Our sampled farms were small , had conducted some form of clearing for production space, and three had constructed some form of fence or barrier around their crop. Nonetheless, specific land use practices and production philosophies differed between farms . We cannot disclose farm locations, as per our research agreement for access. Monitored farms were clustered within each watershed: one farm in Slate Creek, five in Lower Deer Creek, and two in Lower East Fork Illinois River; however, most farms were also located near other nearby cannabis farms that were not directly monitored in this study. We placed unbaited motion sensitive cameras on cannabis farms as well as in random locations up to 1.5 km from the monitored farms. This is an expansion on previous camera research that only assessed on-site wildlife at these same farms . We placed cameras approximately 0.5 m off the ground to capture animals squirrel-sized and larger. We set cameras to take bursts of 2 photos, with a quiet period of 15 seconds. To guide the placement of cameras, we overlaid the area surrounding each cannabis farm cluster with a 50 x 50 m grid and then selected a random sample of at least one quarter of grid cells . We selected a 50 x 50 m grid size because we wanted to be able to detect fine scale space use responses of wildlife. The random sample was stratified by vegetation openness and distance to cannabis farm in all watersheds, and additionally by distance to clearcut in the Slate Creek watershed, such that cameras were placed in proportion to the landscape attributes and a distance gradient was achieved. When a selected site was inaccessible, we selected a new one that also met the same stratification criteria. We rotated 15-20 cameras through the sampled grid cells, ensuring each camera was deployed for at least one round of two week duration. Because of rotations and field constraints, all cannabis sites were not monitored at the same time or for the same length of time . Altogether, we monitored a total of 149 camera stations for a combined 4,664 trap nights. We then used a team of researchers trained to identify species found in the study area to sort photos by hand, grouping by species. We calculated spatial and descriptive covariates for each site to use in wildlife occupancy and detection models . First, we calculated spatial distance covariates. Our main covariate of interest was distance to cannabis farms.

To calculate distance to cannabis, we combined the location data for participating farms in our study with mapped data on Josephine County cannabis farms from 2016 aerial imagery . Then we calculated the minimum distance from each camera to its nearest farm using the package sf in R. We transformed distance to cannabis using a square root to help fit potential thresholds in wildlife responses. Next, we again used the sf package, this time to calculate the distance from each camera to the nearest major paved roadway, which was primarily highway 99 for most sites. For our two raster-based covariates, we used the raster , and exactextractr packages in R. We calculated the proportion of forested land cover within a 50 m buffer around each camera, and extracted the elevation in meters at each camera site. We also included some non-spatial covariates. We included a covariate for Julian date of each interval, as well as Julian date squared, to capture seasonal peaks. We then included an estimated distance at which a camera could still detect an animal , which was measured at camera setup. We also generated activity indices for dogs and humans by calculating the number of observations of humans or dogs, respectively, at each camera within the last three days, divided by the number of days the camera was active. This produced an activity rate where the beginnings or ends of placement rounds were on the same relative scale as all other days. All continuous variables were scaled so that they centered on 0 with a standard deviation of 1 and checked for correlations in R. Finally, we used additional categorical covariates to account for potential effects of geographic region and camera type. We assigned each camera a binary region variable based on which USGS Unit 12 watershed it was located in, such that Region1 represents Lower Deer Creek, Region2 for Lower East Fork Illinois River, and Region3 for Slate Creek. We created a binary variable for camera type. We gave a 0 to camera models that generally performed well in our study system and a 1 to camera models that generally seemed to perform worse or were older models .To assess the local space use response of wildlife to cannabis production, we used single-season, hierarchical single and multi-species occupancy models. Our approach is a departure from the typical use of these models to estimate occupancy in that we knowingly violated multiple assumptions of occupancy models: first, because cameras were spaced relatively close together compared to the home range of species included in the study, we have likely violated the assumption of independent cameras; second, as a result of the aforementioned spacing as well as sampling across two years , we likely violated the model’s assumption of geographic and demographic closure . We have done our best to account for these violations in our use of regional fixed effects, as well as our narrow interval of replication . However, given our interest was in space use associations and not estimates of occupancy, we believe the violations are a minimal issue. This use of occupancy models is not particularly unusual, as the use of occupancy modeling to assess space use is becoming more common in wildlife response studies, and even traditional uses of occupancy modeling are influenced by wildlife space use .

