Species distribution of Cannabis sativa: Past, present and future

Anna Halpin-McCormick; Tai McClellan Maaz; Michael B Kantar; Kasey E Barton; Rishi R Masalia; Nick Batora; Kerin Law; Eleanor J Kuntz

doi:10.1371/journal.pone.0306007

. 2025 Mar 13;20(3):e0306007. doi: 10.1371/journal.pone.0306007

Species distribution of Cannabis sativa: Past, present and future

Anna Halpin-McCormick ¹, Tai McClellan Maaz ¹, Michael B Kantar ^1,^*, Kasey E Barton ², Rishi R Masalia ³, Nick Batora ³, Kerin Law ³, Eleanor J Kuntz ³

Editor: Andrea Mastinu⁴

PMCID: PMC11906060 PMID: 40080480

Abstract

Cannabis sativa L. is an annual flowering herb of Eurasian origin that has long been associated with humans. Domesticated independently at multiple locations at different times for different purposes (food, fiber, and medicine), these long-standing human associations have influenced its distribution. However, changing environmental conditions and climatic fluctuations have also contributed to the distribution of the species and define where it is optimally cultivated. Here we explore the shifts in distribution that C. sativa may have experienced in the past and explore the likely shifts in the future. Modeling under paleoclimatic scenarios shows niche expansion and contraction in Eurasia through the timepoints examined. Temperature and precipitation variables and soil variable data were combined for species distribution modeling in the present day and showed high and improved predictive ability together as opposed to when examined in isolation. The five most important variables explaining ~ 65% of the total variation were soil organic carbon content (ORCDRC), pH index measured in water solution (PHIHOX), annual mean temperature (BIO-1), mean temperature of the coldest quarter (BIO-11) and soil organic carbon density (OCDENS) (AUC = 0.934). Climate model projections where efforts are made to curb emissions (RCP45/SSP245) and the business as usual (RCP85/SSP585) models were evaluated. Under projected future climate scenarios, shifts worldwide are predicted with a loss of ~ 43% in suitability areas with scores above 0.4 observed by 2050 and continued but reduced rates of loss by 2070. Changes in habitat range have large implications for the conservation of wild relatives as well as for the cultivation of Cannabis as the industry moves toward outdoor cultivation practices.

Introduction

Cannabis sativa is an annual diecious herb of Eurasian origin and inhabits a range of distinct geographies and climates [1–3]. These environments are characterized by having a good water supply and a range of soils that tend to be well-drained, nitrogen-rich, loamy and alluvial [4,5]. Humans have long had a relationship with Cannabis with the first observations of seeds associated with pottery fragments dated to ~ 10,000 years ago [4,6,7]. This long human use has made the taxonomy of the Cannabis genus a major question. Historically, it has been broadly divided into two types, hemp- or drug-type, with early descriptions dating back to Linnaeus (1753) and Lamarck [4,8,9]. Linnaeus described the plants from Northern Europe as Cannabis sativa and Lamarck described plants from India as Cannabis indica. In 1924 an additional purported wild species growing in central Russia was described by Janischevsky [10] and termed Cannabis ruderalis. At the time, morphological differences between these three taxa led to the proposition of multiple species (sativa, indica and ruderalis) however, more recent work supports the rank of subspecies [11] with genetic diversity occurring across a latitudinal gradient along which classic differentiating phenotypes occur [12]. Due to the cross fertility of the proposed multispecies, Cannabis is now more commonly considered a monotypic genus [8,13,14]. The domestication of Cannabis occurred for fiber, seed and cannabinoid content [4,15,16]. The divergence of hemp and drug-type Cannabis ancestors from wild populations occurred ~ 12,000 years ago, followed by a separation of hemp and drug-type gene pools occurring ~ 4,000 years ago [16]. Hemp-type Cannabis can be further sub-classified into Narrow Leaf Hemp (NLH) or Broad Leaf Hemp (BLH) with all hemp types classified as C. sativa ssp sativa. Pollen grains in the archeological record has revealed that NLH spread out of the putative ancestral zone in Central to Northern Eurasia moving westward across into mainland Europe around 6 million years ago (MYA) whereas in contrast BLH spread into Southern China and Southeast Asia about 1.2 MYA [3,17]. Similarly, drug-type Cannabis can be divided into Narrow Leaf Drug (NLD) originating from South Asia (named “sativa” in the recreational market) and Broad Leaf Drug (BLD) originating from Central Asia (referred to as “indica” in the recreational market) [18]. The NLD varieties are typically found along the Himachal Pradesh with a range that expands into the Montagne regions of Northern India, Uttarakhand, Nepal, Sikkim, Bhutan and into the Arunachal Pradesh [3]. On the other hand, BLD varieties are reported to have spread from Jammu and Kashmir into Pakistan and Afghanistan [3]. Cannabis occurs in a range of diverse habitats, from cold and dry climates with short growing seasons in temperate regions to warm and wet climates with longer growing seasons in the tropics [4]. Since its divergence from Humulus Lupulus between 18.23 – 25.4 MYA [12,19], Cannabis has been exposed to many different environmental stressors. It has been hypothesized that following the last glacial maximum (LGM), Cannabis plants migrated into and persisted in refugia sites (e.g., Hengduan Mountains, Yungui Plateau, Caucasus Mountains, northern Mediterranean peninsula) until conditions were favorable once again for range expansion [4]. Habitat fragmentation and isolation driven by changing climate may have aided population separation and driven adaptive divergence among sub-species.

Currently, Cannabis cultivation occurs in both outdoor and indoor settings with hemp plants producing fibers and seed oils usually cultivated outdoors and high value medicinal and recreational drug-type plants often cultivated indoors or in more controlled farming systems [20,21]. Despite this widespread cultivation, no prior species distribution modeling for Cannabis has been published for present day or future scenarios. As outdoor cultivation becomes the standard practice, species distribution maps can help define and rank the importance of critical environmental properties and identify where these suitable environments may exist around the world currently and how they are likely to be affected by changing climate in the future. Further, there has been increased scientific interest in the last decade around understanding the soil determinants important for Cannabis cultivation [22].

Under climate change it is very possible that in the future, different regions may be more appropriate for different crops [23]. Species distribution models (SDMs) provides a method for informed land selection by identifying regions with favorable climatic and soil conditions and providing a suitability score for identifying these regions. SDMs can also support conservation decision making by identifying where other suitable habitats may be for endangered species and identifying the rank in important environmental factors involved in their distribution. Additionally, shifting climate also requires long term planning to prepare for future changes in agricultural requirements for outdoor production. Currently, one major region for Cannabis cultivation is in California, in particular the Emerald Triangle. This state has become a major cultivation center in recent history and continues to be one of the largest Cannabis markets in the world with $5.3 billion in sales in 2022 [24]. However, it is currently unknown how future climate changes may impact Cannabis cultivation and conservation globally and what are the most important environmental variables that explain Cannabis species distribution. Therefore, the objectives of this study were twofold 1) integrate publicly available soil data and climate data, past, present, and future with wild Cannabis occurrence points to understand how regions of the world change in suitability for Cannabis through time and 2) specifically explore present-day and future suitability for Cannabis cultivation globally and in the state of California, as this state is known worldwide for its outdoor cultivation.

Materials and methods

Occurrence points

Occurrence data were obtained from iNaturalist (GBIF.org - https://doi.org/10.15468/dl.d8n6hx). This dataset contained 416 occurrence points which had paired images for each occurrence point (Table S1). Of these, 302 were deemed as wild or escapees and growing without human intervention (S2 Table). Plants were classified as wild if image inspection revealed no visible man-made objects of any kind, the plant was growing amongst other plants and the landscape appeared unmanaged. After removing duplicates there were 234 observations. After filtering for a longitude greater than zero, 137 observations remained and were used for SDM construction (S3 Table ; S1 Fig). Eurasia is considered the center of origin of Cannabis, which provided rationale for including only data points with a longitude greater than zero in this study.

