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Title: Agricultural encroachment and lack of enforcementthreaten connectivity of range-wide jaguar (Panthera onca)corridor
Author: Lisanne S. Petracca Sandra Hernandez-PotosmeLenin Obando-Sampson Roberto Salom-Perez HowardQuigley Hugh S. Robinson
PII: S1617-1381(14)00051-XDOI: http://dx.doi.org/doi:10.1016/j.jnc.2014.04.002Reference: JNC 25354
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Received date: 7-11-2013Revised date: 1-4-2014Accepted date: 17-4-2014
Please cite this article as: Petracca, L. S., Hernandez-Potosme, S., Obando-Sampson, L.,Salom-Perez, R., Quigley, H., & Robinson, H. S.,Agricultural encroachment and lackof enforcement threaten connectivity of range-wide jaguar (Panthera onca) corridor,Journal for Nature Conservation (2014), http://dx.doi.org/10.1016/j.jnc.2014.04.002
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Agricultural encroachment and lack of enforcement threaten connectivity of range-wide jaguar
(Panthera onca) corridor
Lisanne S. Petracca a,b
Sandra Hernández-Potosme a [email protected]
Lenin Obando-Sampson a [email protected]
Roberto Salom-Pérez a [email protected]
Howard Quigley a [email protected]
Hugh S. Robinson a [email protected]
a Panthera, 8 West 40th Street, 18th Floor, New York, NY 10018, U.S.A.
b Corresponding author; e-mail: [email protected] +016467860408
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Abstract
The purpose of protected areas is to safeguard natural habitats and their biodiversity. However, few
protected areas are large enough for the long-term conservation of resident species, making the
landscapes surrounding protected areas equally important for species conservation. This is especially true
for wide-ranging carnivores such as the jaguar (Panthera onca), for which conservation strategies stress
the maintenance of range-wide habitat connectivity via protected areas and dispersal corridors. We used
587 interviews with local people in 441 36-km2 sampling units to assess the presence of jaguar and six
prey species in a critical corridor in Central America, linking Bosawas Biosphere Reserve and Wawashan
Nature Reserve in Nicaragua. We analyzed these data using single-season false-positive detection models
in a site occupancy framework. We found that agricultural encroachment, particularly pervasive within a
protected area, was the main factor limiting the presence of jaguar and three large-bodied prey species:
white-lipped peccary (Tayassu pecari), collared peccary (Pecari tajacu), and red brocket deer (Mazama
americana). We suggest that improperly-managed protected areas can suffer the loss of apex predators
and their prey, thereby jeopardizing range-wide conservation strategies for threatened species.
Keywords: agriculture; habitat use; interviews; Nicaragua; occupancy; prey.
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Introduction
The jaguar (Panthera onca) has been extirpated from 40% of its historic range (Sanderson et al.,
2002) and is listed as Near Threatened on the IUCN’s Red List of Endangered Species (Caso et al., 2008).
Across their range, which extends from northern Mexico to northern Argentina (Sunquist & Sunquist,
2002), jaguars are currently threatened by habitat loss due to deforestation, loss of prey to human hunting,
and retaliatory killing in response to livestock loss (Sunquist & Sunquist, 2002; Swank & Teer, 1989).
Despite these threats, analyses of jaguar mitochondrial DNA and microsatellite loci revealed an absence
of subspecific partitioning across their geographic range, suggesting that large-scale connectivity of
jaguar populations was still intact at the turn of the century (Eizirik et al., 2001; Ruiz-Garcia, Payan,
Murillo, & Alvarez, 2006).
Wildlife corridors are a means of preserving connectivity across a species’ range, allowing for
dispersal of individuals and genetic exchange (Beier & Noss, 1998; Noss, 1987). In the absence of
corridors, habitat fragmentation and a lack of immigration from dispersing individuals can lead to
inbreeding depression and a reduction in adaptive fitness (MacArthur & Wilson, 1967; Westemeier et al.,
1998). Wildlife corridors have been proposed in conservation strategies for species as diverse as
songbirds (Machtans, Villard, & Hannon, 1996), elephants (Bouche et al., 2011), and pronghorn antelope
(Poor, Loucks, Jakes, & Urban, 2012).
