This disconnect between the farmers’ perceptions of the industry compared with its rapid expansion could mean that the specific type of producers we interviewed were not benefitting from the industry increase that accompanied legalization. Other research on small scale cannabis producers from northern California supports this interpretation . It is also possible that landscape-scale industry change does not translate to the scale of an individual farm. If this is the case, it might help explain why the model of change in plant count had the fewest significant predictors—rather than being a more simplified process, it might instead be that the drivers for farms that existed before legalization are highly individualized or localized.Despite the uncertainty surrounding the trajectory of legacy cannabis farms, the models for new cannabis development provide insights into predicting the growth of the industry. While we did not project our predictions into the future, due in part to large policy changes that were not explicitly addressed in our interviews or models , our results do provide a baseline and contextualized understanding that could be used for future predictions. For example, based on farmer descriptions for why they may seek out large and rural parcels, it is unlikely that the strength of those drivers would decrease over time. On the other hand, farmers’ stated preference for farm-zoned parcels, which by contrast ended up as a significant driver in the opposite direction for new farm development, rolling hydro tables might be more likely to change over time as a potential driver due to shifts in regulation, enforcement, or social pressures for those renting/selling farm zoned parcels.
While our results are broadly useful for understanding cannabis landscapes in southern Oregon, there are many levels of complexity that are not captured by the models. For example, we treat cannabis agriculture as a single entity for these models, while in reality it contains a diversity of production styles and regulatory statuses. It is entirely likely that a large-scale licensed hemp farmer and a small-scale unlicensed cannabis farmer will reveal different drivers of their land use. Similarly, whether a farmer owns their own land or rents it, or whether a farmer lives on site or off, could also change the relationship with potential drivers. While we did not have detailed information on each cannabis producer at the county level to classify or group production styles, this would be an important avenue for future research. Future research would also benefit from added time points, particularly after the 2018 federal hemp legalization. In addition, this study was largely confined to a small number of small-scale farmers, and thus an expanded interview or focus group data collection process might reveal new drivers that would be relevant for other production styles. The relatively low pseudo r-squared values for our models suggests that there may be additional drivers functioning in this system, which extended interviews could help uncover. Our study focused on private land production, but it is important to remember that public land production also occurs in this area and influences not only the local environment, but the public perceptions of cannabis in the region. Incorporating the links between public and private industries might strengthen our understanding of these systems. Similarly, linking different scales of drivers would be a valuable next step. The interview data indicates that the southern Oregon industry is tied to regional and national markets , and that much of the economic decisions are either very fine scale at the level of the farm, or broader scale at the level of the state. Within the scale of Josephine County, the significant effect of mapped year implies that there may also be different dynamics in the two halves of the county that were mapped at different time points .
Although it did not directly emerge in the interviews, while living in Josephine County, PPS observed different local approaches to integrating cannabis farmers into the community in Williams as opposed to the Illinois Valley. This is an example of a secondary way in which the observations that occur during the interview process can assist with model interpretation. Further research on differences in local policies, community standards, or other regional differences might elucidate this pattern. Capturing interrelated dynamics such as local to county-wide processes would require a complex modeling approach but might lend insights into multi-scalar drivers. Understanding wildlife response to disturbance across landscape gradients is a complex endeavor. Individual animals can respond to anthropogenic disturbance with a variety of different behavioral changes , but these responses are all context dependent . For example, in some studies, coyotes demonstrate a space use preference for agricultural areas , while in others, they avoid farmland ; similarly, at times they are labeled as urban exploiters , and at times avoiders . These differences are often tied to context-dependent responses and differences in landscape configurations . At a wildlife community level, the complexity of responses increases even more. Disturbance may affect some species more than others, or in opposite directions, leading to broader contractions or expansions in species assemblages and interactions . Changes in species interactions, especially if they involve keystone species, can have cascading effects on ecosystem function . The context-dependence of these shifts means that consistently predicting how wildlife communities will respond to rapid land use change at a local level is very difficult and requires understanding multiple interacting mechanisms . Nevertheless, wildlife community responses to disturbance matter because the context-dependent consequences in turn can affect ecosystem health , effectiveness of wildlife management strategies , and human-wildlife conflict . Thus, there is a continuing need to examine the effects of disturbance on wildlife in order to develop strategies to mitigate the negative effects of land use change. Understanding wildlife response to disturbance is particularly important in areas where land use change is occurring rapidly.