With the closure assumption violated, the occupancy probability estimate represents the likelihood that the animal occupied the site at any point during the study period, while the detection probability represents a combination of the probability that the species is detected and the intensity of use of the site within its larger range . This interpretation is common in camera trapping studies , but we proceed while being careful to acknowledge where appropriate that any covariate’s influence on detection probability is a combination of its effect on detection and the intensity with which an animal uses a given space. In addition, we have taken care to include variables in the detection process to account for what we anticipate to be the largest sources of variation in detectability, so that the other variables should primarily reflect space use intensity. We therefore interpret occupancy for the models as space use rather than true occupancy . We operationalize detection as a combination of intensity of use, and camera detectability or error . To examine animals’ space use in relation to distance from cannabis grow table farms, we first conducted single species occupancy analyses on nine wild and one domestic species . We summarized species observations on and surrounding cannabis farms and created detection histories using the package CamtrapR in program R using Rstudio . We used a 24-hr time interval because our focus was on estimating space use associations instead of occupancy , and a short interval reduced the likelihood of the same individual animal being detected on neighboring cameras . We modeled the space use probabilities of the most commonly detected species or those of particular ecological interest, including: black-tailed deer , black bear , bobcat , coyote , gray fox , black-tailed jackrabbit , striped skunk , California ground squirrel , tree squirrels , and domestic dog using the NIMBLE and nimble Ecology packages in Program R . We selected these species because they had sufficient detections to model , and because they covered a range of functional groups, including predators and mesopredators , omnivores , large and small prey , and a domestic predator . We included dogs as an added check on our modeling approach, as their general distributions and associations are already well known in the study system, unlike wildlife species. We modeled the observed data as a binary variable where 1 was an observation for a given species at camera station s, and 0 was a non-detection. We modeled the observed data for each species as a product of both true occurrence of a given species at a site and our probability of actually detecting it , which is also influenced by intensity of use at a given site. The model assumes that true occupancy is an outcome of a Bernoulli-distributed random variable, denoted zs~ Bern, where is is the probability that a given species used site s on any day during the survey period. We assumed that occurrence and detection probabilities varied by species, and that cannabis might influence both in different ways. For occupancy, we expected that increasing distance from cannabis farms would increase animal space use for all species except domestic dogs, and ground squirrels. We also expected that elevation and forested land cover would influence space use based on their importance in other wildlife studies . We expected distance to highways to negatively affect space use, and to function as a proxy for other non-cannabis forms of human land use in our study system. While we initially wished to include distance from clear cuts as the other major source of human disturbance in the study system, it was highly correlated with distance to highways, so we did not include it in our models. Finally, we accounted for potential regional differences in the three watersheds by including a fixed effect of region. We parameterized regional fixed effects using region-specific intercepts as described in the following equations. For the single species occupancy models, occupancy and detection varied by species . Recall that for our models, we are interpreting occupancy as space use, and detection as a combination of detectability and space use intensity .

Dampened reactivity and recovery following stress have also been related to poorer health including depression and externalizing problems . Individuals can show blunted rather than exaggerated stress reactivity and recovery for many reasons . Individuals who experience chronic or repeated stress may initially show heightened emotional and biological stress reactivity and recovery, and these responses may habituate and show a blunted profile over time . Therefore, whereas unpredictable, acute stressful life events may promote a profile of exaggerated reactivity to stress, living in adversity can serve as a chronic stressor and consequently can promote inflexibility of psychobiological systems over time, such that individuals are incapable of responding to acute stressors . Indeed, youth and adults who experience more adversity generally show blunted rather than enhanced cortisol and heart rate reactivity to acute stress , as well as reduced activation of neural regions involved in threat such as the amygdala . It has been posited that individuals who experience high levels of adversity may be inclined to disengage from stressors, which can attenuate psychobiological reactivity and recovery . Lastly, low reactivity may result from socialization from peers and parents . For instance, youth who experience adversity may interact with deviant peers or bullies who prompt them to be less responsive to stress and may be socialized by parents to be less affected by daily stressors . Just as heavy substance use can dysregulate HPA axis function , dysregulation of the HPA axis may also contribute to substance use risk. Youth with blunted HPA axis reactivity to stress may lack physiological inhibitory control, such that they may be less inhibited by the social consequences of risk-taking compared to adolescents who show greater cortisol reactivity to stress . Alternatively, adolescents with chronic under arousal may be generally more inclined to pursue risky behaviors to promote physiological arousal . Youth may not show cortisol reactivity to a stressor because they are not sensitive to that stressor, or because they have already become elevated in anticipation of a stressor .

That is, certain youth may be more responsive to the threat such that they already show elevated levels of cortisol prior to stress onset and consequently show no further elevation in cortisol thereafter. Both blunted cortisol reactivity and anticipatory cortisol have been associated with more frequent substance use later in adolescence, especially among youth with difficulties in emotion regulation . Dysregulation of HPA axis function may similarly promote risk for lifetime substance use during adolescence. Adolescents with higher basal cortisol had earlier onset of substance use, although cortisol was not assessed following stress , and blunted cortisol secretion in anticipation of a laboratory task has been linked to greater substance use in pre-pubertal boys . Given the potential for bidirectional associations between HPA axis function and substance use, longitudinal studies are needed to disentangle whether HPA axis reactivity to and recovery from stress relate to risk for substance use onset during adolescence. Specifically, it is well-established that heavy substance use—as opposed to substance use initiation or less frequent substance use—can dysregulate physiology , so researchers may be best positioned to examine the role of physiology on substance use risk during adolescence when youth are initiating substance use but have not yet engaged in heavy substance use. In addition to cortisol reactivity, emotion reactivity to stress may relate to substance use. There are several emotion-related risk factors for substance use and substance use disorders in both adults and adolescents, including greater negative emotions, emotional lability, and emotional dysregulation . Although it is well-established that emotions influence frequency of substance use among users, it remains unclear whether emotion reactivity to stress relate to adolescents’ risk for substance use initiation. Emotion reactivity to stress often includes increases in negative emotions of both high arousal and low arousal and decreases in positive emotion, and each form of emotional change can have unique implications for health . Youth with exaggerated and dampened stress reactivity and recovery with respect to emotion may be particularly at risk for earlier onset of substance use, especially for Mexican-heritage adolescents, who experience culturally-specific stressors .