Environmental variables

Occurrence points were used to query the datasets examined in this study which included the WorldClim 2.1 climate data (all 19 bioclim variables for temperature and precipitation, S4 Table) as well as monthly climate data for solar radiation, wind speed, water vapor pressure and elevation. Data was downloaded at the highest available spatial resolution of 30 seconds (~1 km²) [25]. Soil properties were downloaded from the global soil database ISRIC World Soil [26] (S5 Table). Paleoclimate data were sourced from paleoclim.org with the highest spatial resolution of 2.5 arc-minutes (~5km) downloaded [27] (S6 Table). Future climate data were sourced from the WorldClim repository for SSP245 (mitigation) and SSP585 (business as usual) for 2050 (averages for 2041-2060) and 2070 (averages for 2061-2080) at a spatial resolution of 30-arc seconds. Future climate projections were based on the Intergovernmental Panel on Climate Change (IPCC) sixth assessment report (AR6) and used shared socioeconomic pathways (SSPs) for different global climate models (CMIP6). California soil orders were downloaded from https://databasin.org. Map overlays were created using the ‘raster’, ‘rworldmap’, ‘ggplot2’, ‘sf’ and ‘mapdata’ packages in RStudio [28].

Model building

These bioclimatic and soil data were used to create species distribution models (SDM) using the software Maxent (Version 3.4.4 [29] in RStudio (Version 2022.2.0.443 [28]). Suitability maps were created using the Maxent software (Version 3.3.4). For historic distribution models, present day latitude and longitude data points (n = 137) were used under the assumption that where Cannabis grows wild today may reflect where it could have grown in the past. Suitability maps were overlaid for the present day (1970-2000), 2050 and 2070, with a suitability cutoff score of 0.2. Acceptable suitability is defined as 0.2 for cultivated regions [30] and 0.4 for natural areas [31].

Model evaluation

Model quality was explored using area under the curve (AUC) and the standard deviation of the AUC across replicates (SDAUC). A good model required an AUC ≥ 0.7 and SDAUC < 0.15. Shape files used for cropping raster extents included the World Administrative Boundaries, Countries and Territories shape file and the United States shapefiles (states_21basic). Asia and Russia and Europe and Southwest Asia shape file were retrieved from Stanford University EarthWorks (2008). Detailed World Polygons (LSIB) Europe and Southwest Asia, 2013. [Shapefile]. United States. Department of State. Office of the Geographer. Humanitarian Information Unit). For assessing habitat reduction, the ‘rasterio; and ‘numpy’ packages were used in PyCharm (version 2023.3.3) to identify pixels above the threshold examined (above 0.4 suitability). These files were then imported into RStudio where pixel counts were converted to km² accounting for the curvature of the earth over the range of latitudes in each file. All code is available at https://github.com/ahmccormick and high resolution figures are available at https://figshare.com/authors/Anna_H_McCormick/17741367.

Results and discussion

Historic changes in the distribution of Cannabis

Eurasia is the putative ancestral zone for Cannabis and thus was the focus for the historical timepoints (S6 Table, S11 Fig). Across this geography there were many changes in the extent of suitable native habitat over geologic time. At 3.3 million years ago (mya) high potential suitability was observed in west and central Eurasia, as well as temperate South and East Asia (S11A Fig). At 3.2 million years ago during the Mid-Pliocene Warm Period (mPWP), high potential suitability remained in West-central Eurasia (S11B Fig), as well as temperate South and East Asia. There is also an increase in potential suitability in the Tibetan Plateau region (S11B Fig) as well as in Bangladesh and Northern Myanmar. The mPWP offers an opportunity to examine how a warmer than present world may have affected species distribution, as climate models estimates for this time show that the mean surface temperature globally was between 2.7- 4.0 °C higher than present along with high atmospheric CO₂ concentrations (350-450 ppm compared to 280 ppm pre-industrial revolution)[32]. Highlighting the significance of the increase in global surface temperature during the mPWP, annual temperature explained nearly 60% of the variation during the mPWP (S12B Fig). Between 3.2 million and 787,000-years ago there was a loss of lower suitability ranges (0.2 - 0.4) along the Tibetan Plateau. Habitat changes likely facilitated separation of Eurasian Steppe populations from those in China and the Himalayan Mountains (S11B and S11C Fig). Such habitat fragmentation likely contributed to adaptive divergence, however, throughout the entire time series there remain areas between east and west Eurasia where potential suitability was likely sufficient ( > 0.4) to maintain gene flow, preventing full speciation.

At 787,000 years before present (MIS19) there is a loss of broad potential suitability in the Tibetan Plateau region, however, western Eurasia, north east China, northern India, Nepal and North and South Korea maintain high potential suitability (S11C Fig). This same patter occurs 130,000 years before present (S11D Fig). At the height of the Last Glacial Maximum, 21,000 years ago the outline of the descending ice sheet is visible in the Northern latitudes, with a loss of potential suitability observed in the more northerly Eurasian Steppe regions (S11E Fig). This ice age event also coincides with a reduction in high suitability (>0.7) in north east China. Previous work suggested that Cannabis populations may have been driven into glacial refugium in the Caucasus Mountain region in Europe and east of the Himalayan foothills in the Hengduan Mountain region [4]. The Hengduan mountains of southwest China is a biodiversity hotspot [14] and had a high potential suitability during this period likely supporting refugial Cannabis populations during the Last Glacial Maximum (LGM)[4]. The Himalayan Mountain system also remained suitable; however, it did exhibit a reduction in the more western regions of the Himachal Pradesh (S11E Fig). Between 17,000 – 300 years ago (S11F-K Fig), potential suitability did not change substantially, likely due to the shorter length of the time steps. Despite the relative stability there was an overall expansion in East Asia during the Heinrich-Stadial (S11F Fig) and contraction during the Bolling-Allerod (Fig 1G). This pattern of expansion in East Asia continued after the Younger Dryas Stadial (12,900-11,700) (S11H Fig) and was visible from the Early-Holocene (S11I Fig), Mid-Holocene (8,326 – 4,200) (S11J Fig), the late Holocene (S11K Fig) and into the Anthropocene (S11L Fig). Variable contribution and AUCs for each era can be found in S12 Fig.

Fig 1 — (A) Maxent generated suitability map for *Cannabis* cultivation worldwide for the present day using WorldClim version 2 climatic and ISRIC soil variable data compiled (B) Standard deviation for the merge of the WorldClim and ISRIC SoilGrid variables together. (C) Variable contribution of the WorldClim and ISRIC SoilGrid variables together (D) AUC for the model.

Different eras showed different types of range fluctuations, for example East Asia in particular shows large suitability fluctuations during the LGM (S11E Fig) as compared to the Heinrich Stadial (S11F Fig), Bolling-Allerod (S11G Fig) and Younger Dryas Stadial (S11H Fig). High potential suitability is consistently observed in the Eurasian Steppe region and while the range of potential suitability changes throughout the Holocene (S11I-K Fig), it is maintained through all the timepoints examined here (S11A-K Fig). This is similarly the case for north east China and the Himalayan Mountain system (S11A-K Fig). It is therefore possible that Cannabis may have had access to a much wider habitat range during this time than previously thought and perhaps during the LGM (S11E Fig). The models here also correspond well to the subfossil pollen records which converge at the northeastern Tibetan Plateau as a proposed center of origin [17]. From here Cannabis it is thought to have first dispersed west to Europe by 6 MYA and to eastern China by 1.2 MYA [17]. The development of agricultural practices and the establishment of trade routes along the Eurasian Steppe (e.g., Silk Road) likely facilitated range expansion. Archeological evidence, including carbon dated pollen samples across the species distribution range modeled here is still needed to support and validate the findings presented in this study.

Current Habitat Suitability of Cannabis.

Distributions were constructed using current (1970—2000) bioclimatic (temperature and precipitation) and soil properties separately (S2A-B, S3A-B, S4A-B Fig) and together (Fig 1) to explore global suitability. Six temperature and precipitation variables (BIO-1, BIO-11, BIO-10, BIO-18, BIO-19, BIO-14 - see S4 Table for definitions) explained ~ 81% of the total variation (S3A Fig), with an AUC of 0.9 (S4A Fig). When exploring bioclimatic variables alone, highest suitability was found in mixed deciduous forest, temperate forest steppe and taiga of Eurasia and North America (S2A Fig). When exploring soil alone, four variables (ORCDRC, PHIHOX, OCDENS, CECSOL - see S5 Table for definitions) explained ~ 79% of the total variation (S3B Fig) with an AUC of 0.939 (S4B Fig). Here suitability was also found in the mixed deciduous forest, temperate forest steppe and taiga of Eurasia and North America (Fig 2B).

Fig 2 — (A) Worldwide suitability map for 2050 (B) Standard deviation for the worldwide suitability map for 2050 (C) Worldwide suitability map for 2070 (D) Standard deviation for the worldwide suitability map for 2070.