The concept of a large-scale jaguar corridor was proposed by Sanderson et al. (2002) and
expanded upon by Rabinowitz and Zeller (2010), who used a GIS-based least-cost path analysis to
identify 182 jaguar corridors connecting 90 known jaguar populations across jaguar range. The longest
corridor identified by Rabinowitz and Zeller (2010) in Central America is along the east coast of
Nicaragua and connects Bosawas Biosphere Reserve and Wawashan Nature Reserve (Fig. 1), serving as a
critical link in the maintenance of jaguar habitat connectivity.
Following a 2006 survey of jaguar experts, Wawashan was designated a Jaguar Conservation
Unit (JCU) (Zeller, 2007), defined as having a jaguar population of at least 50 breeding individuals or
adequate habitat and prey numbers to eventually support this number of jaguars (Sanderson et al., 2002).
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However, its designation was unclear following a longitudinal analysis of satellite imagery, which
revealed substantial forest loss and fragmentation within its boundaries (Fig. 2).
Peres and Terborgh (1995) noted that limited funding of certain governments leads to protected
area systems with low budgets, chronic understaffing, a lack of infrastructure and institutional support,
and susceptibility to illegal activities such as hunting, logging, and land clearing. An analysis of 60
protected areas in the tropics revealed that about half are experiencing eroding biodiversity across all
taxonomic levels, with habitat disruption, hunting, and exploitation of forest products the strongest
predictors of declining reserve health (Laurance et al., 2012). A similar analysis of 16 protected areas in
Africa found that most were “seriously compromised" by a deficiency of funding and governmental
support (Struhsaker, Struhsaker, & Siex, 2005). In light of this research, however, the extent to which
ineffective protected area management affects the presence of the ecosystem’s top predators is unclear.
Jaguars are elusive animals that are difficult to detect in the wild (Sollmann et al., 2011).
Common methods of surveying jaguars include spoor transects (Schaller & Crawshaw, 1980), camera-
trapping (Maffei, Cuellar, & Noss, 2004; Silver et al., 2004), and GPS telemetry (Soisalo & Cavalcanti,
2006). However, implementation of these methods may be difficult when surveying large areas where
equipment and personnel costs can limit their feasibility.
Zeller et al. (2011) described and demonstrated an approach that addressed the limitations of
large-scale carnivore and prey survey methods using interviews with local people to gather
presence/absence data. Interview data was analyzed within a site occupancy modeling framework, using
detection/non-detection data to estimate likelihood of species’ habitat use while compensating for
imperfect detection (MacKenzie et al., 2002; MacKenzie et al., 2006). Repeated surveys of sampling units
were used to generate detection histories for the species of interest (MacKenzie et al., 2006); by using
interviews with local people as separate surveys in each sampling unit, Zeller et al.’s (2011) method was
considered a cost and time-effective approach to modeling species’ habitat use over a large area.
Due to the rapid land use change currently occurring along the eastern coast of Nicaragua and the
paucity of scientific inquiry in the area, we considered it a priority to assess this section of jaguar corridor.
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More specifically, we wished to investigate: (1) variation in jaguar and prey habitat use across a disturbed
landscape; (2) how these patterns affected greater landscape connectivity; and (3) whether the
deforestation observed within Wawashan Nature Reserve had affected the presence of mammal species.
Material and methods
Study area
Our study area (15,876 km2) was a 240-km-long corridor modeled by Rabinowitz and Zeller
(2010) along the eastern coast of Nicaragua, from the eastern boundary of Bosawas Biosphere Reserve to
the southern boundary of Wawashan Nature Reserve (Fig. 3). In this analysis, Wawashan Nature Reserve
is considered part of the corridor due to the degree of deforestation and agricultural spread within the
reserve. Elevation ranges from -20 to 948 meters asl, with highest elevations in the northwestern and
southwestern sections of the study area. The primary land cover types are primary/mature forest
(55.77%), lowland pine savanna (22.35%), and secondary/disturbed forest (13.13%). Agriculture and
pasturelands (5.02% of the landscape) were present within Wawashan Nature Reserve and along river
courses. Most agricultural plots were derived from clear-cutting forest and are devoted to the growing of
beans, cassava, corn, and rice for subsistence purposes. According to census data from 2001, there are
110 settlements in the study area, with a total population of approximately 46,000 (median settlement size
= 161 persons) (INEC, 2001). There are seven additional nature reserves (IUCN category IV) present
within the study area (IUCN & UNEP, 2012), ranging from 18 to 222 km2 (Fig. 3).
