Deer and tree squirrel occupancy probability increased with distance from cannabis farms, indicating potential avoidance. Domestic dogs, as expected, decreased in predicted occupancy with distance to cannabis farms. Interestingly, gray fox and ground squirrel occupancy probability also decreased with distance from cannabis farms, indicating that these species may be more likely to be found on and around cannabis farms . Six species had a meaningful detection response to cannabis farms . As expected, bobcat and ground squirrel detection probability increased with distance from cannabis farms, indicating that they may use areas further from cannabis farms more intensively. For ground squirrels, this implies that although they are more likely to be found closer to cannabis farms, they may use the spaces farther from farms more intensively. Again as expected, domestic dog detection probability decreased with distance from cannabis farms, confirming that they spend most of their time on and surrounding cannabis farms. Surprisingly however, deer, jackrabbit, and striped skunk detection also decreased with distance from cannabis farms. More frequent detections on occupied cannabis farms implies that these species may also be using the space on and surrounding cannabis drying trays farms more intensively .For a majority of species, at least one regional intercept was meaningfully associated with occupancy probability. Elevation predicted occupancy for coyotes and striped skunks, and forest proportion predicted occupancy for jackrabbits, tree squirrels, and ground squirrels. Distance to highways was the only occupancy covariate that was not credibly non-zero for any species. As for detection, all covariates were meaningful for at least some species. The covariates for detectability, camera type and camera view, were credibly non-zero for four species all together.
There was evidence for seasonal effects, with date and date2 meaningfully predicting detection for a majority of species. The activity indices had meaningful, and somewhat surprising results. Coyotes, bobcats, and tree squirrel detection was negatively associated with human activity, and ground squirrel detection was negatively associated with dog activity. However, coyote, gray fox, and jackrabbit detection probabilities were all positively associated with dog activity.For the multi-species occupancy models, almost no population-level parameters were meaningful . No group meaningfully responded to cannabis in either detection or occupancy processes. No covariates were meaningful for occupancy or detection at the population level, aside from omnivore detection intercept. However, there was more variation at the species level . For the species that also had single species model results, the MSOM results largely matched, with occasional changes in credibility. For instance, for the deer SSOM, date and date2 were not credibly non-zero, but in the MSOM they were, even though the actual estimated values were similar in both . Despite the lack of population-level associations, some groups did have common responses to cannabis at the species level. For example, the occupancy probability for all ground bird species was credibly positive, increasing with distance from cannabis farms, which implies possible spatial avoidance of cannabis farms . For all ground bird species and both herbivore species, detection probability credibly decreased with increasing distance from cannabis farms, which may imply that these groups use areas around farms more intensively . Domestic species largely responded as predicted at the species level: cat and dog occupancy decreased with distance to cannabis, and dog and horse detection decreased with distance from cannabis . The other groupings were more mixed. Carnivores largely did not respond meaningfully to cannabis in either detection or occupancy .
Omnivores had slightly more sensitivity, with three out of seven species responding meaningfully to cannabis in either occupancy or detection . For small mammals, tree squirrels and ground squirrels had opposite occupancy responses, and only ground squirrels had a credibly non-zero detection response . This study assessed wildlife space use responses to active small-scale outdoor cannabis farms on private land. Our work provides a timely baseline for understanding potential wildlife community consequences from an emerging land use frontier. Our application of occupancy modeling to space use responses has yielded two main conclusions: 1) even at small scales, rural cannabis farming can affect local wildlife space use; 2) patterns of animal space use responses are species-specific, but there may be common patterns for herbivores, ground birds, and some mesopredators in how they use spaces near to cannabis farms. These results have implications for the cannabis industry and small farm strategies for conservation.Eight out of ten species modeled individually had a meaningful response to distance from cannabis farms, either in occupancy or detection. Although the population-level means were not meaningful, at an individual level, 13 out of 24 of the species included in multi-species models had a meaningful response to distance from cannabis farms, either in occupancy or detection. Our hypothesis that a majority of species would avoid farms was not supported, since the strength and direction of effects were species-specific. However, the results imply a general ability for cannabis farming to affect local wildlife space use. The relationships between occupancy and detection probabilities and distance to cannabis also indicate that there could be threshold effects relatively close to farms where the slope of the relationship is steeper , though further steps would be needed to confirm this relationship. These results are in contrast with research from the western US on vineyards and avocado production that indicates the ability of some wildlife to use farmed land in seeming preference over surrounding land uses .
