There are many possible explanations for why deer and gray fox space use might be more influenced by cannabis farms than lagomorphs. These generally have to do with characteristics on the farms themselves. Wildlife may be interacting with the increased presence of domestic cats and dogs on cannabis farms , for instance, for deer as potential or perceived prey, or gray foxes as competitors . Alternatively, deer and gray foxes may be responding to behavioral cues from increased human presence and activity on cannabis farms . It is possible that lagomorphs are more behaviorally flexible than deer and gray foxes and can avoid altering their spatial patterns by instead shifting their temporal activity patterns, for instance, becoming more nocturnal . More research is needed to disentangle these potential mechanisms. Both detection rate summaries and model results suggest that cannabis farms appeared to disproportionately influence the space use of larger wildlife species. Black bears had a higher detection rate on comparison sites compared to cannabis farms and the model results indicate that larger black-tailed deer and gray foxes might avoid cannabis farms, while smaller animals such as lagomorphs appear to be unaffected. This result is expected, as large bodied animals such as deer may be unable to access space on the farms if they are physically blocked by fencing,vertical grow system while smaller species are still able to move through these barriers .
Despite variation in which species responded to cannabis farms, we did not find evidence from either detection rate summaries or model results to suggest that predators were attracted to these sites. Other studies have shown predators tend to avoid agricultural development, and our results seem to support the same trend . By contrast, there has been recent suggestion that cannabis production on public lands may serve as an “ecological trap” by attracting carnivores to production areas that then expose individuals to deadly toxicants . Our results, at least in the short-term, suggest that this dynamic may be less likely to occur on small-scale private land cannabis farms. This highlights the different potential ecological threats and processes playing out on public versus private land cannabis production sites. Not only do private land cannabis farms seem to use fewer toxicants , but they may also have higher human activity levels on site compared to public land production located in more remote areas. Wildlife may in turn tend to avoid this human presence rather than being attracted . This study begins the discussion regarding a glaring shortage of data on animal space use on cannabis sites, but there are many further avenues for future research. For example, the relative importance of cannabis farms in their influence on animal space use should be analyzed in the surrounding landscape context. The influence of roads on the modeled detection results implies that cannabis cultivation, despite occurring in a rural area in this case, was not the only form of human disturbance to which animals were responding. It may therefore be useful to compare cannabis and other forms of rural land use.
In addition, it is necessary to conduct further study at broader spatial and temporal scales in order to examine long term wildlife community response to cannabis and unravel the complicated set of potential contributing factors.Wildlife are likely to have species-specific responses to small-scale outdoor cannabis farms, and, thus, the specific land use practices occurring at a site may be influential for biodiversity conservation in these communities. Future studies should examine the role of fencing, timing of human activity, presence of domestic dogs and cats, and other site level practices that may influence wildlife use. Many small-scale cannabis farms are not part of a licensed production system , and therefore cannot be regulated for their production practices . For these producers, a mix of educational resources on wildlife friendly growing practices, grower enforced community standards or expectations, and law enforcement efforts to reduce noncompliance, may play an important role in increasing or maintaining biodiversity. For species deterred from cannabis farms, such as was implied by our deer and gray fox results, further research is needed to understand the mechanism for this avoidance. If, for example, fencing, artificial lighting, or sound are identified as major causes of this deterrence, then careful consideration should be given to the regulations on these practices at cannabis farms and their relation to critical habitat features such as water sources or animal migration routes.There is a national trend toward statewide legalization of medical marijuana despite federal classification of marijuana as a Schedule I illicit drug. There are compelling arguments for and against medical marijuana legalization and its potential impact on an array of complex social issues .
