Wildlife Monitoring at Chiricahua National Monument

Bobcat (Lynx rufus). NPS photo.

Overview

At National Park Service units across the Sonoran Desert and Apache Highlands, the Sonoran Desert Network (SODN) is monitoring medium- and large-sized mammals. The goal of this project, started in 2016, is to detect biologically significant changes in mammal community and population parameters through time. The intent is to provide park managers with reliable, useful information on mammal species at various spatial and temporal scales. To do this, we use passively triggered remote wildlife cameras in concert with methods of sampling and analysis that address management needs.

Key points

• In 2022, field crews deployed 41 wildlife cameras that recorded 10 different mammal species at Chiricahua National Monument.
• Knowing how wildlife populations change through time, and how different variables impact them, helps managers decide how best to protect species now and into the future.
• Single-year data provide species-specific insights.

In 2022, SODN field crews deployed 41 wildlife cameras at Chiricahua National Monument (NM). The cameras recorded 734 total detections (animal photographs) from May 16 until June 27. Upon analysis, the photos revealed 10 mammal species plus an additional two mammalian families or genera that could not be identified to species due to insufficient visual evidence. Five bird species were also detected, along with 8 reptiles and 27 birds that could not be identified to species.

Investigating how wildlife populations change through time, and how different variables impact them, gives us valuable insight into how best to manage and protect species now and into the future. While this report only summarizes findings from 2022, those findings will be combined with data collected from past and future years for use in occupancy modeling. Occupancy modeling provides SODN and park managers with multi-year information on the status and trends of mammal species, including their population distribution and stability.

At least five years of pooled data are needed to establish reliable multi-year occupancy models. The methods for multi-year trend analysis are currently in development, as we have just obtained enough data to begin. However, to provide a sense of what can be learned from this modeling, an example of an occupancy model for a single species using only the 2022 data appears below. Single-year data are also useful for other species-specific insights, such as new detections within the monument and potential drivers of species distributions.

Background

Motivation for wildlife monitoring

The need for wide-scale environmental monitoring to meet conservation needs is increasing. In response to anthropogenic disturbances, such as habitat loss and climate change, many species face pressures that force shifts in their distribution. Expansive tracts of suitable habitat to allow for such shifts are a conservation necessity (Parmesan and Yohe 2003, Thomas et al. 2004). Large, protected areas, such as national parks, can provide critical wildlife habitat—but even they are not immune to anthropogenic disturbances (Brashares 2010, Carroll 2010, Chen et al. 2022). Knowing how species respond to various pressures requires an understanding of abiotic and multi-species interactions at broad spatial scales. Occupancy modeling of data from remote cameras can help meet this challenge (Post et al. 2009).

Use of cameras

Wildlife camera deployed at Chiricahua National Monument.

Remote wildlife cameras are powerful research tools. They are non-invasive and allow researchers to collect data on multiple species simultaneously over large spatial scales. Scientists worldwide have demonstrated the ability of remote cameras to provide robust trend-monitoring data for terrestrial mammals. Remote cameras can capture data on individual animals that are uniquely identifiable, such as jaguars, which enables trend estimations of their broader population through mark-recapture modeling (Steenweg et al. 2015). However, many animals without unique pelage (fur markings and patterns) can only be recognized to the species level, rendering mark-recapture modeling impossible. Instead, occupancy modeling provides a means to monitor distribution (occupancy) trends of numerous species concurrently.

What is occupancy modeling?

In SODN’s monitoring, occupancy is the proportion of an area that is inhabited (occupied) by a species. When a species is detected by a camera, it is classified as present. If a species is not detected by a camera, it may be either truly absent, or present but undetected. This issue of imperfect surveys (i.e., when the probability of detecting a species is less than 1) can be statistically accounted for with occupancy modeling. A rigorous framework for occupancy modeling has existed since 2002 (MacKenzie et al. 2002) and is in strong subsequent development (MacKenzie et al. 2006, 2018). Therefore, we utilize occupancy modeling for the data collected by wildlife cameras at Chiricahua NM and other parks.

While the data tell us where a species occurs, they do not tell us why a species does or does not occur at a particular location. We can assess the driving contributors to a species’ distribution by evaluating the effects of covariates on occupancy and detection probability. Some environmental influences, such as elevation, do not change during a sampling window and likely influence occupancy probability. Other environmental variables, such as temperature or precipitation, change day-to-day and may influence detection probability (e.g., species may be harder to see and less active in excessive heat). By including covariates, we can evaluate their influence on wildlife occupancy and detection probability and begin to understand why a species is detected at one place and/or time but not another.