However, these other studies were conducted in areas where the agricultural land formed a corridor through more human-dominated land covers, which is the inverse of the landscape studied here. Our results are similar to studies on agroforestry systems with annual and perennial croplands, where there may be differential responses to agricultural land use and potential for filtering responses . Compared to the other covariates in the models, distance to cannabis farms meaningfully affected more species than any other single covariate other than the intercepts, or Date and Date2 . It was particularly surprising that wildlife responded to the physical land use of cannabis farms even more than human or dog activity, given that in other systems their space use intensity often responds more to human activity than human footprint , and is often negatively affected by the presence of dogs . This implies that cannabis farms may combine multiple potential sources of disturbance that wildlife may react to, and/or that the physical modifications for cannabis farms on their own are enough to trigger wildlife responses. More research is needed to disentangle some of the potential mechanistic pathways by which cannabis farms may affect wildlife. Overall, space use responses to cannabis were species-specific, confirming our alternative hypothesis for individual responses. While functional- or diet-group patterns are not as clear in this case as in other study systems , a few general patterns may be emerging, specifically in regard to herbivores/ground birds, and mesopredators. Our approach of using an occupancy modeling framework to assess wildlife space use associations was useful to identify some of these emerging patterns, because it allowed us to look at space use, separately from inferences on space use intensity . This is important because it helps capture different types of responses: attraction and deterrence, as well as potential behavioral shifts in activity patterns . For example,heavy duty propagation trays this helped identify opposing occupancy and detection responses from some herbivore and ground bird species. For medium to large herbivores and ground birds , occupancy credibly increased with distance from cannabis farms, while detection credibly decreased. This is the inverse of our alternative hypothesis that species using cannabis farms would decrease their activity intensity near to cannabis and suggests that while these species maygenerally avoid cannabis farms in space , the few areas that they do use, they may use more intensively. If this pattern is indeed driven by space use intensity, there are many possible explanations— for instance, perhaps these species, in an attempt to avoid cannabis farms, end up concentrated in smaller areas. The results for deer are at least partially consistent with other studies that indicate they generally have a neutral occupancy response to human presence and footprint, but have an increased intensity of use response . Another potential emerging pattern is the possible behavioral flexibility of some mesopredator/omnivore species, lending limited support to our alternative hypothesis that omnivores would display greater variation in space use responses. While less consistent across all omnivores than the pattern with herbivores and ground birds above, gray fox, striped skunk, and raccoons all displayed different potential ability to use the space on and nearby cannabis farms. Fox occupancy probability decreased with distance to cannabis, implying a potential attraction to cannabis farms. Raccoon and striped skunk detection probability decreased with distance to cannabis, implying that they may have a higher space use intensity near to cannabis farms. This is consistent with other studies that demonstrate that these species are often behaviorally flexible and able to coexist in human-dominated spaces . This association with mesopredator use of human spaces is also often explained via mesopredator release, when larger predators avoid an area of disturbance and thereby open a niche for smaller predators .
What is interesting is that in this case, however, our alternative hypothesis that carnivores would avoid farms was not supported, and predators largely did not respond to cannabis. Bear and coyote occupancy and detection did not respond to cannabis, and although puma did not have enough detections to include in the single species models, one was photographed in the middle of one of our study farms. Bobcat detection probability did increase with distance from cannabis farm but did not have a meaningful occupancy response. In fact, all four of these large predators were photographed at least once in the middle of a cannabis farm . Also interesting is that there was not a clear pattern of response for small mammal species that might be prey for the mesopredators. Unlike our alternative hypothesis that predicted a general attraction for all small mammals to cannabis farms, tree squirrels and ground squirrels had opposing responses. Tree squirrel occupancy increased with detection from cannabis farms, indicating avoidance, while ground squirrel occupancy decreased. For ground squirrels, our models suggest that while they are frequently found near cannabis farms, their space use intensity may be lower closer to farms. Again, there may be multiple reasons for this, but one possibility is that cannabis farms are being developed on ideal ground squirrel habitat, and while the squirrels have not yet relocated away from the farms, they are not as active on these sites due to the disturbance associated with the farms. Alternatively, cannabis farms may be creating new habitat for ground squirrels by clearing vegetation and irrigating the land, and the lower detection may simply reflect lower population densities as fewer individuals have discovered the new sites. It would be interesting to see whether these patterns change over time.This study has many limitations that are important to acknowledge. First, cannabis production comes in many forms in different locations, and this study does not represent all of them. This study is most applicable for small-scale and mixed light outdoor cannabis cultivation occurring on private lands in legacy production regions of the rural Western US. It is very likely that larger farms would have a greater impact on wildlife than those included in this study, or that farms developed in areas with existing agriculture might have less, or different kinds of effects. Because cannabis production is often unique from other forms of agriculture, these types of observational studies are valuable and merit repeating in different contexts. Next, we recognize we are applying occupancy modeling for a purpose that it was not directly designed for, and in doing so, we are violating multiple assumptions of the model. The use of occupancy modeling to assess space use relationships is increasingly common in wildlife studies , and we have done our best to account for the violation of assumptions in our modeling approach. Ultimately, we have confidence in our results. For example, we included domestic dogs because their space use patterns are already well understood on the landscape. That the models reflect our understanding of reality on the ground for this domestic species gives us confidence in the results for the unknown wild species.