Spaces of rapid development for agriculture are called frontiers, and are often spurred by the growth of a new industry, while accompanied by the movement or growth of human populations, and transportation structure improvements . Frontiers are naturally spaces of rapid land use change, and often sites where different approaches to land use planning and conservation clash . While frontiers present a novel disturbance scenario, most studies of wildlife response to agricultural land use have been concentrated in Asia, South America, and Europe , and often in areas that have long been dominated by agriculture. Such studies may miss some of the immediate responses of wildlife to development that occur over shorter spatial and temporal scales . Recreational cannabis agriculture represents an ideal opportunity to study wildlife community response to disturbance generated by a currently expanding land use frontier. In the US, state level legalization of recreational cannabis has initiated a rapid land use frontier for outdoor cannabis production . This frontier is particularly noticeable in rural areas of the western US. Influenced by its illicit history, outdoor cannabis is often grown in remote, bio-diverse regions with minimal other non-timber agriculture . Regardless of individual legal status, private land cannabis farms are typically smaller than those of other commercial crops, and are clustered in space, creating a unique land use pattern of small points of development surrounded by less developed land . This pattern of development locates the cannabis frontier directly at the wilderness boundary—a somewhat rare characteristic for agriculture in the United States . At a broad scale, cannabis development in rural areas overlaps with regions that may be important habitat for wildlife , yet it is unclear whether, where, vertical horticulture and to what extent this broad scale spatial overlap actually results in negative impacts on animals at a local scale. There have been studies suggesting that cannabis production may lead to habitat destruction or modification , and wildlife death due to toxicant use and poaching . However, most studies on direct impacts of cannabis farming have largely been conducted on illegal public land production sites , as opposed to private land sites. The research conducted to date on private land has not encompassed a full landscape gradient around cannabis farms. Not only have private land sites likely seen the largest production increases due to legalization in recent years , they are also often characterized by very different production practices than public sites. For example, on many private land farms, indirect sources of disturbance to wildlife such as noise and light are more common than direct causes of mortality. Private land sites may use high-powered grow lights, drying fans, and visual barrier fencing, which could create potential wildlife disturbance . Such practices are less common on public land. It is possible that as cannabis production expands, particularly in the licensed industry, these forms of indirect impact may be more typical of cannabis production overall. Indeed, indirect effects of production practices on wildlife space use and behavior is a common concern for other agricultural crops , and may also interact with direct effects on mortality .
Therefore, it is critically important to study both indirect and direct effects of cannabis on wildlife communities, particularly on private lands where research is lacking. Because outdoor cannabis farming is a land use frontier and therefore often characterized by different land use practices and patterns from traditional established farming in the US, it is uncertain whether other agricultural systems provide the best models to predict wildlife responses to cannabis development. Wildlife may use, avoid, or display differential responses to cannabis development, depending on whether production more resembles small scale countryside farming , industrial agriculture , or exurban/suburban development . In the case of differential responses, it’s also unclear whether cannabis production would have widespread enough effects to trigger mesopredator release , or generate novel food sources that could be exploited by behaviorally adaptable species like omnivores and small mammals . We based our study in Josephine County, in southwestern Oregon , in 2018- 2019, three years after statewide recreational legalization took effect. Josephine County was an ideal location to capture the start of the cannabis frontier expansion post-legalization in a rural, bio-diverse legacy production region. Our study area sits within the Klamath-Siskiyou Ecoregion, which is one of the most bio-diverse temperate forest regions on Earth . The Klamath-Siskiyou Ecoregion straddles the Oregon-California border and contains several areas identified as critical climate change refugia . Within this ecoregion, Josephine County contains several protected areas including state and federal protected lands , as well as several species of concern, including native salmonids, threatened Humboldt martens , fishers , and spotted owls , all of which are hypothesized to be directly or indirectly affected by cannabis agriculture . Unlike other forms of traditional agriculture, outdoor cannabis is often grown directly alongside or nestled within areas of high biodiversity . Southern Oregon, and Josephine County in particular, has a long history of illicit and medical cannabis cultivation, as well as an active presence in the growing legal industry in Oregon . Southern Oregon became known as a prime destination for outdoor cannabis production even before legalization, and Josephine County had the highest number of licensed producers relative to population size in the state by 2019 . Production in the county accelerated after recreational legalization went into effect in 2015 , in a similar pattern to cultivation occurring across the border in northern California, with clusters of small farms surrounded by undeveloped or less developed rural land . Our study area consisted of farms spread across three sub-watersheds in Josephine County . We set cameras at 1,110 m to 2,470 m above sea level. The study area included a mix of vegetation types, including open pasture, serpentine meadows, oak woodland, and mixed conifer forest. Rainfall in this region varied seasonally and by elevation, with an average of 82.7 cm annually . Mean temperatures ranged between 3.9-20.6°C in 2018–2019 .The small-scale, private-land cannabis farms for this study included one licensed recreational production site, one medically licensed production site, and six unlicensed sites. All farms were producing cannabis for sale, though in different markets depending on their access to licensed markets. We also had cameras placed in three hemp fields next to cannabis farms. We selected these eight cannabis farms because they: were representative of the size and style of cultivation predominant in Josephine County in the years immediately following recreational legalization in 2015 , were all established after recreational legalization except for the medical farm, did not replace other plant-based agriculture, granted us permission to set up cameras on site, and were located next to a large section of unfarmed land that could grant researchers access in order to place cameras across a gradient of distance to cannabis farms.