When climate and soil variables were combined (Fig 1) the most important variables were soil organic carbon content (ORCDRC), pH index measured in water solution (PHIHOX), annual mean temperature (BIO-1), mean temperature of the coldest quarter (BIO-11) and soil organic carbon density (OCDENS) (Fig 1C). With an AUC of 0.934 these five variables explained ~ 65% of the total variation (Fig 1D). Integrating these two datasets we see a notable improvement in model performance (Fig 1A), specifically in mitigating the occurrence of anomalous suitable areas, for example those previously identified at high latitudes of Northern Canada for the ISRIC soil dataset when examined in isolation (S2B Fig). Cannabis is thought to have had a Central Asian Origin [11,33] or an origin region spanning Eurasia [34,35]. Here, high broad suitability (above 0.4) is seen in these regions with a range spanning Europe (S6 Fig), Russia and Asia (Fig 1A, 4A, S6). The patterns of suitability observed matches present-day reports of extant Cannabis populations [4]. High suitability is also seen in other continents such as regions of North America (Fig 1A, 4A, S9).

Fig 4 — (A) Worldwide suitability map (B) Suitability map for the United States and (C) Suitability map for the state of California, where green represents present day suitability (above 0.2), Blue represents suitability (above 0.2) for 2050, red suitability (above 0.2) for 2070 and purple the overlap of suitability for 2050 and 2070 (D) Soil orders for the state of California. Maps were generated using Maxent generated using WorldClim Bioclimatic (temperature and precipitation) variables and ISRIC.

To examine how other environmental variables may affect the model, additional environmental properties namely; solar radiation (kJm -2/day) (S2C Fig), wind speed (m/s) (S2D Fig ), water vapor pressure (kPa) (S2E Fig), and elevation (m) (S2F Fig) were acquired. Bringing together all of these environmental properties, a similar pattern of suitability is observed across Eurasia and North America. However, there is a more pronounced variability in the standard deviation for the model worldwide (S5B Fig) in contrast to the more localized variations seen in the bioclimatic and soil standard deviations (Fig 1B). Examining variable contribution across all 73 layers (S6A Fig) shows that 6 of the top 10 contributors are bioclimatic variables (BIO-1, BIO-9 and BIO-11) and soil (ORCDRC, PHIHOX, PHIKCL) properties (Fig 6A) and suggests that integrating bioclimatic and soil properties may reduce background errors that could arise when a broader array of variables is included in the modeling process and which have low variable contributions.

In the important production area of California, good suitability occurs in many counties including Sonoma, Napa, Marin County, San Meteo, Almeda, Monterey, Suter, Yuba and Butte, San Luis Obispo, Santa Barbara, Placer, Mendocino and Humboldt County and almost all coastal counties (Fig 4C, S10A). Additional states which are known for outdoor cultivation were also examined for suitability for all six environmental properties examined namely; Colorado (S10B Fig), Maine (S10C Fig), Oregon (S10D Fig), Washington (S10E Fig ), Massachusetts (S10F Fig) and Michigan (S10G Fig).

Suitability and Soil.

Like many plant species, making use of soil data increases distribution model performance [36]. Important soil determinants for Cannabis suitability included soil pH, soil organic carbon, and cation exchange capacity (CEC) (S3B Fig), which together regulate soil fertility and nutrient cycling in soil. Others have previously reported that Cannabis favors growing in alluvial soils under slightly acidic conditions in temperate and subtropical regions [4]. Alluvial soils can be rich in organic matter and plant nutrients, and thus relatively fertile [37] and such soils are found along the Himachal Pradesh [38] and in many places within the global model that have good suitability for Cannabis cultivation. Taxonomically, alluvial soils are diverse and fall under an assortment of soil classes [37]. Likewise, in the case of Cannabis, higher suitability is observed in regions defined by an array of classifications, including the soil orders of Mollisols (Central Eurasian Steppe and North America), Inceptisols and Ultisols (both found predominantly in southern Russia, Mongolia, and China), with some smaller suitable regions found in areas consisting of Alfisols and Spodosols (Western Eurasian steppe) (S2B Fig). This range in soil taxonomic classifications may highlight the plastic and generalist nature of Cannabis. In California, we found a correlation between Cannabis suitability and the soil orders of Inceptisols, Mollisols, Alfisols, Aridisols, and Ultisols (Fig 4C-D). The weakly developed Inceptisols of California have a wide range of characteristics, but are mostly associated with steeply sloped chaparral or montane conifer forests and along streams [39]. Certain Inceptisols in Northern California were also developed from volcanic deposits (Andisols) and have slightly acidic pH with highly variable cation exchange capacity. Mollisols define a large proportion of the suitability score that we see in California (Fig 4C-D), North America (Fig 2B), and the Eurasian Steppe (S2B Fig). Mollisols are inherently fertile with over 50% base saturation, abundant organic matter, and near neutral pH; whereas moderately weathered Alfisols have more than 35% base saturation with clay rich subsoil horizons with high cation exchange capacity having undergone less intensive leaching than Ultisols [40,41]. There are a number of limitations and biases associated with the climate and soils data that need to be considered. The limitations include the resolution of the bioclimatic and biophysical data and the assumptions made in the interpolation of these data. This is particularly true for biophysical (soil) variables, which likely vary over finer scales than the database which impact the interpolation techniques [42]. Further, the limited sample size of the wild occurrences may lead to increased variability.

Suitability of Cannabis under Future Climate Scenarios.

Future distributions were constructed for both 2050 and 2070 under two emission scenarios (SSP245 - curbing emissions and SSP585 - business as usual). Under SSP245 there is decreasing suitability by 2050 (Fig 2A) and 2070 (Fig 2C). Under SSP585 there is an even greater decrease in suitability by 2050 (Fig 3A) and 2070 (Fig 3B). Future climate datasets represent climatic variables only, as soil properties are more challenging to estimate. Despite the loss of suitability in the future there was overlap between suitability ranges in 2050 and 2070 for both SSP scenarios (Fig 4A).

Fig 3 — (A) Worldwide suitability map for 2050 (B) Standard deviation for the worldwide suitability map for 2050 (C) Worldwide suitability map for 2070 (D) Standard deviation for the worldwide suitability map for 2070.

Partitioning by subregions such as Asia and Russia (S7A-E Fig), Europe and SE Asia (S8 A-E Fig) and the United States (S9A-E Fig) allows for a more quantitative examination of the changes these areas may experience from the present day moving into 2050 and 2070. The rasters generated for each climate scenario (SSP45 and SSP85) and each future time point (2050 & 2070) were filtered for suitability values above 0.4, which is considered an acceptable suitability value for natural growth. Pixel counts were performed and converted into km², accounting for the curvature of the earth within the range of latitudes in the partition. Worldwide across all climate models and timepoints we see an average of ~ 43% reduction in suitable area (S7 Table) with a reduction from ~ 13.8 million to ~ 7.8 million km². Within the partition of Asia and Russia a ~ 29% reduction in suitable area from ~ 6.4 million to ~ 4.5 million km² is observed (S7A-E Fig, S7 Table) and similarly in Europe ~ 15% losses are observed with a reduction from ~ 2.5 million to ~ 2.2 million km² (S8A-E Fig, S7 Table). In the United States an average of ~ 81% loss in suitable area is observed with a reduction from ~ 2.8 million to ~ 0.5 million km² (S9A-E Fig, S7 Table).

Within the United States, there was a contraction in suitability (Fig 4B, S8A-E Fig), although, suitability remains favorable in areas of California (Fig 4C), with many important production counties showing favorable suitability scores (e.g., Humboldt, Mendocino Sonoma, Marin, Santa Barbara, and Ventura) (Fig 4C). Again, Cannabis suitability was correlated with areas where Inceptisols, Mollisols, Vertisols, Ultisols and Alfisols are predominantly found (Fig 4C-D).

Implications for Future Cultivation.

Globally combining climate and soil variables provides improved resolution (Fig 1A). The observed suitable areas (>0.4) match common agricultural lands known for Cannabis cultivation in Asia and Europe and captured specific geographies where genetic studies have shown the presence of drug-type feral Cannabis populations [16]. The overlap between suitability for 2050 and 2070 suggests that the effects on Cannabis distribution due to changing climate will have mostly taken effect by 2050 (Fig 4). Moving into the future, in North America, the states of North Dakota, Minnesota, Wisconsin, and Michigan show high suitability (Fig 4B).