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Sampling design
We divided the study area into 441 6x6 km sampling units. The 36-km2 sampling unit size
represents the minimum estimated jaguar home range in this region (Ceballos et al., 2002; Rabinowitz &
Nottingham, 1986). We sought to collect 6 interviews per sampling unit.
Interview protocol
We conducted structured interviews with local people who were knowledgeable about wildlife in
the area, such as hunters or farmers who walk long distances to their farms. Interviewees were asked
about species presence in a specific area of knowledge, composed of one or more sampling units.
Interviewees were required to have known each sampling unit for at least one year and to have visited that
area at least twice per month over that period. Interviews were conducted on a sampling unit basis,
meaning that the interview had to be repeated to gather data for each sampling unit in the interviewee’s
area of knowledge.
Our interview protocol (see Appendix) was designed to gather detection histories for the jaguar
and six common prey species: white-lipped peccary (Tayassu pecari), collared peccary (Pecari tajacu),
red brocket deer (Mazama americana), white-tailed deer (Odocoileus virginianus), spotted paca (Agouti
paca), and Central American agouti (Dasyprocta punctata) (Novack, Main, Sunquist, & Labisky, 2005;
Weckel, Giuliano, & Silver, 2006). We inquired only about detections that had occurred within the last
year.
Acceptable detections included direct sightings of tracks or of a live or dead animal. Following a
verbal description of what the interviewee had seen, we used cards with pictures of all the animals, and
tracks of jaguar and puma, to confirm the species was identified correctly. In an attempt to reduce false
positives, data was discarded from the analysis if there was any doubt concerning the validity of the
interviewee’s response (i.e. a reported inability to distinguish between similar-looking tracks, such as
those of the jaguar and puma). We then collected data on the frequency of prey species sightings. If a
species was reported absent (not observed) or rarely observed (1-5 times per year), we inquired as to why
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the species was not observed more frequently. These responses would provide a local perspective on
threats and population trends of the jaguar’s main prey species.
The final interview section collected data about the interviewees themselves. These data were
incorporated as variables affecting detection (p) and included percentage of the year spent in a sampling
unit (Effort), number of years of familiarity with a sampling unit (Residence), and their reason for
entering a sampling unit (responses relevant to this analysis, Hunter and Farmer, were coded as dummy
variables). Additional data on personal perceptions of jaguars were used to supplement the quantitative
analysis.
Habitat use covariates
We created a land cover classification of 30-m resolution based on an unsupervised classification
of nine Landsat images across the study area. Recent imagery (2008-2010) was chosen to reflect the
impacts of Hurricane Felix in August 2007. These images were characterized by the striping pattern that
has affected all Landsat 7 images post-May 2003 due to a faulty scan line corrector, which necessitated
use of older Landsat 5 and Landsat 7 imagery (1995-2001) to fill in the missing data. The final land cover
map consisted of seven classes: water, primary/mature forest, secondary/disturbed forest, lowland pine
savanna, wetland, agriculture, and urban.
We obtained elevation data from the SRTM 90m v. 4.0 database (Jarvis, Reuter, Nelson, &
Guevara, 2008) and protected area boundaries from the World Database on Protected Areas (IUCN &
UNEP, 2012). Our settlement layer was derived from Nicaragua’s National Institute of Statistics and
Census (INEC, 2001). Layers for roads and rivers were from Nicaragua’s Ministry of the Environment
and Natural Resources (MARENA, 2007).
One of the assumptions of occupancy modeling is that all sites are closed to changes in
occupancy during the survey season (MacKenzie et al., 2006). MacKenzie et al. (2004) noted that this
assumption could be relaxed so long as changes in occupancy were random, but that interpretation of
model output psi ( ) would change from probability of site “occupancy” to probability of site “use” by
the target species. We therefore interpreted model output as “likelihood of habitat use.” We hypothesized
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that habitat use would be affected by ten landscape covariates, and that detection would be affected by
four interviewee-specific covariates (Table 1). All spatial data was compiled using ArcMap v. 10.0
(ESRI, 2010).