Methods

Effort

In May 2022, SODN field crews deployed 41 Cuddeback G Series wildlife cameras at non-baited, pre-established monitoring locations throughout Chiricahua NM. The cameras have been deployed in the same locations annually since 2017 (except for 2020, due to the COVID-19 pandemic). We chose to pair our camera locations with SODN’s long-term vegetation and soil monitoring plots, which were allocated mostly through a Reversed Randomized Quadrant-Recursive Raster (RRQRR) spatially balanced design (Theobald et al. 2007, Hubbard et al. 2012). This pairing will allow us to apply detailed covariate data on vegetation species composition and structural characteristics to our wildlife data—useful because vegetation covariates are potentially important drivers of habitat conditions impacting wildlife.

Technicians followed standard operating procedures to ensure the cameras were consistently deployed (Hubbard et al. in prep). For example, the cameras were placed in the same locations and employed the same settings as in prior years. The cameras were either mounted on a stake in the ground or strapped to a secure structure, such as a tree.

Relying on infrared and motion-trigger technology, the cameras took a photo whenever they detected motion or a sudden temperature change in their detection zone. They collected data from May 16 until June 27, 2022, and were then retrieved. Technicians again followed standard operating procedures to ensure the cameras were retrieved consistently (Hubbard et al. in prep), including documenting each camera’s functionality and properly removing and storing their memory cards.

Back at the office, technicians analyzed the contents of the cameras’ memory cards. This process begins by downloading the photos and sorting them to remove “false triggers,” which are photos that do not contain an animal. A photo that does contain an animal is classified as a “detection.” From there, the dataset is further sorted using the Colorado Parks and Wildlife Photo Warehouse Database, which enables us to categorize the photos and attribute spatial, temporal, and species metadata to them (Ivan and Newkirk 2016). Finally, a metadata export is produced for use in occupancy analysis.

Single-species single-season occupancy models

Many large-scale wildlife occupancy trend models require data from consecutive years, which renders data from a single sampling period inadequate for trend analysis. However, the terrestrial mammal data we collect from a single field season can still be informative and incorporated into different occupancy models that provide information on specific species or covariates of interest.

As an example, we modeled occupancy using a single-season, single-species approach (MacKenzie et al. 2002, 2006) with package “unmarked” in R (Fiske and Chandler 2011). To minimize heterogeneity caused by short periods of high activity in front of the cameras, we grouped our 2022 detection data into seven-day sampling periods (Bowler et al. 2017, MacKenzie et al. 2018, Kays et al. 2020). We then selected nine target species for analysis based on two criteria: rarity and body size. We deemed terrestrial mammal species reliably detectable and identifiable by cameras if they were at least 43 centimeters long, which is equivalent to the size of a rock squirrel (Spermophilus variegatus). Smaller animals can be difficult to identify correctly through photographs, especially nocturnal animals with similar characteristics. For this reason, we excluded smaller rodents, such as chipmunks, rats, and mice from analysis. Furthermore, we only included species for analysis that were detected at least once on >30% of sampled sites to minimize statistical modeling errors. This excluded rarer species, such as western spotted skunk (Spilogale gracilis).

Because we had little initial information about which covariates might influence occupancy and detection probabilities, we chose backwards elimination over an information-theoretic approach (Steidl 2006, 2007) to identify covariates with explanatory power (Ramsey and Schafer 2013). We first identified covariates that influenced detection probability with an intercept-only model, then used those in models for occupancy, retaining only those covariates with some explanatory power (p <0.10).

Field crews deployed 41 wildlife cameras at the monument in 2022.

Results for 2022

Detections

During the 2022 sampling window (May 16–June 27), 41 wildlife cameras recorded 734 total detections (i.e., animal photographs) at Chiricahua NM, including 646 detections of mammals identified to 10 species (see table), and 10 detections identified to genus or family. Several animals, detected 10 times, exhibited clear mammalian characteristics but could not be speciated further due to insufficient visual evidence. Five bird species were also detected, along with 8 reptiles and 27 birds that could not be identified to species.

Notable detections included western spotted skunk (Spilogale gracilis) and zone-tailed hawk (Buteo albonotatus). It is uncommon to see western spotted skunks within the monument, so we were intrigued to have two detections. The zone-tailed hawk was captured by accident, after the camera was knocked over during the deployment window. While it is common to observe these birds in flight within the monument, it is uncommon to detect them on our wildlife cameras.

