildlife occupy the same areas but not necessarily simultaneously (e.g.,
humans observing wildlife on their property), or (3) domestic
animal-mediated contact, as a way of quantifying the potential for
exposure via intermediate host species that also have contact with
humans (Table S1). Lastly, we collected information on the type of study
(before-after comparison, cross-sectional, experimental, and
longitudinal) and on techniques of data collection to understand methods
applied within included studies.
To investigate how the total number of publications reporting
human-wildlife interactions varied by continent, taxa, interaction type,
and LUC type, we used chi-square analyses. Post-hoc testing was done to
understand interactions between every two categorical variable
combination (package “chisq.posthoc.test”; Ebbert 2022). Due to a lack
of studies on energy development and resource extraction, and given
their similar characteristics with urbanization, these three LUC types
were combined to permit more robust analyses. Due to lack of studies
reporting human interactions with amphibians, we removed these records
from analyses. For studies mentioning multiple continents, wildlife
taxa, levels of interaction intensity, or LUC types, we counted the
study for multiple categories (Gottdenker et al. 2014). Lastly, because
of the differing zoonotic potential amongst wildlife orders, we used a
chi-square analysis to compare publication count among orders within
each class. Due to the large number of orders involved in interactions,
we only included orders documented in 10 or more publications in this
set of analyses.
Results
A total of 529 articles were identified that met our inclusion criteria
(Figure 1; a full list of included articles and their citations are
provided in the Supplementary Materials). Articles were published from
1994-2022 in 173 different journals and two pre-print servers. Over 85%
of included articles were published since 2012 (n = 462), demonstrating
a strong recent increase in relevant literature. Almost 70% of included
articles included cross-sectional data (n = 357), followed by
longitudinal (n = 231), experimental (n = 15), and before-after
comparisons (n = 8). There were 13 main types of data collection
techniques, with the most common methods including human interviews (n =
298), structured observation (n = 137), and analysis of government,
non-governmental organization, or public records (n = 121; Table S2).
We identified differences in reporting of human-wildlife interactions
within all four categories. Human-wildlife interactions were reported in
96 countries, including all continents except Antarctica, with most
reported in Asia and Africa and the least reported in Europe and Oceania
(χ2 = 246.27, df = 5, P < 0.001; Figure 2).
Agriculturalization was the most
common LUC type reported (n = 407), followed by urbanization (n = 285),
and restoration (χ2 = 37.805, df = 2, P <
0.001; n = 263). Human and domestic animals interacted with wildlife
belonging to 50 distinct orders and 1,021 species, all of which fall
into amphibians, birds, mammals, and reptiles (Classes: Amphibia, Aves,
Mammalia, and Reptilia, respectively). Human interactions with mammals
were reported most often (n = 493), followed by birds (n = 74) and
reptiles (χ2 = 616.19, df = 2, P < 0.001; n
= 45). Wildlife was documented to interact with 21 different species of
domestic animal, with cows (n = 99), goats (n = 71), and sheep (n = 63)
most reported. Indirect contacts (i.e., human spatial proximity to
wildlife without physical contact) were the most common type of
human-wildlife interaction (n = 400), followed by domestic
animal-mediated contact (i.e., interactions between wildlife and
domestic animals; n = 223), and direct contact (i.e., physical contact
between a human and wildlife or their effluent; χ2 =
76.14, df = 2, P < 0.001; n = 219).
We identified interactions in four of the six possible pair-wise
variable combinations.
We
detected an interactive effect between wildlife taxa and interaction
type (χ2 = 11.281, df = 4, P = 0.023).
Interaction type did not vary
within birds or mammals; however, within reptiles, direct and domestic
animal-mediated interactions were significantly more common than
indirect interactions (P = 0.029). There was an interactive effect
between LUC type and continent (χ2 = 34.672, df = 10,
P < 0.001), with reported interactions in areas of
urbanization in Oceania and North America more common than in other LUC
types on these continents (P ≥ 0.001; Figures 3 and 4). We detected an
interactive effect between wildlife taxa and continent
(χ2 = 18.456, df = 10, P = 0.047), with reported
interactions with mammals in Asia more common than reports of
interactions with other taxa (P = 0.028). Lastly, there was an
interactive effect between continent and interaction type
(χ2 = 19.504, df = 10, P = 0.034), with reports of
direct interactions in Europe less common than other interaction types
(P = 0.038; Figure 5). There were no interactive effects between LUC
type and wildlife taxa (χ2 = 3.4819, df = 4, P = 0.481) or LUC type and
interaction type (χ2 = 6.733, df = 4, P = 0.151).
Order diversity within taxa varied considerably. Humans and domestic
animals interacted with one amphibian, 27 avian, 19 mammalian, and three
reptilian orders (Figure 6). Our
analysis of the number of publications reporting interactions with
different taxa varied among avian (χ2 = 14.44, df = 6,
P = 0.025), mammalian (χ2 = 829.45, df = 10, P
< 0.001) and reptilian (χ2 = 17.10, df = 2,
P < 0.001) orders. Across avian orders, only Galliformes were
reported more often than expected, with all other orders
(Accipitriformes, Anseriformes, Charadriiformes, Columbiformes,
Passeriformes, and Psittaciformes) reported as often as expected (P =
0.025). Within Mammalia, the orders Artiodactyla, Carnivora, Primates,
and Rodentia were reported more often in the literature than expected;
the orders Chiroptera, Cingulata, Didelphimorphia, Lagomorpha,
Perissodactyla, and Pholidota were reported less often than expected;
and the order Proboscidea was reported as often as expected (P
< 0.001). Within Reptilia, the order Crocodilia was reported
more often than expected, the order Testudines was reported less often
than expected, and the order Squamata was reported as expected (P =
0.002).
Discussion
This study is the first to characterize publication trends documenting
human and domestic animal exposure to wildlife in the context of LUC. We
show that human-wildlife interactions are most often reported in Asia
and Africa and are most commonly associated with agriculturalization on
a global scale but with urbanization in North America and Oceania,
specifically. Humans and domestic animals interacted with over 1000
species of wildlife, but interactions with mammals were documented most
often, particularly with members of the Artiodactyla (even-toed
ungulates), Carnivora, Primate, and Rodentia orders. Interaction type
varied among continent and wildlife taxa, which has important
implications for the risk of zoonotic pathogen spillover following
wildlife exposure. We further focus on understanding how these
geographic and taxonomic hotspots of human-wildlife interactions relate
to known zoonotic disease emergence and identify areas for future
research that will facilitate a comprehensive picture of zoonotic
disease risk.