Residents in states where medical marijuana is legal are more likely to have tried marijuana, report current marijuana use, and be diagnosed with marijuana abuse or dependence . Additionally, there is preliminary evidence to suggest that there is likely a dose-response relationship between the number of years since legalization and marijuana prevalence rates . A key question regarding more liberal marijuana policies is whether and how they affect use of other drugs including addictive and harmful substances like tobacco. Previous studies have found a strong positive association between cigarette and marijuana use . Epidemiologic data indicate that the prevalence of tobacco and marijuana co-use has increased from 2003 to 2012 . Moreover, the increase in co-use occurred specifically among those ages 26–34 years, and the greatest percent increase, in those ages 50 years and older . It is unknown, however, if this national increase in co-use is directly associated with statewide legalization of medical marijuana. If marijuana policies are indeed associated with co-use, the current trend toward legalization of medical and/or recreational marijuana, without any regulatory action, has the potential to influence patterns of cigarette and marijuana use/co-use over time. An increase in cigarette and marijuana co-use has the potential to create challenges for cigarette smokers who want to quit. There is evidence to suggest that cigarette and marijuana co-use is associated with greater nicotine dependence . Possible explanations for this link include the role of the endocannoboid system in nicotine metabolism , genetic predisposition for co-use , and various environmental and cultural influences . The relationship between co-use and nicotine dependence, however, is understudied in adults, particularly among those ages 50 years and older. Since nicotine dependence is influenced by both nicotinic receptors and nicotine associated metablism that change with age ,pipp racking we can expect nicotinedependence among cigarette and marijuana co-users will also vary over the lifespan. Few studies have examined cigarette and marijuana co-use and nicotine dependence from adolescence through adulthood. As the nation is well-past the tipping point on medical marijuana legalization, studies are needed to take a closer look into whether marijuana policies have the potential to influence tobacco control efforts at the population level. For example, over time, it is likely that greater access to legal marijuana will increase the absolute number of co-users who have greater nicotine dependence and difficulty quitting cigarettes. Such data can help to identify subset populations at higher risk of nicotine dependence and could have both policy and treatment implications in tobacco control. In this study, we sought to examine relationships between medical marijuana laws and cigarette and marijuana co-use. Additionally, we examined the likelihood of nicotine dependence in co-users. We analyzed data from the 2013 National Survey on Drug Use and Health and stratified the analysis by age categories. Results from this study can inform the direction of future medical marijuana policies that may inadvertently affect tobacco control efforts.We analyzed cross-sectional data from the 2013 NSDUH conducted by the Substance Abuse Mental Health Services Administration . The primary purpose of NSDUH is to measure prevalence and correlates of drug use in the civilian, non-institutionalized U.S. population aged 12 years and older. Since 1991, NSDUH has consisted of an independent multistage area probability sampling design for each state and the District of Columbia and uses a combination of the Computer-Assisted Interviewing and Automated Computer Assisted Interviewing instruments in selected individuals and households . The survey offered $30 in cash to participants and was conducted in 2013 by Research Triangle Institute . The final survey consisted of 67,838 CAI interviews with a weighted screening response rate of 84% and an interview response rate of 72%. The public use file consisted of 55,160 records due to a sub-sampling step which included a minimum item response requirement for weighting and further analysis.
A detailed description of the questionnaire items, sampling methodology, data collection/ response rates, and sample weights is published elsewhere . The present study was exempt from the University of California San Francisco’s Human Research Protections Program approval since data were publically available and subjects cannot be identified. In this analysis, only those with complete responses for all measures were included. Additionally, while the analysis included participants aged 50–64 years, those 65 years of age and over were excluded due to a small sample size . The final sample included 51,993 participants. The item “How long has it been since you last used marijuana or hashish?” was used to classify respondents into three categories: “Within the past 30 days” ; “more than 30 days” ; and “never used marijuana” . Current marijuana users reported frequency of past 30-day use [Range = 1–30 days]. Cigarette use was assessed with an item asking whether and how recently participants had smoked “part or all of a cigarette.” Past 30 day users were categorized as current cigarette smokers, other than “within the past 30 days” as former smokers, and “never used cigarettes” as never smokers. Participants were coded as co-users if they had smoked at least one cigarette in the past 30 days and used marijuana in the past 30 days. Respondents who indicated blunt use were not included in our analysis since our analysis includes comparison of nicotine dependence in cigarette smokers who use marijuana vs. those who do not marijuana .Nicotine dependence was measured in two ways: the 17-item Nicotine Dependence Syndrome Scale and the single “time to first cigarette” item from the Fagerstrom Test of Nicotine Dependence . Respondents’ average NDSS scores were calculated over 17 items across five aspects of dependence and current smokers with a cutoff score of 2.75 or above were categorized as nicotine dependent. Those who responded smoking cigarettes in the past month and having their first cigarette of the day within 30 minutes of waking on the TTFC were categorized as nicotine dependent. Additional information on NDSS and TTFC questionnaire items, scoring procedure, and methods used for cutoff scores are published elsewhere . We examine both NDSS and TTFC scores to potentially increase the reliability of our findings. Descriptive statistics are reported for demographics, cigarette and marijuana use, and lifetime depression as well as chi-square tests of differences by statewide medical marijuana legalization status . One-way ANCOVA models tested for differences in marijuana use and cigarette and marijuana co-use in the overall sample, and separately for each age category, between states where medical marijuana was legal vs. illegal, adjusting for age , gender, race/ethnicity, education, age at first cigarette initiation, age at first marijuana initiation, and lifetime depression. Additionally, we calculated mean NDSS and frequency of TTFC scores by statewide legalization categories across age groups. In the overall sample and within each age category, two logistic regression models examined nicotine dependence, as measured by NDSS and TTFC scores, in cigarette and marijuana co-users . Models were adjusted for age , gender, race/ethnicity, education, lifetime depression, and statewide medical marijuana legalization status. Bonferroni adjustments were applied to all models with over five independent variables . In this analysis, we used the Taylor series method for replication methods to estimate sampling errors of estimators based on complex sample designs. The regression coefficient estimators were computed by generalized least squares estimation using element-wise regression. The procedure assumes that the regression coefficients are the same across strata and primarily sampling units . All models were run in SAS 9.4 using the SURVEY procedures to obtain weighted estimates to increase the generalizability of the findings . The study sample was approximately half male, majority non-Hispanic White , and more than a quarter was college-educated .