Projected suitable habitat models were constructed in randomForest (R package, version 4.6-10) using a set of presence points for the species derived from element occurrence and herbarium records, together with temperature, precipitation, and soil variables. The current distribution used modeled historic period (1970-2000) climate variables from the appropriate matching GCM model run. These model parameters were then used with projected climate data to get future (2020-2050) modeled suitable habitat for each scenario. Modeled past suitable habitat and modeled future suitable habitat are combined to show areas of change, using various thresholds to distinguish change categories, as well as current mapped pinyon occupied habitats from LANDFIRE existing vegetation (version 1.3.0). Current occupied habitat is represented as areas with probability greater than the all-scenario average model-reported threshold (sensitivity = specificity) AND currently mapped as PIED. These probability threshold levels were also applied to projected future habitat (since we have no “future” mapping), with the final model was classified as: Value Habt Class Current 2035 1 Lost >= 0.83 < 0.52 2 Threatened >= 0.83 >= 0.52 and < 0.83 3 Persistent >= 0.83 >= 0.83 4 Emergent < 0.83 >= 0.83 0 none of the above where: 0.83 is the average probability of occurrence value from the 3 scenarios, current timeframe, where PIED is known to occur (using LANDFIRE vegetation). 0.52 is the average probability of occurrence value from the 3 scenarios, current timeframe, where the model specificity = the model sensitivity.
Projected suitable habitat models were constructed in randomForest (R package, version 4.6-10) using a set of presence points for the species derived from element occurrence and herbarium records, together with temperature, precipitation, and soil variables. The current distribution used modeled historic period (1970-2000) climate variables from the appropriate matching GCM model run. These model parameters were then used with projected climate data to get future (2020-2050) modeled suitable habitat for each scenario. Modeled past suitable habitat and modeled future suitable habitat are combined to show areas of change, using various thresholds to distinguish change categories, as well as current mapped pinyon occupied habitats from LANDFIRE existing vegetation (version 1.3.0). Current occupied habitat is represented as areas with probability greater than the all-scenario average model-reported threshold (sensitivity = specificity) AND currently mapped as PIED. These probability threshold levels were also applied to projected future habitat (since we have no “future” mapping), with the final model was classified as: Value Habt Class Current 2035 1 Lost >= 0.83 < 0.52 2 Threatened >= 0.83 >= 0.52 and < 0.83 3 Persistent >= 0.83 >= 0.83 4 Emergent < 0.83 >= 0.83 0 none of the above where: 0.83 is the average probability of occurrence value from the 3 scenarios, current timeframe, where PIED is known to occur (using LANDFIRE vegetation). 0.52 is the average probability of occurrence value from the 3 scenarios, current timeframe, where the model specificity = the model sensitivity.
These datasets contain time series of anomalies, relative to 1971-2000 period, in the mean, daily minimum and maximum temperatures (F), precipitation (%), growing season lenght (GSL in days), and warm season duration index (WSDI in days) for the Southwest Colorado region for the three future climate scenarios considered in the Social Ecological and Climate Resiliency (SECR) project.
These datasets contain time series of anomalies, relative to 1950-1999 period, in the annual and seasonal soil moisture (%) and runoff (%) in the Pinyon-Juniper ecosystem of Southwest Colorado for the three future climate scenarios considered in the Social Ecological and Climate Resiliency (SECR) project.
These datasets contain time series of anomalies, relative to 1950-1999 period, in the annual and seasonal soil moisture (%), runoff (%), precipitation (%) and evapotranspiration (%) in the Upper Gunnison Basin in Southwest Colorado for the three future climate scenarios considered in the Social Ecological and Climate Resiliency (SECR) project.
Projected suitable habitat models were constructed using a set of presence points for the species derived from element occurrence and herbarium records, together with temperature, precipitation, and soil variables. The current distribution used modeled historic period (1970-2000) climate variables from the appropriate matching GCM model run. These model parameters were then used with projected climate data to get future (2020-2050) modeled suitable habitat for each scenario. Modeled past suitable habitat and modeled future suitable habitat are combined to show areas of change, using various thresholds to distinguish change categories, as well as comparison to current mapped habitats from SWReGAP landcover (USGS 2004) or LANDFIRE existing vegetation (version 1.3.0). The change categories are (raster values in parentheses): (1) Lost = will not remain in place (2) Threatened = unlikely to remain in place, especially after a disturbance (3) Persistent = conditions remain within historical range (4) Emergent = new areas where climate will become suitable
Projected suitable habitat models were constructed using a set of presence points for the species derived from element occurrence and herbarium records, together with temperature, precipitation, and soil variables. The current distribution used modeled historic period (1970-2000) climate variables from the appropriate matching GCM model run. These model parameters were then used with projected climate data to get future (2020-2050) modeled suitable habitat for each scenario. Modeled past suitable habitat and modeled future suitable habitat are combined to show areas of change, using various thresholds to distinguish change categories, as well as comparison to current mapped habitats from SWReGAP landcover (USGS 2004) or LANDFIRE existing vegetation (version 1.3.0). The change categories are (raster values in parentheses): (1) Lost = will not remain in place (2) Threatened = unlikely to remain in place, especially after a disturbance (3) Persistent = conditions remain within historical range (4) Emergent = new areas where climate will become suitable
Projected suitable habitat models were constructed using a set of presence points for the species derived from element occurrence and herbarium records, together with temperature, precipitation, and soil variables. The current distribution used modeled historic period (1970-2000) climate variables from the appropriate matching GCM model run. These model parameters were then used with projected climate data to get future (2020-2050) modeled suitable habitat for each scenario. Modeled past suitable habitat and modeled future suitable habitat are combined to show areas of change, using various thresholds to distinguish change categories, as well as comparison to current mapped habitats from SWReGAP landcover (USGS 2004) or LANDFIRE existing vegetation (version 1.3.0). The change categories are (raster values in parentheses): (1) Lost = will not remain in place (2) Threatened = unlikely to remain in place, especially after a disturbance (3) Persistent = conditions remain within historical range (4) Emergent = new areas where climate will become suitable
America’s remaining grassland in the Prairie Pothole Region (PPR) is at risk of being lost to crop production. When crop prices are high, like the historically high corn prices that the U.S. experienced between 2008 and 2014, the risk of grassland conversion is even higher. Changing climate will add uncertainties to any efforts toward conservation of grassland in the PPR. Grassland conversion to cropland in the region would imperil nesting waterfowl among other species and further impair water quality in the Mississippi watershed. In this project, we sought to contribute to the understanding of land conversion in the PPR with the aim to better target the use of public and private funds allocated toward incentivizing grassland preservation on private lands in the Dakotas. We assembled data on historical land switching in the area and on land conversion costs. We analyzed crop vulnerabilities to weather and climate change. We examined practical analytical tools to assess the likelihood of grassland conversion to cropping. With our weather-yield-land use modeling framework we evaluated the likely outcomes of land use changes in the region. Among other land use patterns, our research indicated a possible increase of grassland acres as grasses could be less adversely impacted by changing climate. Working with farmers and conservation partners, our project assessed drivers of land use changes. In particular, while economics and climate factors were admittedly obvious important motivations for land use changes, our findings suggested that landowners’ decisions were significantly affected by non-pecuniary factors, like lifestyle choices, or some behavioral biases (e.g., recency bias, and anticipated regret factor) whose roles in economic decision making have been increasingly recognized. As landowners and conservation managers gradually adapt to changing climates, it is not only important that we understand the impacts of the changing climates, but also imperative that we are aware how landowners perceive how climate is changing and how well they are willing to embrace sound adaptation strategies.