Occupancy modeling
This paper extends the approach of Zeller et al. (2011) to account for false positives, thereby
relaxing the assumption that detections do not occur when a species does not occupy a site (Royle &
Link, 2006). Failure to account for false positives, which are common in datasets identifying anuran
(Miller et al., 2012) and avian (Farmer, Leonard, & Horn, 2012) species from auditory cues, has been
demonstrated to bias occupancy estimates (McClintock, Bailey, Pollock, & Simons, 2010; Royle & Link,
2006). This approach is also appropriate for an interview-based framework, in which the veracity of
responses cannot be guaranteed.
We analyzed the interview data in single-season false-positive detection models (Miller et al.,
2012) using program Presence v.5.8 (Hines, 2010). We assigned as “uncertain” any singular positive
detection in a site, as those detections could not be confirmed by another human observer. We assigned as
“certain” detections in a site that numbered two or greater, meaning that more than one person had
detected the target species in that site.
Model-building
We created a list of biologically relevant candidate variables based on a priori hypotheses as to
how each would affect species habitat use or detection (Table 1). We tested each covariate univariately
and retained only those that were statistically significant (p <= 0.05), non-confounded, and uncorrelated
(|r| < 0.5) (Hosmer & Lemeshow, 2000). This list of retained variables was considered our final candidate
variable set. We then combined these candidate variables in a number of competing models, using
Akaike’s Information Criterion with the small-sample correction (AICc) to select the model that best
explained variation in species detection and habitat use (delta AICc < 2.0) (Burnham & Anderson, 2002).
We modeled detection covariates first, and then held our top detection covariates constant while modeling
habitat use.
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In addition to modeling likelihood of habitat use in our surveyed cells, we projected the top
model of each species in ArcMap to estimate the likelihood of habitat use in sampling units where
logistical constraints prevented data collection.
In order to obtain estimates of concurrent habitat use by more than one prey species in a given
sampling unit, we used a simple probability statement ( PREY 1 * PREY 2 * … PREY n) to express this
likelihood. The jaguar was then added to the probability statement ( JAGUAR * PREY 1 * PREY 2 * … PREY
n) to estimate the likelihood of habitat use by jaguar and each prey species in the sampling units.
Results
Interviewee data
We conducted 587 interviews in five corridor sections: north (April to August 2010), central-
northeast (October 2010 to January 2011), central-southwest (June to October 2011), south (November
2011 to April 2012), and Wawashan (October to December 2012). Due to a lack of accessibility in some
areas, data were collected in 313 of 441 sampling units (70.98%). Within the sampled cells, there was an
average of 5.84 surveys collected per unit.
Interviewees were between 14 and 88 years old, with a mean age of 37.47 years. All but two
interviewees were male. Interviewees spent an average of 121 days of the year (33%) in the sampling
units and had an average of 16.57 years’ worth of knowledge about these areas. Their main purposes in
the study area were farming (38% of interviewees), visiting/passing through (16%), and hunting (14%).
Habitat use models
Agriculture was the main factor limiting habitat use for jaguars, white-lipped peccaries, collared
peccaries, and red brocket deer (Table 2). In addition, habitat use of the white-lipped peccary was
associated with increasing distance from settlements, while collared peccary habitat use was associated
with increased area of secondary/disturbed forest.
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Covariates affecting detection for each species were effort (percentage of year spent in sampling
unit) for the jaguar, residence (number of years of familiarity with the sampling unit) for the jaguar and
two peccary species, hunter (if the interviewee was a hunter) for the three prey species, and farmer (if the
interviewee was a farmer) for the collared peccary and red brocket deer (Table 2).
Likely due to the high naive occupancy (proportion of sampling units with at least one detection)
of the agouti (0.98), paca (0.98), and white-tailed deer (0.97), there were no significant covariates to
explain variation in habitat use. These species were therefore considered ubiquitous in the corridor. It
should be noted that while the red brocket deer also had a high naïve occupancy (0.97), a significant
negative association emerged between agriculture and habitat use that was not observed in the other
species.
We created maps of likelihood of habitat use for: (1) jaguar ( jag); (2) all three modeled prey
species ( WLP * CLP * RBD); and (3) jaguar and all three modeled prey species ( jag * WLP * CLP *
RBD) (Fig. 4). The latter estimate was then compared to the extent of agriculture, which highlights two
areas with both agricultural presence and low probability of jaguar and prey habitat use (Fig. 5).