Areas in the states of Colorado, Wyoming and California show moderate suitability for the present day, however, moving into the future these areas show decreasing suitability (S9 Fig). California is known worldwide for its cultivation of Cannabis. Proposition 64 in 2016 brought legalization to the recreational use of Cannabis and saw a movement towards legal operations in the production and sales of Cannabis. Exploring this region more specifically we observed an increase in suitability from the present day to 2050 and 2070 in areas such as Marin County, Contra Costa, San Mateo, Santa Barbara and Ventura (Fig 4C). Counties which currently produce Cannabis may be impacted under different climate change scenarios in the future. However, future models here only take into account abiotic factors and biotic pressures will also have significant roles in future species distribution.

Conclusion

Global suitability for Cannabis was explored with the intent of identifying regions where Cannabis cultivation could be facilitated by suitable climate and soil properties. Across Eurasia and the United States there will be broad loss of suitability by 2050 for two different emission scenarios (SSP245 and SSP585). As suitable habitat decreases, conservation of wild relatives and naturalized populations of Cannabis around the world will be critical for the preservation of diversity and act as a valuable source of variation for trait improvement as the industry continues to develop. Similar to previous work we see an expansion and contraction at known times of historical climate fluctuations [43]. Using bioclimatic and biophysical variables has shown to be effective in conservation efforts to help identify areas that should have priority for conservation [44, 45]. This principal has the same potential benefit for identifying priority areas for cultivation under climate change.

Supplemental information

S1 Fig

137 observations used for SDM model construction with a longitude greater than zero. Fig S2. Individual species distributions for each set of environmental properties examined (A) WorldClim Bioclimatic variables (B) ISRIC soil data (C) Solar radiation (kJm²/day) (D) Wind speed (m/s) (E) Water vapor pressure (kPa) (F) Elevation suitability maps. These maps were generated with Maxent using Worldclim and ISRIC data. Fig S3. Variable contribution graphs for each set of environmental properties examined (A) WorldClim Bioclimatic variables (B) ISRIC soil data (C) Solar radiation (kJm²/day) (D) Wind speed (m/s) (E) Water vapor pressure (kPa) and (F) Elevation. Fig S4. Area under the curve graphs each set of environmental properties examined (A) WorldClim Bioclimatic variables (B) ISRIC soil data (C) Solar radiation (kJm²/day) (D) Wind speed (m/s) (E) Water vapor pressure (kPa) and (F) Elevation. Fig S5. Overlay of all six environmental datasets (A) Worldwide plot (B) standard deviation for the overlay of all six environmental variables. These maps were generated with Maxent using Worldclim and ISRIC data. Fig S6. Overlay of all six environmental datasets (A) Variable contribution graph (B) Area under the curve graphs each set of environmental properties examined. Fig S7. Species distribution with temperature and precipitation data in Asia and Russia for (A) present day (B) SSP45 2050 (C) SSP45 2070 (D) SSP85 2050 (E) SSP85 2070. These maps were generated with Maxent using Worldclim data. Fig S8. Species distribution with temperature and precipitation data in Europe for (A) present day (B) SSP45 2050 (C) SSP45 2070 (D) SSP85 2050 (E) SSP85 2070. These maps were generated with Maxent using Worldclim data. Fig S9. Species distribution with temperature and precipitation data in the United States for (A) present day (B) SSP45 2050 (C) SSP45 2070 (D) SSP85 2050 (E) SSP85 2070. These maps were generated with Maxent using Worldclim data. Fig S10. Species distribution for a subset of the United States with data for all six environmental properties examined (A) California (B) Colorado (C) Maine (D) Oregon (E) Washington (F) Massachusetts (G) Michigan. These maps were generated with Maxent using Worldclim data. Fig S11. (A) Pleistocene: M2 (ca. 3.3 Ma) (B) Predicted Distribution for the Paleoclimate timepoint of the Mid Pliocene warm period (ca. 3.2 Ma) (C) Predicted Distribution for the Paleoclimate timepoint of the Pleistocene: MIS19 (ca. 787,000 years ago) (D) Predicted Distribution for the Paleoclimate timepoint of the Pleistocene: Last Interglacial (130,000 years ago) (E) Predicted Distribution for the Paleoclimate timepoint of the Pleistocene: Last Glacial Maximum (ca. 21,000 years ago) (F) Potential Distribution for the Paleoclimate timepoint of the Pleistocene: Heinrich Stadial (14,700 – 17,000 years ago) (G) Potential Distribution for the Paleoclimate timepoint of the Pleistocene: Bolling-Allerod (12,900 – 14,700 years ago) (H) Potential Distribution for the Paleoclimate timepoint of the Pleistocene: Younger Dryas Stadial (11,700 – 12,900 years ago) (I) Potential Distribution for the Paleoclimate timepoint of the Pleistocene: Early Holocene, Greenlandian (8,366 - 11,700 years ago) (J) Potential Distribution for the Paleoclimate timepoint of the Pleistocene: Mid Holocene, Northgrippian (4,200 – 8,326 years ago) (K) Potential Distribution for the Paleoclimate timepoint of the Pleistocene: Late Holocene, Meghalayan (300 – 4200 years ago). These maps were generated with Maxent using Worldclim data. Fig S12. AUC and variable contribution graphs for each timepoint from the Paleoclim dataset (A) Pleistocene: M2 (ca. 3.3 Ma) (B) Mid Pliocene warm period (ca. 3.2 Ma) (C) Pleistocene: MIS19 (ca. 787,000 years ago) (D) Pleistocene: Last Interglacial (130,000 years ago) (E) Last Glacial Maximum (ca. 21,000 years ago) (F) Heinrich Stadial (14,700 – 17,000 years ago (G) Bolling-Allerod (12,900 – 14,700 years ago) (H) Younger Dryas Stadial (11,700 – 12,900 years ago) (I) Early Holocene, Greenlandian (11,700-8,326 years ago) (J) Mid Holocene, Northgrippian (4,200 – 8,326 years ago) (K) Late Holocene, Meghalayan (300 – 4200 years ago).

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pone.0306007.s001.zip^{(122MB, zip)}

S2 File

Table S1. 416 occurrence points from iNaturalist which had paired images. Table S2. Latitude and longitudes of 302 occurrence points deemed to be growing wild without human intervention. Table S3. Latitude and Longitude for the 137 observations used for SDM model construction, post-filtering for a longitude greater than zero. Table S4. WorldClim2 Bioclimatic variable definitions. Table S5. ISRIC soil variable definitions. Table S6. Paleoclimate Time Period and date range Table S7. Pixel counts for present day, SSP 45 and SSP85 for 2050 and 2070 above the 0.4 threshold for the world and the partitions of Asia & Russia, Europe and SE Asia and the United States

(XLSX)

pone.0306007.s002.xlsx^{(77.7KB, xlsx)}

Acknowledgments

We would like to thank Koa the University of Hawai’i (UH) high performance computing (HPC) cluster.

Data Availability

All code and data is available at https://github.com/ahmccormick and high resolution figures are available at https://figshare.com/authors/Anna_H_McCormick/17741367.

Funding Statement

The author(s) received no specific funding for this work.

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PLoS One. 2025 Mar 13;20(3):e0306007. doi: 10.1371/journal.pone.0306007.r001

Author response to Decision Letter 0

8 Jun 2024

PLoS One. doi: 10.1371/journal.pone.0306007.r002

Decision Letter 0

Andrea Mastinu

1 Aug 2024

PONE-D-24-23287Species Distribution of Cannabis sativa: Past, Present and futurePLOS ONE

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Reviewer #1: The article attempts to depict the past, present, and future distribution of hemp species using available global data. Although the paper is well-structured, I found the results and discussion sections difficult to follow due to the numerous figures and tables. I suggest adding a separate section focused directly on the discussion. The methodology is straightforward, but there is a significant gap in training data from other continents, which the authors did not address or discuss the implications of. While the authors used freely available global data, the accuracy of this data, especially soil information, is questionable and should be properly discussed in the paper.

Introduction

- "Cannabis now considered a monotypic genus". This is still a matter of debate, particularly when we consider Cannabis ruderalis. Please add more context here.

- "Identifying the best climatic and soil conditions for growth informs nutrient and water management and approaches such as species distribution modeling (SDM) providing a means for more informed land selection that may facilitate minimizing negative environmental externalities (Mehrabi et al., 2019)" This sentence contaisn many ideas and the reference seems to be for sunflower. Please re-write it and provide a more relavant reference.

- Instead of just pasting the link, provide a proper citation for the Economist article about Cannabis market in California.

