Proponents often argue that smaller-scale styles of farming are more sustainable , sometimes drawing parallels to industries such as craft vineyards . However, these farms are also often located in more rural, bio-diverse watersheds close to protected wilderness and managed timberlands that could be at environmental risk from expanding development . As land managers and policymakers decide where to prioritize cannabis farming, there is a growing need to contextualize the potential effects of the legacy pathway in ecologically sensitive regions. In Josephine County, Oregon, the co-occurrence of cannabis agriculture within the highly bio-diverse Klamath-Siskiyou Ecoregion has created a natural experiment to examine how the post-legalization expansion of small-scale, private land farms might affect freshwater and terrestrial biodiversity. In this study we ask: what was the development pattern of cannabis land use in Josephine County during the first year of recreational legalization, and how might cannabis production overlap with sensitive ecological features? To address these questions, our objectives were to: map and characterize the spatial configuration of cannabis farms in Josephine County, Oregon in an early stage of cannabis legalization, and examine the proximity of cannabis production to undeveloped land cover, freshwater, sensitive fish species , Chinook salmon , and Steelhead, and terrestrial carnivore richness , coastal marten , ring tail , cougar , bobcat , gray fox , and coyote. We anticipated that due to the cultural dominance of historical growing practices, grow trays 4×4 cannabis production in this region would be comprised of relatively small-scale farms representative of the legacy industry pathway , but most farms would be new since legalization.
Based on research from California pre-legalization , we expected that cannabis in our study area would also be clustered at the subwatershed level. Concerning proximity to ecologically sensitive areas, we expected that cannabis agriculture would be located in more undeveloped lands, closer to freshwater streams or rivers, and closer to sensitive fish species compared with the surrounding context of all private land parcels. The proposed mechanisms behind these predictions are summarized in Table 1 and draw on the five hypothesized pathways of effect for cannabis on the surrounding environment listed earlier . Finally, we quantified spatial overlap of cannabis farms with projected terrestrial carnivore distributions. We focused on carnivores because previous studies have described this group as particularly sensitive to cannabis cultivation , and because this group includes species of regional conservation concern, such as the fisher.Our study focused on Josephine County in Southern Oregon . Josephine county was an ideal location to measure cannabis dynamics of legacy areas and to gain broader insights on the ecological outcomes of cannabis legalization. Josephine County had a long history of illicit and medical cannabis cultivation, and an active presence in the growing legal industry in Oregon . In 2014, Oregon became one of the first U.S. states to legalize recreational cannabis. Southern Oregon has become known as a prime destination for outdoor cannabis production , and Josephine County had the highest number of applications for licensed producers relative to population size in the state . Widespread cultivation of cannabis started in the region during the 1960s and is now viewed as one of the county’s main economic drivers . Josephine County is also located in a biodiversity hotspot.
The study area is part of the KlamathSiskiyou Ecoregion, one of the most biodiverse temperate forest regions, and an area of increasing conservation focus . The KlamathSiskiyou Ecoregion straddles the Oregon-California border and contains several regions identified as critical climate change refugia . The study area contains several protected areas including state and federal protected lands , and several federally threatened and endangered species including northern spotted owl and coho salmon , and state sensitive species such as fisher . To characterize the spatial distribution of cannabis farming, we hand-digitized cannabis production sites across Josephine County using high spatial resolution Google Earth images taken after statewide legalization . We based our methods on those previously used to map cannabis production in regions of northern California . We used publicly available satellite imagery for May or July 2016, the first year with a full growing season after recreational legalization went into effect in July 2015. Next, we characterized the farms themselves. Digitizers counted the number of plants visible in outdoor gardens, recorded whether there was a visible fence surrounding each cannabis production site, and recorded whether each site was new . To estimate the number of plants produced in greenhouses, we used 180 instances where we could count the number of plants through the see through top of greenhouses and divided this count by greenhouse area. This yielded an average of one plant per 7.23 m2 of greenhouse area, which we then used to estimate greenhouse plant counts. See supplement online for full mapping procedure.To test the accuracy of image-based data collection, we visited approximately 30 farms between 2017-2019 to verify and refine our mapping protocol after a pilot mapping process. Because systematic ground-verification for all grow sites was not possible, we used a qualitative confidence score to represent digitizers relative certainty about each mapped site . For consistency, we thoroughly checked all mapped polygons and associated scoring using the same person who conducted on-the-ground site verification . We used only high confidence sites for these analyses, but see supplemental materials for a comparison to the full data set . Finally, we used only sites with more than four plants for analyses because we were focused on the cannabis industry rather than plants grown for personal consumption .