Local perceptions
The most common responses to explain low encounter rates for some prey species were hunting
(28% of responses), wrong habitat for the species (19%), and deforestation (17%) (Table 3). While some
responses were general to all prey species, the white-lipped peccary was the most-referenced species in
more detailed responses (33% of all responses and 63% of responses referencing a particular species), and
was reported to be in decline across the corridor.
Most attitudes towards jaguars were negative (65% of responses), with the most common
responses being fear (29%) and a desire to kill a jaguar (26%) (Table 4). Only 14% of responses were
positive, and expressed desires to leave jaguars alone and protect them as threatened species.
Discussion
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Our results suggest that the presence of agriculture is significantly limiting habitat use by the
jaguar, white-lipped peccary, collared peccary, and red brocket deer in a critical section of jaguar
corridor. Agricultural expansion has essentially created two blocks of low probability of habitat use by
these four species, one of which lies entirely within Wawashan Nature Reserve (Fig. 5). If unchecked
agricultural expansion continues in the study area, the long-term persistence of the jaguar and its large-
bodied prey species cannot be assured.
The relationship between increased development/agriculture and reduced habitat use by jaguars
and several of their prey species has been documented elsewhere (Altrichter & Boaglio, 2004; Foster,
Harmsen, & Doncaster, 2010; Tejeda-Cruz, Naranjo, Cuaron, Perales, & Cruz-Burguete, 2009). The
association between white-lipped peccary presence and increasing distance from settlements, and collared
peccary presence and greater proportion of secondary/disturbed forest, have also been recognized
(Altrichter & Boaglio, 2004; Tejeda-Cruz et al., 2009). In fact, the sensitivity to anthropogenic
disturbance characteristic of white-lipped peccary was recently acknowledged by the IUCN Red List,
which upgraded the species’ population status from “Near Threatened” to “Vulnerable;” habitat loss and
illegal hunting were cited as major threats to species persistence (Keuroghlian et al., 2013). Unlike their
white-lipped counterparts, collared peccary have been shown to take advantage of human crops (Perez &
Pacheco, 2006; Romero-Balderas, Naranjo, Morales, & Nigh, 2006), though our findings contradict these
earlier studies.
Of greatest concern in our study area is the spread of agriculture within Wawashan despite its
status as a protected area. In 2006, this area was declared a Type I JCU, a designation given to areas with
a stable prey base and enough resident jaguars (at least 50 breeding individuals) to be potentially self-
sustaining over the next 100 years (Zeller, 2007). However, analysis of 2010 satellite imagery revealed
rampant deforestation within protected area boundaries (Fig. 2). The 2004 management plan for
Wawashan designated a core area of 345 km2 within the reserve (3,500 km2) along the Wawashan River
and extending to the Kurinwas River to the north, in which human settlement and resource extraction was
specifically prohibited (MARENA, 2004). However, satellite imagery reveals obvious human impact in
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this core area, and field staff reported that much of this zone has been deforested due to the agricultural
conversion of areas where trees had been felled by Hurricane Felix in 2007 and then extracted (S.
Hernandez, pers. obs.).
In a report for the U.S. Agency for International Development (USAID), Weaver et al. (2003)
stated that the protected areas system of Nicaragua is not well-protected, and that subsequently many of
the country’s protected areas have less than 50% forest cover. They noted that undefined and/or
unpatrolled boundaries, few forest guards, and illegal hunting and extraction of forest materials are
“commonplace.” There is only one park ranger currently employed in Wawashan; Bruner et al. (2001)
found that national park effectiveness was most strongly correlated with guard density, so the
employment of additional park guards within Wawashan could have far-reaching impacts on the
protection of biodiversity. Laurance et al. (2012) found that reserves in the African, American, and Asia-
Pacific tropics where monitoring and protection had increased over the past 20-30 years generally fared
better than those where protection had decreased, also stressing the importance of on-the-ground
enforcement.
Hunting was present throughout the study area; in fact, detection probabilities of the three prey
species were higher if the interviewee was a hunter. The white-lipped peccary, collared peccary, and red
brocket deer are commonly-hunted species in the region (Carrillo, Wong, & Cuaron, 2000), and all three
species were less abundant in hunted versus non-hunted areas in Costa Rica (Carrillo et al., 2000) and
Guatemala (Novack et al., 2005). Although hunting intensity could not be ascertained by the present
study, 14% of interviewees admitted to hunting (due to illegal hunting activity, this percentage is likely
higher), and hunting was the most common response as to why prey were not seen more frequently (Table
3). We recommend further investigation into prey abundance and local hunting pressure in efforts to
avoid the “empty forest” characteristic of overhunted areas (Redford, 1992). Ensuring a stable prey base
could also reduce human-jaguar conflict, as jaguars may prey increasingly upon domestic stock where
native species have been extirpated (Polisar et al., 2003; Quigley & Crawshaw, 1992).