- Whilst the introduction section is well organised, it lacks depth and proper linkage to the 'knowledge gap' the article is trying to fill. More importantly, the focus of the article seems to be a study of status and shift of cultivation at the global level. Bud the authors suddenly add information about the cultivation center in California. I suggest the authors expand the introduction section and provide a better linkage to the aim and objectives of the study.

Methodology

- Please provide a proper citation for the GBIF occurrence data.

- Were there other occurrence data for C. Sative on GBIF?

- "Of these, 302 were deemed as wild or escapees and growing without human intervention " how they were deemed wild? what was the method?

- Table Table S3 137 in Supplementary Materials is empty!

- The ISRIC soil data were old. Can you use OpenLandMap or https://soil.copernicus.org/articles/7/217/2021/?

- Please create proper citations for URLs in the methodology section.

- Why different sources was used for admin boundaries shapefiles? Was GADM database not comprehensive enough?

- Please add a better description of how historical SDMs were created. How they were validated?

Results and discussion

- Although both "suitability" and "species occurrrence" have been used interchangeably, suitability encompasses a much bigger area and in this context, I advice authors to avoid use of the word 'suitablity', particularly when they are referring to the past coditions.

Other comments:

- References are not ordered. That makes it difficult for the people to find

- There is no record in bibliography linked to Clare and Merlin, 2013: Do you mean Clarke, R. C., & Merlin?

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PLoS One. 2025 Mar 13;20(3):e0306007. doi: 10.1371/journal.pone.0306007.r003

Author response to Decision Letter 0

20 Aug 2024

Response To reviewer Comments

Editor:

Thank you for the opportunity to revise the manuscript. The comments were very helpful and we feel they have greatly improved the manuscript.

Comment 1: When submitting your revision, we need you to address these additional requirements. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and

https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

Response: We have modified the manuscript to fit the template and the reference scheme changed to numbers

Comment 2: Thank you for stating the following in the Competing Interests section: "LeafWorks Inc. is a for profit company." We note that you received funding from a commercial source: LeafWorks Inc. Please provide an amended Competing Interests Statement that explicitly states this commercial funder, along with any other relevant declarations relating to employment, consultancy, patents, products in development, marketed products, etc. Within this Competing Interests Statement, please confirm that this does not alter your adherence to all PLOS ONE policies on sharing data and materials by including the following statement: ""This does not alter our adherence to PLOS ONE policies on sharing data and materials.” (as detailed online in our guide for authors http://journals.plos.org/plosone/s/competing-interests). If there are restrictions on sharing of data and/or materials, please state these. Please note that we cannot proceed with consideration of your article until this information has been declared. Please include your amended Competing Interests Statement within your cover letter. We will change the online submission form on your behalf.

Response: We have changed the Conflict of Interest Statement to

“LeafWorks is a for profit company that provided funding to help with the computing for this manuscript. This does not alter our adherence to PLOS ONE policies on sharing data and materials.”

Comment 3: We note that Figures 1,2,3,4, S1,S2,S5,S7,S8,S9,S10 and S11 in your submission contain [map/satellite] images which may be copyrighted.

USGS National Map Viewer (public domain): http://viewer.nationalmap.gov/viewer/

The Gateway to Astronaut Photography of Earth (public domain): http://eol.jsc.nasa.gov/sseop/clickmap/

Maps at the CIA (public domain): https://www.cia.gov/library/publications/the-world-factbook/index.html and https://www.cia.gov/library/publications/cia-maps-publications/index.html

NASA Earth Observatory (public domain): http://earthobservatory.nasa.gov/

Landsat: http://landsat.visibleearth.nasa.gov/

USGS EROS (Earth Resources Observatory and Science (EROS) Center) (public domain): http://eros.usgs.gov/#

Natural Earth (public domain): http://www.naturalearthdata.com/

Response: These images do not actually contain satellite data, the input rasters are from worldclim and ISRIC and the images and colouring generated is based on the analysis done in this manuscript using Maxent. The viridis palette was used in the final plots.

Comment 4: Please include captions for your Supporting Information files at the end of your manuscript, and update any in-text citations to match accordingly. Please see our Supporting Information guidelines for more information: http://journals.plos.org/plosone/s/supporting-information.

Response: We have added the captions.

Reviewer #1 comments:

Comment 1: The article attempts to depict the past, present, and future distribution of hemp species using available global data. Although the paper is well-structured, I found the results and discussion sections difficult to follow due to the numerous figures and tables. I suggest adding a separate section focused directly on the discussion. The methodology is straightforward, but there is a significant gap in training data from other continents, which the authors did not address or discuss the implications of. While the authors used freely available global data, the accuracy of this data, especially soil information, is questionable and should be properly discussed in the paper.

Response: Thank you for this summary. We have added cavates regarding the soil data. The below has been added to the end of the suitability and soil section in the results & discussion section for this purpose.

“There are a number of limitations and biases associated with the climate and soils data that need to be considered. The limitations include the resolution of the bioclimatic and biophysical data and the assumptions made in the interpolation of these data. This is particularly true for biophysical (soil) variables, which likely vary over finer scales than the database which impact the interpolation techniques (Brady et al. 2005). “

Brady, K. U., A. R. Kruckeberg, and H. D. Bradshaw, Jr., 2005 Evolutionary ecology of plant adaptation to serpentine soils. Annu. Rev. Ecol. Evol. Syst. 36: 243–266.

As regards the significant gap in training data from other continents - as we are dealing with suspected wild plants, we wouldn't expect to use data points from other continents for training as the native range for cannabis is Eurasia. We have added the below into the text to elaborate on why eurasian data points were selected in the materials and methods

“After filtering for a longitude greater than zero, 137 observations remained and were used for SDM model construction (Table S3; Fig. S1). Eurasia is considered the center of origin of Cannabis, which provided rationale for including only data points with a longitude greater than zero in this study.”

Comment 2: "Cannabis now considered a monotypic genus". This is still a matter of debate, particularly when we consider Cannabis ruderalis. Please add more context here.

Response: Cannabis ruderalis could be considered in some more northern Russian latitudes where it grows freely, a wild relative. These plants have been used widely for the introgression of day neutrality in flowering time. Due to this fertility between all cannabis types when crossed (hemp, ruderal, recreational) it is clear there is no species barrier. There is considerable debate about if species delineations are therefore appropriate given this cross variety fertility. Only one paper to date (Ren et al, 2021) includes a genetic comparison of wild or feral, hemp type and THC-dominant type cannabis samples - and while the feral sampled varieties are most basal in the phylogeny, this genetic distance does not seem to affect crossing compatibility. The below has been included in the introduction to add more nuance

“Historically, it has been broadly divided into two types, hemp- or drug-type, with early descriptions dating back to Linnaeus (1753) and Lamarck (1785) (Linnaeus, 1753; Lamark, 1783). Linnaeus described the plants from Northern Europe as Cannabis sativa and Lamarck described plants from India as Cannabis indica. In 1924 an additional purported wild species growing in central Russia was described by Janischewsky (Janischewsky, 1924) and termed Cannabis ruderalis. At the time, morphological differences between these three taxa led to the proposition of multiple species (sativa, indica and ruderalis) however, more recent work supports the rank of subspecies (McParland, 2018) with genetic diversity occurring across a latitudinal gradient along which classic differentiating phenotypes occur (Zhang et al., 2018). Due to the cross fertility of the proposed multispecies, Cannabis is now more commonly considered a monotypic genus (Small, 2017; Barcaccia et al., 2020; Kocalchuck et al., 2020)”

Comment 3: "Identifying the best climatic and soil conditions for growth informs nutrient and water management and approaches such as species distribution modeling (SDM) providing a means for more informed land selection that may facilitate minimizing negative environmental externalities (Mehrabi et al., 2019)"

This sentence contains many ideas and the reference seems to be for sunflower. Please re-write it and provide a more relavant reference.

Response: We have rewritten the sentence

“Under climate change it is very possible that in the future different regions may be more appropriate for different crops (Pironon et al., 2019). Species distribution modeling (SDM) provides a method for informed land selection by identifying regions with favorable climatic and soil conditions and providing a suitability score for identifying these regions.”

Pironon, S., Etherington, T. R., Borrell, J. S., Kühn, N., Macias-Fauria, M., Ondo, I., et al. (2019). Potential adaptive strategies for 29 sub-Saharan crops under future climate change. Nature Climate Change, 9(10), 758-763.