Therefore, research is needed to determine whether emotion reactivity to stress and recovery from stress is related to substance use and the emergence of substance use among these youth.The impact of stress reactivity and recovery on substance use during adolescence may vary by sex. Adolescents’ motivations for substance use differ by sex . Male youth tend to be more motivated to use substances for social enhancement whereas female adolescents are more motivated to use substances to cope with negative emotion and stress . Further, female adolescents show higher comorbidity between substance use and depression relative to male adolescents,cannabis grow supplies suggesting that emotion and stress may be particularly tied to female adolescents’ substance use . Therefore, although male adolescents tend to show earlier and more frequent substance use relative to female adolescents , substance use may be particularly related to the stress response among female adolescents. Indeed, prior research regarding youth who have used substances by age 16 in this cohort of Mexican-origin adolescents has found that greater cortisol reactivity relates to earlier age of initiation of alcohol use for girls, whereas blunted cortisol reactivity was related to earlier initiation of marijuana use only for boys with less advanced pubertal status . It is critical to disentangle whether differences in stress reactivity and recovery precede substance use across the sexes.Poverty status may also moderate associations between responses to stress and substance for two reasons. First, early life adversity including poverty status has been found to influence psychobiology such that youth who experience early life adversity, including youth below the poverty line, tend to show profiles of blunted cortisol responses to stress . Because these youth are already at heightened risk for blunted cortisol responses, the association between these responses and substance use may be stronger among these youth. Second, poverty status may influence adolescents’ propensity for substance use. Youth below the poverty line may experience earlier exposure to substance use and substance-related crime, more targeted marketing of substances, and lower parental involvement . They may also be more motivated to use substances for reasons beyond stress, such as due to boredom, sensation seeking, and pursuit of enhancing effects in order to compensate for a lack of pleasurable substance-free daily activities . Poverty status may similarly influence the types of substances that adolescents use. Whereas cigarette use is more common among youth with lower socioeconomic status, marijuana, alcohol, and vaping are generally more prevalent among more affluent youth, potentially due to differences in cost, availability, and social norms . As a result, associations between stress reactivity and recovery and certain substances may differ by poverty status. The present study investigated whether adolescents’ HPA axis and emotion responses to the Trier Social Stress Test , a validated paradigm for eliciting social-evaluative threat, were related to the use of various substances among Mexican-origin youth growing up in a low income, high-risk agricultural setting . Responses to a social stressor were selected because adolescents tend to be particularly responsive to social threats, compared to younger children and adults , and youth often use substances in peer contexts to reduce social stress or enhance social experiences.

In line with prior research highlighting how people vary in the types of emotions they experience in response to stress , we examined changes in three emotions following stress: anger, sadness, and happiness. Discrete emotions have different functional purposes and have unique impacts on cognitions and judgments . Therefore, rather than aggregating across emotions, we assessed unique effects of each emotion. We tested whether stress reactivity and recovery related to substance use among adolescents at heightened risk for substance use, in line with previous studies that have examined substance use initiation in high-risk samples . Most prior studies examining stress responses and substance use have been conducted in the context of adult substance users or with cross-sectional designs . Therefore, we employed a longitudinal design to disentangle whether dampened psychobiological stress reactivity and recovery at age 14 precede the emergence of substance use initiation by age 16. Models examined whether differences in adolescents’ HPA axis and emotion reactivity and recovery to the TSST at age 14 were related to a) use of substances by age 14, b) use of substances by age 16, and c) emergence of substance use between ages 14 and 16, excluding youth who had already used by age 14. Given the high levels of adversity in this sample, dampened psychobiological stress reactivity and recovery were predicted to be associated with use of alcohol and marijuana among these youth, in line with previous research . Although not previously tested with use of cigarettes and vaping, we examined whether dampened psychobiological reactivity and recovery would similarly relate to these substances which are also commonly used in adolescence. Finally, models examined whether associations between HPA axis and emotion stress reactivity and recovery and substance use differ by sex and poverty status. Given that female adolescents may be more inclined than male adolescents to use substances to reduce negative emotion , we predicted that associations between dampened stress reactivity and recovery and substance use would be stronger for female adolescents than male adolescents. Because poverty status can promote profiles of dampened reactivity and can influence the types of substances that youth use , we tested whether associations differ by poverty status.Adolescents provided four 1-2 mL saliva samples via passive drool throughout the task. They provided the first sample after spending over two hours in the laboratory environment, during which they completed benign surveys, and then resting in the lab for 10 min. The second sample was collected immediately after the TSST was completed, roughly 15 min. after TSST onset. The third sample was collected 30 min. after TSST onset, and the fourth and final sample was collected 60 min. after TSST onset. This sampling procedure was similar to previous administrations of the TSST .