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In addition to agricultural expansion and the likely overhunting of prey, a serious threat is the
predominantly negative perception that local people had toward jaguars (65% of total responses). A
common response was a desire to kill jaguars, noted by Zeller (2007) to be the most urgent threat
currently facing the species. Negative perceptions of jaguars and the existence of human-jaguar conflict
are long-standing concerns in jaguar conservation (Michalski, Boulhosa, Faria, & Peres, 2006; Quigley &
Crawshaw, 1992; Rabinowitz, 1986). If the local human population lives in fear and prefers the
extirpation of jaguars from the landscape, jaguar populations cannot be maintained even in the presence
of range-wide habitat connectivity.
A final concern specific to Wawashan is the land management of the resident indigenous people,
mainly Miskitos and Criole, who have land titles for the majority of the reserve. In the past, they had
subsisted on fishing and small-scale agriculture, but are now increasingly allowing outsiders to deforest
their lands in exchange for rent. This deforestation has forced jaguars to come into greater contact with
humans, which, in turn, has caused an increase in livestock depredation events and the retaliatory killing
of jaguars (S. Hernandez, pers. obs.). All of these factors threaten the connectivity of the range-wide
jaguar corridor that has become the nexus for jaguar conservation efforts.
Conclusions
We conclude that jaguars are present in a 240-km unprotected corridor along the eastern coast of
Nicaragua but are experiencing a decline in habitat use due to agricultural encroachment. The lack of
effective habitat protection within Wawashan Nature Reserve is especially alarming given its high level
of protected status within the Nicaraguan resource protection framework. There is mounting evidence that
jaguars are using habitat outside protected areas (Foster et al., 2010; Zeller et al., 2011) but the question is
to what degree jaguars can persist long-term in a mosaic of forest and agriculture, an increasingly
common landscape in corridor areas, and how they will respond as the balance tips in favor of agriculture
as the human population expands. Our results suggest that an improperly-managed protected area is
already experiencing a decline in the probability of habitat use of threatened species, a trend that is bound
to continue in the absence of immediate conservation action.
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Acknowledgments
We would like to thank the following for their efforts in the field: M. Valle, F. González (may he rest in
peace), A. Hernández, L. Mendoza, C. Salazar, S. Amador, L. Barleth, P. Ordoñez, G. and V. Hodgson, J.
Dixon, and the leaders of Kukrahill, Pearl Lagoon, and the rest of the communities in the corridor. In
addition, we are indebted to FADCANIC, MARENA, RAAN, and RAAS for their collaboration and
support. This study was designed and funded by Panthera.
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Tables
Table 1
Covariates used in occupancy models, with hypothesized effect of each covariate on detection or habitat
use. Detection covariates were interviewee-specific and predicted to affect likelihood of species detection
(p), while habitat use covariates were at the landscape level and predicted to affect likelihood of species
habitat use ( ).
Model process Covariate Description Hypothesized effect
Detection (p)
Effort
Percentage of the year the interviewee spent in the sampling unit
+
Residence
Number of years the interviewee was familiar with the sampling unit
+
Hunter
Coded as "1" if the interviewee was a hunter, "0" otherwise
+++
Farmer
Coded as "1" if the interviewee was a farmer, "0" otherwise
+
Habitat use ( ) Water Proportion of water in sampling unit
+
1_Forest
Proportion of primary/mature forest in sampling unit
+++
2_Forest
Proportion of secondary/disturbed forest in sampling unit
-
Agric
Proportion of agricultural plots and pasturelands in sampling unit
- - -
Urban
Proportion of urban area in sampling unit
- - -
DistPA
Average distance (km) to protected area from sampling unit pixels
- - -
DistVill
Average distance (km) to settlement from sampling unit pixels
+++
DistRoad
Average distance to road (km) from sampling unit pixels
+
DistRiver
Average distance to river (km) from sampling unit pixels
-
Elev
Average elevation (m) of sampling unit pixels
-
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Table 2
Top models and untransformed coefficients of covariates for the jaguar and three modeled prey species. All data were analyzed in single-season
false-positive detection models.