The number of statistically significant wildlife photos (i.e., photos containing an animal) collected from sampling at Chiricahua NM has ranged from 734 to 4,293 annually. It is hard to know exactly why this range has varied throughout the years. We are hoping to gain insight into this question and others by assessing the impacts of environmental factors on mammal distribution and detectability via occupancy modeling. We currently have five years of data (2017–2022, excluding 2020 due to the COVID-19 pandemic).

Citizen scientists typically assist with the fieldwork associated with camera deployment and retrieval. In 2022, we had one volunteer and seven interns from various parks and partner organizations help deploy and retrieve cameras in Chiricahua NM. We are grateful for their support.

Wildlife detections (photographs) collected from 41 remote cameras at Chiricahua National Monument, May 16–June 27, 2022.

Class	Common name	Scientific name	Number of detections
Mammal	White-tailed deer	Odocoileus virginianus	448
Mammal	Gray fox	Urocyon cinereoargenteus	72
Mammal	American black bear	Ursus americanus	70
Mammal	Rock squirrel	Spermophilus variegatus	21
Mammal	White-nosed coati	Nasua narica	12
Mammal	Bobcat	Lynx rufus	9
Mammal	Cliff chipmunk	Tamias dorsalis	6
Mammal	Hog-nosed skunk	Conepatus leuconotus	5
Mammal	Western spotted skunk	Spilogale gracilis	2
Mammal	White-throated woodrat	Neotoma albigula	1
Mammal	Unknown cottontail	Sylvilagus sp.	8
Mammal	Unknown skunk	Mephitidae	2
Mammal	Unknown mammal	Mammalia	10
Total mammals	–	–	666
Bird	Common raven	Corvus corax	2
Bird	Gilded flicker	Colaptes chrysoides	3
Bird	Turkey vulture	Cathartes aura	10
Bird	Wild turkey	Meleagris gallopavo	12
Bird	Zone-tailed hawk	Buteo albonotatus	6
Bird	Unknown bird	Aves	27
Reptile	unknown reptile	Reptilia	8
Total non-mammals	–	–	68
Total	–	–	734

Why is this Information Useful?

Long-term monitoring allows us to evaluate trends in parameters of management interest. Modeling occupancy over several years gives us occupancy estimates for a species/population of interest that can be plotted into a trend. Occupancy is often used as a surrogate for abundance (MacKenzie et al. 2018), so pooling annual occupancy estimates can help us determine how stable or unstable a wildlife population is. When analyzed with occupancy models, SODN’s photographic dataset enables us to monitor trends of numerous terrestrial mammal species, including whether they are potentially increasing, stable, or decreasing.

Depending on the covariates used in the modeling process, we can also plot trends of covariate influence on wildlife occupancy and detection probabilities. For example, assessing the influence of temperature and precipitation on wildlife occupancy and detection is helpful when we want to understand the potential impacts of climate change on species/populations of interest.

Single-season, single-species analysis example

Below is an output of a single-season, single-species occupancy model for white-tailed deer (Odocoileus virginianus) in Chiricahua NM, based on data collected in 2022. This model illustrates the significant influence of elevation on the occupancy probability of white-tailed deer. More specifically, it shows a negative relationship between these two parameters, with the occupancy of white-tailed deer decreasing as elevation increases. In the lower-elevation areas of Chiricahua NM, occupancy probability of white-tailed deer is high (80–95% occupied). However, the occupancy probability of white-tailed deer drops significantly (to less than 25%) in high-elevation areas (above 2,100 meters) within the monument.

The model suggests white-tailed deer prefer to inhabit lower-elevation areas over higher-elevation areas in Chiricahua NM. This is likely because lower-elevation areas provide easier access to food, water, and cover compared to higher-elevation areas that are more barren, rugged, and steep. This outcome provides park managers with insight into how white-tailed deer use different areas of the monument. The model also illustrates how data from a single sampling period can provide valuable information.

Occupancy probability of white-tailed deer in response to elevation at Chiricahua National Monument in 2022. Occupancy probability decreases with increasing elevation within the monument. The average is illustrated in green and the standard error is illustrated in grey.

Literature Cited

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Past Findings

Wildlife Monitoring at Chiricahua National Monument, 2021

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This report was prepared by Elise Dillingham and Alex Buckisch, Sonoran Desert Network.

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