Comment 4: Instead of just pasting the link, provide a proper citation for the Economist article about Cannabis market in California.

Response: We have changed this reference to (Walsh, 2023). Full reference is: Walsh, Dustin. "Michigan's weed market is now the top in the nation." Crain's Detroit Business, vol. 39, no. 33, 28 Aug. 2023, p. 0005. Gale OneFile: Health and Medicine, link.gale.com/apps/doc/A762882796/HRCA?u=albe12389&sid=googleScholar&xid=f17ee90f.”

No issue number could be found for the economist article or author, only the date it was published online: May 14th 2022 (https://www.economist.com/united-states/2022/05/14/in-california-the-worlds-largest-legal-weed-market-is-going-up-in-smoke)

Comment 5: Whilst the introduction section is well organised, it lacks depth and proper linkage to the 'knowledge gap' the article is trying to fill. More importantly, the focus of the article seems to be a study of status and shift of cultivation at the global level. Bud the authors suddenly add information about the cultivation center in California. I suggest the authors expand the introduction section and provide a better linkage to the aim and objectives of the study.

Response: The knowledge gap and rationale for this study was that no prior species distribution modeling for cannabis has been published for present day or future climate scenarios. We aimed to provide a quantitative analysis on how favorable ranges of Cannabis may change moving towards the various future climate scenarios. The introduction has been amended to include the below to better link the aim and objectives of the study and why the focus was directed to California more specifically

“Despite this widespread cultivation, no prior species distribution modeling for Cannabis has been published for present day or future scenarios”

“Species distribution modeling (SDM) provides a method for informed land selection by identifying regions with favorable climatic and soil conditions and providing a suitability score for identifying these regions. SDM can also support conservation decision making by identifying where other suitable habitats may be for endangered species and identifying the rank in important environmental factors involved in their distribution.”

“However it is currently unknown how future climate change may impact Cannabis cultivation and conservation globally and what are the most important environmental variables that explain Cannabis species distribution.”

Comment 6: Please provide a proper citation for the GBIF occurrence data.

Response: GBIF suggests this is how data to be cited: GBIF.org (20 April 2020) GBIF Occurrence Download https://doi.org/10.15468/dl.d8n6hx and this is what is referenced in the document. Please see below image for rationale.

Comment 7: Were there other occurrence data for C. Sative on GBIF?

Response: There are many other occurrence data for C.sativa on GBIF, however only data points which had associated images were included in this analysis, reducing the total number of GBIF records dramatically. Without being able to see the plants surroundings, it would be impossible to know if the plant was growing wild and without human intervention and so occurrence points with no associated image were not included.

Comment 8: "Of these, 302 were deemed as wild or escapees and growing without human intervention " how they were deemed wild? what was the method?

Response: For the data points used in the study, associated images for each latitude and longitude were individually examined and the data point was only included where it was clear the plant was growing wild and most likely without any human intervention. The call on wild was made if no man made objects of any kind were seen in the photo and that the plant appeared to be growing along with others or in a landscape that appeared to not be being managed. In table S1 the associated links to the images for each datapoint that was assessed are included. In this version of the tables, the link to the corresponding images is included for the 137 datapoints (Table S3) used in the downstream analyses.

The following has been added to the materials and methods “Plants were classified as wild if image inspection revealed no visible man-made objects of any kind, the plant was growing amongst other plants and the landscape appeared unmanaged.”

Comment 9: Table Table S3 137 in Supplementary Materials is empty!

Response: Apologies these data points have been included in this version of the tables.

Comment 10: The ISRIC soil data were old. Can you use OpenLandMap or https://soil.copernicus.org/articles/7/217/2021/?

Response: We thank the reviewer for pointing this out. We are not able to redo the analysis with this data, but we have added caveats about the data being used. The following has been added to the text of the discussion.

Comment 11: Please create proper citations for URLs in the methodology section.

Response: The links to worldclim and isric have been removed leaving just the primary references for these. A citation was possible for the EarthWorks shapefiles, but none was found for the world administrative boundaries shape file, however the link was removed as requested. Remaining links are for the source code github and high resolution figures.

Comment 12: Why different sources was used for admin boundaries shapefiles? Was GADM database not comprehensive enough?

Response: There was no specific reason for using different shapefiles other than prior knowledge of the world administrative boundaries and earthworks Standford sources.

Comment 13: Please add a better description of how historical SDMs were created. How they were validated?

Response: Historical SDM were created similarly to the present day SDM using Maxent. Present day latitude and longitude datapoints (n=137) were used under the assumption that where cannabis grows wild today may reflect where it could have grown in the past. The Paleoclimate was instead the input raster for the conditions at each time point. Without samples with geo-references and pollen dating, it is not possible to validate the historical SDM. This is why they are a supplemental figure. It is acknowledged that assumptions were made about present day and past ranges to conduct this analysis.

The following has been added to the text of the manuscript:

“Paleoclimate data were sourced from paleoclim.org with the highest spatial resolution of 2.5 arc-minutes (~5km) downloaded (Table S6, (Fordham et al., 2917)) with suitability maps were created using the Maxent software (Version 3.3.4). For historic distribution models, present day latitude and longitude data points (n=137) were used under the assumption that where cannabis grows wild today may reflect where it could have grown in the past.”

As well as the following to results and discussion section:

“Archeological evidence, including carbon dated pollen samples across the species distribution range modeled here is still needed to support and validate the findings present

Attachment

Submitted filename: Cannabis Distribution Response to Reviewer Comments PlosOne.docx

pone.0306007.s004.docx^{(291.8KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0306007.r004

Decision Letter 1

Andrea Mastinu

6 Nov 2024

PONE-D-24-23287R1Species Distribution of Cannabis sativa : Past, Present and futurePLOS ONE

Dear Dr. Kantar,

Please submit your revised manuscript by Dec 20 2024 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org . When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

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A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

We look forward to receiving your revised manuscript.

Kind regards,

Andrea Mastinu

Academic Editor

PLOS ONE

Journal Requirements:

Comments from PLOS Editorial Office: We note that one or more reviewers has recommended that you cite specific previously published works. As always, we recommend that you please review and evaluate the requested works to determine whether they are relevant and should be cited. It is not a requirement to cite these works. We appreciate your attention to this request.

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #2: (No Response)

Reviewer #3: (No Response)

Reviewer #4: (No Response)

Reviewer #5: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #2: Yes

Reviewer #3: Yes

Reviewer #4: Yes

Reviewer #5: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #2: Yes

Reviewer #3: Yes

Reviewer #4: Yes

Reviewer #5: No

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #2: Yes

Reviewer #3: Yes

Reviewer #4: Yes

Reviewer #5: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #2: Yes

Reviewer #3: Yes

Reviewer #4: Yes

Reviewer #5: Yes

**********

6. Review Comments to the Author

Reviewer #2: The manuscript entitled “Species Distribution of Cannabis sativa: Past, Present and future" used species distribution modeling approach together with relevant climate variables to construct the past and predict the current and future distribution patterns of Cannabis sativa. The manuscript is well written, and the drawn conclusions are coherent with the obtained results. Although similar methodologies are common, the results of the study could have useful implications for management actions. The manuscript requires some changes before it's ready for publication.

Abstract:

I suggest reporting the habitat areas (changes) in Kilometers for the past, current, and the future.

Introduction

- “(12)(Zhang et al., 2018).” Please either use ‘numbered’ reference style or ‘authors, date’ style.

Material and Methods

- “species distribution models (SDM)” should be “species distribution models (SDMs)”

- I suggest restructuring the methodology section into the following subsection:

1- Occurrence points

2- Environmental variables

3- Model building

4- Model evaluation (Area Under the Receiver Operating Curve (AUC)).

In this section, it's important to clarify what threshold was used to delineate the suitability and unsuitability areas.

Discussion

Implications for Future Cultivation : A small section highlighting the benefits of the applied modeling techniques in establishing priority zones for management actions is necessary. In addition, a couple of sentences on the limitations of the modeling technique is required. For this, I suggest:

https://doi.org/10.1016/j.ecoinf.2022.101930

https://doi.org/10.1007/s10661-024-12438-z

https://www.mdpi.com/2071-1050/14/21/14621

Reviewer #3: Species Distribution of Cannabis sativa: Past, Present, and Future (PONE-D-24-23287R1)

This manuscript presents a novel exploration of the historical, current, and projected future distributions of Cannabis sativa. By employing species distribution modeling (SDM) based on bioclimatic and soil variables, it offers insights into how changing climate conditions could impact the range and suitability of Cannabis habitats. The modeling under paleoclimatic scenarios, coupled with present and future climate projections, adds significant value to understanding both the ecological and practical dimensions of Cannabis cultivation and conservation.