Untransformed coefficients of covariates (standard errors) Species
Models Naive
occupancya Kb Intercept agric distvill 2forest effort residence hunter farmer
Jaguar (agric),p(effort,residence) 0.65 7 0.48 (0.15)
-9.91 (2.05)
--- --- 0.55 (0.14)
0.02 (0.00)
--- ---
White-lipped peccary
(agric,distvill),p(residence,hunter) 0.69 8 0.00 (0.28)
-8.78 (2.03)
0.07 (0.03)
--- --- 0.01 (0.00)
0.64 (0.17)
Collared peccary
(agric,2forest),p(residence,hunter,farmer) 0.93 9 1.58 (0.22)
-10.36 (2.05)
--- 4.96 (1.64)
--- 0.01 (0.00)
1.57 (0.32)
0.53 (0.14)
Red brocket deer
(agric),p(hunter,farmer) 0.97 7 2.04 (0.20)
-2.61 (1.30)
--- --- --- --- 0.41 (0.20)
0.45 (0.14)
Covariates: agric, proportion of agriculture in sampling unit; effort, percentage of year spent by interviewee in sampling unit; residence, number of years interviewee was familiar with sampling unit; distvill, average distance to settlement from sampling unit pixels; hunter, if the interviewee was a hunter (yes/no); 2forest, proportion of secondary/disturbed forest in sampling unit; farmer, if the interviewee was a farmer (yes/no) a Proportion of sampling units in which species was detected. b Number of parameters in the model.
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Table 3
Reasons provided by interviewees as to why prey species were not seen more frequently. Main categories
are presented in grey and sum to 100%, while the top categories are separated into general comment (not
specific to any species), WLP (white-lipped peccary), and other species.
Reason Percentage HUNTING 27.66
General comment 13.57 WLP 9.45
Other species 4.64 WRONG HABITAT 19.07
General comment 3.61 WLP 9.62
Other species 5.84 DEFORESTATION 17.18
General comment 9.62 WLP 4.98
Other species 2.58 HURRICANE 11.34 HUMAN POPULATION INCREASE 9.97 THEY MOVED ON 7.39 THERE IS NO FOOD 2.58 FIRE 2.06 NOISE 1.37 OTHER 1.37 TOTAL 100.00
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Table 4
Interviewees’ personal feelings towards jaguars. Main categories are presented in grey and sum to 100%,
with each category separated into its main responses.
Response Percentage Negative 65.42 Fear. It will eat me. 28.82 Kill it. 25.87 It is a bad/dangerous animal. It kills livestock. 9.92 Other (Can sell the skin and meat, Nice as decoration) 0.80 Neutral 20.91 I am not scared of them. 11.93 I don't feel anything. 7.10 I would leave it alone if I didn't have my gun. 1.88 Positive 13.67 Leave them alone. They don't do anything. 5.63 We need to protect them. I like that they are in the forest. 2.41 I want to see one. 2.28 Don't kill it. 2.01 I respect them. They are brave/pretty/smart. 1.34 Total 100.00
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Figure legends FIG. 1 Locations of jaguar populations and modeled least-cost corridors in Central America from
Rabinowitz and Zeller (2010). Wawashan Nature Reserve was designated a Jaguar Conservation Unit
(JCU) in the original analysis, but was considered part of a corridor here due to its extent of forest loss.
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FIG. 2 False-color satellite imagery (30-m resolution) of Wawashan Nature Reserve over a 22-year
period. Dark red denotes forested areas, while light shades of red and green denote cleared forest and
agricultural lands. Figure is to remain in color.
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FIG. 3 Location of study area along the eastern coast of Nicaragua.
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FIG. 4 (A) Probability of the jaguar using habitat in each sampling unit. (B) Probability of all three prey
species (white-lipped and collared peccaries, red brocket deer) using habitat in each sampling unit. (C)
Probability of the jaguar and all three prey species using habitat in each sampling unit.
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FIG. 5 Probability of the jaguar and all three prey species using habitat in each sampling unit, compared
with the extent of agriculture. Circled units are areas of conservation concern. Figure is to remain in color.
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Figure5