To assess the potential ecological effects of cannabis at the landscape scale, we quantified spatial characteristics and proximity of cannabis to landscape features, fish populations, and carnivore distributions . This proximity doesn’t directly infer effect, but rather whether the configuration of cannabis may increase the opportunities for negative environmental outcomes. We focused on spatial metrics that might approximate some of the five main hypothesized effects of cannabis farming on local environments . To approximate the potential loss of wildlife habitat, we assessed cannabis production in developed versus undeveloped lands. We extracted elevation and 2013 land cover at the centroid of each farm, and then grouped land cover classes into developed and undeveloped categories . The National Land Cover Database Cultivated category includes hay, annual crops such as corn, or perennial crops such as orchards and vineyards; given the resolution of the NLCD dataset compared to average farm size, this is unlikely to include cannabis pre-recreational legalization. To approximate the potential degradation of forested habitat, we assessed the forest structure on farms used for cannabis production . To do so, we extracted canopy cover and stand age at the centroid of each farm . To approximate the potential effects on carnivores, we examined the overlap of cannabis with projected carnivore richness and individual carnivore species distributions. We extracted the average carnivore richness, and individual carnivore occupancy value at the centroid of each farm . For carnivore richness and individual carnivore distributions, we used projected model data for southern Oregon, from Barry and Moriarty et al., unpublished data . Within our study area, the richness layer represents the total number of carnivores expected in a given grid cell for the following species: fisher, coastal marten, ring tail, cougar, bobcat, gray fox, and coyote. For individual species, horticulture products we used calculated distribution data from projected occupancy and this represented the average probability that a given area would be occupied by that species. Marten projected occupancy was almost entirely absent in this region and was not included in individual species summaries. Finally, to approximate the potential effects of freshwater extraction or declines in freshwater quality due to cannabis production, we assessed the proximity of cannabis to freshwater rivers or streams and fish habitat for potentially sensitive species. For vector data with the proximity analysis , we calculated the distance from the centroid of each cannabis farm to the nearest river and fish habitat in R using the ‘simple features’ package . For rivers, we used the National Hydrography Database . We filtered out canals/ditches and underground aqueducts . For fish habitat data, we used Oregon Fish Habitat Distribution data for coho salmon, fall and spring run Chinook salmon, and winter and summer run Steel head . The fish dataset includes any areas used within the past five reproductive cycles for each species. We then calculated summaries of proximity and overlap metrics in R. In order to inform the interpretation of the fish habitat data, we also extracted the stream order of the nearest stream to each cannabis site, and summarized results in R. The conservation effect of these metrics for cannabis likely depends on how they compare to the potential effect of surrounding land uses and available land for development . Therefore, we contextualized the proximity metrics by comparing cannabis farms to all private land parcels in the county. We used all private parcels instead of parcels without visible, high-confidence cannabis because we were mainly interested in how cannabis production fits into the surrounding landscape context of available private lands.
See the supplement for a more local comparison in which we calculated the proximity and overlap metrics for all parcels within a buffer around each cannabis site. For buffer size we used the average home range of fishers from southern Oregon . Outdoor cannabis production across Josephine County in 2016 was generally small-scale but also pervasive, and suggested that recreational legalization greatly expanded the industry locally. We mapped nearly 4,000 individual gardens and greenhouses on 2,220 different farms, all identified as highly likely to be cannabis . Most sites were new since legalization . Most production was in outdoor gardens , but a greater proportion of greenhouses were new . Farms contained an average of 1.76 individual sites, with a maximum of 14. The average size of individual sites and farms was small but highly variable in terms of cultivated area and number of plants . The average parcel size for farms was 0.098 km2 . 99.6% of detected farms were on private land parcels. Out of all private land parcels in the county, 5.7% contained a farm identified as highly likely to be cannabis. The Ripley’s K analysis indicated that cannabis sites were clustered at all observed spatial scales . At the county level, the Getis-Ord Hotspot maps identified two regional hotspots near Williams in the SouthEast, and in the Illinois Valley in the South-West . The sub-watershed analysis indicated that even within these larger regional hotspots, there were pockets of more and less intensive production . Both the county and sub-watershed hotspots seem to follow primary roads or river networks.Overall, cannabis was produced on more undeveloped and forested parcels compared to all available private lands as a whole . The most common land cover for individual outdoor gardens was shrubland , followed by cultivated , and forest . Greenhouse cannabis production occurred in areas already cultivated with other crops , followed by shrubland , and forest . At the farm scale, however, where outdoor and greenhouse production was combined, forest was the most common land cover type . The predominance of cannabis in forest and undeveloped land covers was also supported by the Gradient Nearest Neighbor data on forest structure. Although the GNN dataset uses a broader categorization for forest, it also indicated that cannabis was disproportionately grown in forested areas . Nevertheless, the forest structure of farms was similar to that on all available private parcels .This study is one of the first landscape-scale assessments of small-scale outdoor cannabis farming and its potential broad-scale ecological effects in a rural biodiversity hotspot. Our results suggest two main conclusions. First, private land cannabis farming in Josephine County, Oregon in 2016 was common and spatially clustered, expanded post-recreational legalization , and yet only covered a small portion of the total land area. This supports our expectation that cannabis farming in Josephine County would exhibit characteristics typical of the legacy development pathway, but that these farms would largely be new post legalization. Second, our spatial proximity results highlighted areas of overlap or proximity of cannabis farms and sensitive habitats and species. Compared to the surrounding context of all available private land parcels, cannabis was more frequently located in forested areas and undeveloped land, closer to rivers/streams and coho salmon habitat, and in areas of high value as fisher habitat. These results provided mixed support for our expectation that cannabis production would be in areas that increase its potential ecological impact.