The revised manuscript addressed many initial comments, particularly in methodology justification, result interpretation, and improvements to data selection rationale. The addition of comments on data limitations, particularly regarding soil information and gaps in training data, enhances the transparency of the methodology. However, some amendments remain necessary for clarity and accuracy.

I recommend that this revised manuscript be accepted for publication in PLOS ONE after addressing some remaining amendments for clarity and scientific rigor. The authors have responded well to previous comments, but additional refinements would further strengthen the manuscript.

• The manuscript currently includes both numerical citations and author-based in-text citations (e.g., “Zhang et al., 2018”), which creates inconsistency. Please unify the reference style, choosing either numerical or author-date citation for all references in accordance with PLOS ONE formatting guidelines.

• The manuscript would benefit from a clearer distinction between Cannabis species growing in the wild and those cultivated under controlled conditions. This differentiation is crucial, as cultivation practices allow for precise management of variables, whereas wild populations are subject to natural selection pressures and environmental variability. Expanding this discussion could clarify the ecological impacts and distinctions in Cannabis adaptation strategies.

• The authors used 137 occurrence points to model habitat suitability globally, which may be a limited representation, especially given the scale of the analysis. A discussion addressing whether this sample size sufficiently captures global suitability would be valuable, perhaps mentioning potential limitations and the implications of using Eurasian data exclusively for a global SDM.

• Although the paper explores past and present distribution shifts, it lacks a focused discussion on historical changes in the distribution of Cannabis. Integrating an analysis or discussion of historical fluctuations in its range, especially in response to past climatic shifts, could provide a more comprehensive context.

• While the authors effectively discuss how climate variables have influenced Cannabis distribution, soil properties such as organic carbon content and pH also vary over millennia. A brief discussion on how soil characteristics have changed historically could help contextualize the model’s results, as climate and soil variability are interrelated in shaping Cannabis habitats.

Reviewer #4: The manuscript, titled "Species Distribution of Cannabis sativa: Past, Present and Future," offers valuable insights into how this species’ distribution responds to climate change. By modeling the historical, current, and projected future ranges of Cannabis sativa, the study highlights key environmental factors that shape its distribution under different climate scenarios. This research adds meaningfully to our understanding of the expansion-contraction model, especially for species affected by environmental shifts.

In terms of modeling, the use of AUC as a performance measure is helpful; however, including AICc and ROC values would make the model evaluation even more robust. AICc would allow for a clearer comparison of model fit across alternatives, while the ROC curve would provide a more complete view of predictive performance. These additions could be especially useful for readers with an interest in model selection and accuracy.

Additionally, the discussion on expansion and contraction might benefit from referencing the study by Ülker, Tavşanoğlu, and Perktaş (2018) on Quercus robur ("Ecological niche modeling of Pedunculate Oak supports the ‘Expansion-Contraction’ model of Pleistocene biogeography"). Including this work may offer a useful comparison and add depth to the discussion of species distribution changes over time.

Finally, it’s clear that the authors have made considerable improvements based on previous feedback, especially in addressing the limitations of soil data, explaining data selection, and refining methodological details. These thoughtful updates have successfully resolved earlier concerns and have strengthened the manuscript overall.

Reviewer #5: In this study, authors have used various multi-source spatial and non-spatial data to investigate species Distribution of Cannabis sativa for Past, Present and future. It is an impressive compilation of information related with the documentation of Cannabis sativa. However, I suggest authors to include/correct following details in the revised manuscript.

1. Authors have used CMIP5 GCM dataset for future period. Cannabis are sensitive to temperature and humidity. Previous CMIP5 models have higher uncertainty in temperature and rainfall than CMIP6 GCMs. I suggest to apply dataset of CMIP6 GCMs, otherwise provide uncertainty in estimated suitability areas due to the use of CMIP5 GCMs.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean? ). If published, this will include your full peer review and any attached files.

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Reviewer #2: No

Reviewer #3: Yes: Hossein Bashari

Reviewer #4: No

Reviewer #5: No

**********

PLoS One. 2025 Mar 13;20(3):e0306007. doi: 10.1371/journal.pone.0306007.r005

Author response to Decision Letter 1

6 Nov 2024

Editor:

Thank you for the opportunity to revise the manuscript

Reviewer 2

Comment: The manuscript entitled “Species Distribution of Cannabis sativa: Past, Present and future" used species distribution modeling approach together with relevant climate variables to construct the past and predict the current and future distribution patterns of Cannabis sativa. The manuscript is well written, and the drawn conclusions are coherent with the obtained results. Although similar methodologies are common, the results of the study could have useful implications for management actions. The manuscript requires some changes before it's ready for publication.

Response: Thank you for the succinct summary

Comment: I suggest reporting the habitat areas (changes) in Kilometers for the past, current, and the future.

Response: For the present and day and future scenarios (both rcp and 2050/2070) we have in Table S7 the conversions to km2 worldwide as well as for the niches, as well as for specific areas of interest.

We have not done this for the past data however, as we feel that these estimations in the past for species distribution may not so easily translate accurately, given the changes in geographic landscapes in a deeper time context (rising ocean levels, glacial maximums etc). It may not be a fair estimate to compare the value in range changes in the past to the present given the occurrence of such major geological events outside of just climate properties.

Comment: “(12)(Zhang et al., 2018).” Please either use ‘numbered’ reference style or ‘authors, date’ style.

Response: Done

Comment: “species distribution models (SDM)” should be “species distribution models (SDMs)”

Response: Done

Comment: I suggest restructuring the methodology section into the following subsection:

1- Occurrence points

2- Environmental variables

3- Model building

4- Model evaluation (Area Under the Receiver Operating Curve (AUC)).

Response: Done

Comment: In this section, it's important to clarify what threshold was used to delineate the suitability and unsuitability areas.

Response: Done, as the reviewer probably accidentally overlooked this was already stated

“Suitability maps were overlaid for the present day (1970-2000), 2050 and 2070, with a suitability cutoff score of 0.2. Acceptable suitability is defined as 0.2 for cultivated regions (30) and 0.4 for natural areas (31).”

Comment: Implications for Future Cultivation : A small section highlighting the benefits of the applied modeling techniques in establishing priority zones for management actions is necessary. In addition, a couple of sentences on the limitations of the modeling technique is required. For this, I suggest:

https://doi.org/10.1016/j.ecoinf.2022.101930

https://doi.org/10.1007/s10661-024-12438-z

https://www.mdpi.com/2071-1050/14/21/14621

Response: We have added the sentences:

“Using bioclimatic and biophysical variables has shown to be effective in conservation efforts to help identify areas that should have priority for conservation (Hama and Khwarahm, 2023; Mirhashemi et al., 2024). The principal has the same potential benefit for identifying priority areas for cultivation under climate change.”

Hama, A. A., & Khwarahm, N. R. (2023). Predictive mapping of two endemic oak tree species under climate change scenarios in a semiarid region: range overlap and implications for conservation. Ecological Informatics, 73, 101930.

Mirhashemi, H., Ahmadi, K., Heydari, M., Karami, O., Valkó, O., & Khwarahm, N. R. (2024). Climatic variables are more effective on the spatial distribution of oak forests than land use change across their historical range. Environmental Monitoring and Assessment, 196(3), 289.

Reviewer 3

Comment: This manuscript presents a novel exploration of the historical, current, and projected future distributions of Cannabis sativa. By employing species distribution modeling (SDM) based on bioclimatic and soil variables, it offers insights into how changing climate conditions could impact the range and suitability of Cannabis habitats. The modeling under paleoclimatic scenarios, coupled with present and future climate projections, adds significant value to understanding both the ecological and practical dimensions of Cannabis cultivation and conservation.

Response: Thank you for the summary

Comment: The revised manuscript addressed many initial comments, particularly in methodology justification, result interpretation, and improvements to data selection rationale. The addition of comments on data limitations, particularly regarding soil information and gaps in training data, enhances the transparency of the methodology.

Response: Thank you

Comment: I recommend that this revised manuscript be accepted for publication in PLOS ONE after addressing some remaining amendments for clarity and scientific rigor. The authors have responded well to previous comments, but additional refinements would further strengthen the manuscript.

Response: Thank you

Comment: The manuscript currently includes both numerical citations and author-based in-text citations (e.g., “Zhang et al., 2018”), which creates inconsistency. Please unify the reference style, choosing either numerical or author-date citation for all references in accordance with PLOS ONE formatting guidelines.

Response: Done

Comment: The manuscript would benefit from a clearer distinction between Cannabis species growing in the wild and those cultivated under controlled conditions. This differentiation is crucial, as cultivation practices allow for precise management of variables, whereas wild populations are subject to natural selection pressures and environmental variability. Expanding this discussion could clarify the ecological impacts and distinctions in Cannabis adaptation strategies.

Response: This is a contested area, what is truly wild, what is feral, and what is wild crafted. This is why we reduced the number of occurrence points to choose those that showed no indication of human intervention.

Comment: The authors used 137 occurrence points to model habitat suitability globally, which may be a limited representation, especially given the scale of the analysis. A discussion addressing whether this sample size sufficiently captures global suitability would be valuable, perhaps mentioning potential limitations and the implications of using Eurasian data exclusively for a global SDM.

Response: We have added the statement

“Further, the limited sample size of the wild occurrences may lead to increased variability.”

As for the limitation of using Eurasian samples, this was done as this is the native range for Cannabis. There are strong reasons for only using these points to explore future niches. By capturing the climate variability that the species experiences in its native range here, we can then predict suitability in other non-observed areas (such as the American continent) where climatic properties match. This therefore does not limit the potential or translation to a global SDM model when only using Eurasian data points.

Comment: Although the paper explores past and present distribution shifts, it lacks a focused discussion on historical changes in the distribution of Cannabis. Integrating an analysis or discussion of historical fluctuations in its range, especially in response to past climatic shifts, could provide a more comprehensive context.

Response: This would be quite interesting but is not the major goal of this paper. The major goal here is to explore present day potential species ranges and how potential future changes may impact cultivation areas. It is difficult to translate the range changes in the past to the effects these may have had on the evolutionary trajectory of the plant without matched DNA or pollen samples for those specific timepoints. Work by Prof John McPartland has covered an exploration of past niches with matching pollen samples and was referenced here in this publication and in discussion.

Comment: While the authors effectively discuss how climate variables have influenced Cannabis distribution, soil properties such as organic carbon content and pH also vary over millennia. A brief discussion on how soil characteristics have changed historically could help contextualize the model’s results, as climate and soil variability are interrelated in shaping Cannabis habitats.

Response: We feel that this is outside the scope of the current paper to discuss historical biogeochemical cycling in soils, as the main goal was to explore the present day and potential future ranges for the species with respect to climate. We only included the soil data for the present day data analysis as we agree, soil properties are subject to change and much more difficult to rely upon through large timeframes at such broad resolutions.

Reviewer 4

Comment:The manuscript, titled "Species Distribution of Cannabis sativa: Past, Present and Future," offers valuable insights into how this species’ distribution responds to climate change. By modeling the historical, current, and projected future ranges of Cannabis sativa, the study highlights key environmental factors that shape its distribution under different climate scenarios. This research adds meaningfully to our understanding of the expansion-contraction model, especially for species affected by environmental shifts.

Response: thank you for the summary.

Comment:In terms of modeling, the use of AUC as a performance measure is helpful; however, including AICc and ROC values would make the model evaluation even more robust. AICc would allow for a clearer comparison of model fit across alternatives, while the ROC curve would provide a more complete view of predictive performance. These additions could be especially useful for readers with an interest in model selection and accuracy.

Response: This is a good point. We apologize but we have not included the AIC as we no longer have access to the model output and because we did not use them to make our model decisions. The ROCs for each analysis are available in the supplementary files.

Comment: Additionally, the discussion on expansion and contraction might benefit from referencing the study by Ülker, Tavşanoğlu, and Perktaş (2018) on Quercus robur ("Ecological niche modeling of Pedunculate Oak supports the ‘Expansion-Contraction’ model of Pleistocene biogeography"). Including this work may offer a useful comparison and add depth to the discussion of species distribution changes over time.

Response: we have added the sentence:

“Similar to previous work we see an expansion and contraction at known times of historical climate fluctuations (Ulker et al., 2018).”

Ülker, E. D., Tavşanoğlu, Ç., & Perktaş, U. (2018). Ecological niche modelling of pedunculate oak (Quercus robur) supports the ‘expansion–contraction’model of Pleistocene biogeography. Biological Journal of the Linnean Society, 123(2), 338-347.

Comment: Finally, it’s clear that the authors have made considerable improvements based on previous feedback, especially in addressing the limitations of soil data, explaining data selection, and refining methodological details. These thoughtful updates have successfully resolved earlier concerns and have strengthened the manuscript overall.

Response: Thank you

Reviewer 5

Comment: All comments have been addressed

Response: Thank you for looking at the specific comments we were asked to address instead of bringing up different new tangential concerns

Comment: In this study, authors have used various multi-source spatial and non-spatial data to investigate species Distribution of Cannabis sativa for Past, Present and future. It is an impressive compilation of information related with the documentation of Cannabis sativa.

Response: Thanks you for the summary

Comment: Authors have used CMIP5 GCM dataset for future period. Cannabis are sensitive to temperature and humidity. Previous CMIP5 models have higher uncertainty in temperature and rainfall than CMIP6 GCMs. I suggest applying a dataset of CMIP6 GCMs, otherwise provide uncertainty in estimated suitability areas due to the use of CMIP5 GCMs.

Response: We apologize if this was unclear, but this is incorrect, we stated in the methods we used CMIP6

Attachment

Submitted filename: Cannabis SDM reponse to reviewer comments.docx

pone.0306007.s005.docx^{(19.2KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0306007.r006

Decision Letter 2

Andrea Mastinu

28 Nov 2024

Species Distribution of Cannabis sativa : Past, Present and future

PONE-D-24-23287R2

Dear Dr. Kantar,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Kind regards,

Andrea Mastinu

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

Reviewer #2: (No Response)

Reviewer #4: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #2: Yes

Reviewer #4: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #2: Yes

Reviewer #4: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #2: Yes

Reviewer #4: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #2: Yes

Reviewer #4: Yes

**********

6. Review Comments to the Author

Reviewer #2: The authors have sufficiently addressed my concerns. The manuscript now has improved in comparison to the previous version

Best wishes,

Reviewer #4: Thank you for addressing my comments effectively. I extend my congratulations on the nice execution of this work.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean? ). If published, this will include your full peer review and any attached files.

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PLoS One. doi: 10.1371/journal.pone.0306007.r007

Acceptance letter

Andrea Mastinu

PONE-D-24-23287R2

PLOS ONE

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

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Data Availability Statement

All code and data is available at https://github.com/ahmccormick and high resolution figures are available at https://figshare.com/authors/Anna_H_McCormick/17741367.

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PERMALINK

Species distribution of Cannabis sativa: Past, present and future

Anna Halpin-McCormick

Tai McClellan Maaz

Michael B Kantar

Kasey E Barton

Rishi R Masalia

Nick Batora

Kerin Law

Eleanor J Kuntz

Roles

Abstract

Introduction

Materials and methods

Occurrence points

Environmental variables

Model building

Model evaluation

Results and discussion

Historic changes in the distribution of Cannabis

Fig 1. Maxent generated mean of WorldClim Bioclimatic (temperature and precipitation) variables and ISRIC soil variables for the present day together.

Current Habitat Suitability of Cannabis.

Fig 2. Maxent generated mean of WorldClim Bioclimatic (temperature and precipitation) variables for future climate projections for 2050 and 2070 with model SSP245 (efforts made to curb climate change).

Fig 4. Suitability overlay with the present day (green) and future projections for Cannabis sativa species distribution for 2050 (blue) and 2070 (red) for SSP585 or the business-as-usual climate model.

Suitability and Soil.

Suitability of Cannabis under Future Climate Scenarios.

Fig 3. Maxent generated mean of WorldClim Bioclimatic (temperature and precipitation) variables for 2050 and 2070 with model SSP845 (business-as-usual climate model).

Implications for Future Cultivation.

Conclusion

Supplemental information

Acknowledgments

Data Availability

Funding Statement

References

Author response to Decision Letter 0

Decision Letter 0

Andrea Mastinu

Roles

Author response to Decision Letter 0

Decision Letter 1

Andrea Mastinu

Roles

Author response to Decision Letter 1

Decision Letter 2

Andrea Mastinu

Roles

Acceptance letter

Andrea Mastinu

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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