Drought-induced tree mortality is predicted to increase in dry forests across the globe as future projections show hotter, drier climates. This could potentially result in large-scale tree die-offs, changes in species composition, and loss of forest ecosystem services, including carbon storage. While some studies have found that forest stands with greater basal areas (BA) have higher drought mortality, many have not evaluated the extent to which forests restored to lower densities via restoration activities affect drought mortality. The southwestern USA is particularly susceptible to tree mortality due to the predicted increases in temperature, drier soils, and forests with high density. Our objective was to evaluate how ponderosa pine mortality is expected to be influenced by the Four Forests Restoration Initiative (4FRI), a large-scale forest restoration effort ongoing in northern Arizona, USA, that will reduce stand BA by approximately 40%. Specifically, we modeled drought mortality in three time periods, one contemporary (1970–2010), and two future (2020–2059 and 2060–2099) under three restoration scenarios: no thinning, 4FRI thinning, and a BA reduction beyond the 4FRI plan (4FRI-intensive). We estimated mortality using 11 climate models under two emissions scenarios. Without thinning, our model predicted that by mid-century (2020–2059), changes in climate could increase annual ponderosa pine mortality rates by 45–57% over contemporary rates. However, with thinning, mid-century mortality was predicted to remain near or below contemporary rates and these rates are 31–35% (4FRI) and 46–51% (4FRI-intensive) less than the mid-century scenarios without thinning. Our study shows that while climate change is likely to increase tree mortality rates, large-scale forest restoration projects, such as 4FRI, have the potential to ameliorate the effects of climate change and keep mortality rates near contemporary levels for decades.

We develop an analytical framework to examine an agency's optimal grassland easement acquisition while accounting for landowners’ optimal decisions under uncertainty in both conversion and conservation returns. We derive the value of “wait and see” (i.e., neither convert nor ease grassland) for landowners and find that grassland-to-cropland conversion probability and easement value vary in opposing directions when “wait and see” is preferred, indicating that a larger conversion probability does not necessarily imply a higher easement value. Our analysis shows that when conservation funds can be flexibly allocated across periods then the agency's optimal acquisition can be readily achieved by sorting land tracts according to their owners’ optimal choices. When funds cannot be flexibly allocated across periods, we examine both a rational agency's and a myopic agency's decision problems. An acquisition index is developed to facilitate optimal easement acquisition.

In the Great Plains, playas are critical wetland habitats for migratory birds and a source of recharge for the agriculturally important High Plains aquifer. The temporary wetlands exhibit complex hydrology, filling rapidly via local rain storms and then drying through evaporation and groundwater infiltration. Using a long short-term memory (LSTM) neural network to account for these complex processes, we modeled the probability of playa inundation for 71,842 playas in the Great Plains from 1984 to 2018. At the level of individual playas, the model achieved an F1-score of 0.522 on a withheld test set, displaying the ability to predict complex inundation patterns. When simulating playa inundation over the entire region, the model is able to very closely track inundation trends, even during periods of drought. Our results demonstrate potential for using LSTMs to model complex hydrological dynamics. Our modeling approach could be used to model playa inundation into the future under different climate scenarios to better understand how wetland habitats and groundwater will be impacted by changing climate.

We used an individual-based plant simulation model that represents intra- and inter-specific competition for water availability, which is represented by a process-based soil water balance model. For dominant plant functional types, we quantified changes in biomass and characterized agreement among 52 future climate scenarios. We then used a multivariate matching algorithm to generate fine-scale interpolated surfaces of functional type biomass for our study area.

Beaver mimicry is a fast-growing conservation technique to restore streams and manage water that is gaining popularity within the natural resource management community because of a wide variety of claimed socio-environmental benefits. Despite a growing number of projects, many questions and concerns about beaver mimicry remain. This study draws on qualitative data from 49 interviews with scientists, practitioners, and landowners, to explore the question of how beaver mimicry projects continue to be promoted and implemented, despite the lack of comprehensive scientific studies and unclear regulatory requirements. Specifically, we investigate how these three groups differentially assess the salience, credibility, and legitimacy of evidence for beaver mimicry and analyze how those assessments affect each group’s conclusions about the feasibility, desirability, and scalability of beaver mimicry. By highlighting the interaction between how someone assesses evidence and how they draw conclusions about an emerging natural resource management approach, we draw attention to the roles of experiential evidence and scientific data in debates over beaver mimicry. Our research emphasizes that understanding how different groups perceive salience, credibility, and legitimacy of scientific information is necessary for understanding how they make assessments about conservation and natural resource management strategies.

Novel approaches for quantifying density and distributions could help biologists adaptively manage wildlife populations, particularly if methods are accurate, consistent, cost-effective, rapid, and sensitive to change. Such approaches may also improve research on interactions between density and processes of interest such as disease transmission across multiple populations. We assess how satellite imagery, unmanned aerial systems (UAS) imagery, and Global Positioning System (GPS) collar data vary in characterizing elk density, distribution and count patterns across times with and without supplemental feeding at the National Elk Refuge (NER), Wyoming, USA. We also present the first comparison of satellite imagery data with traditional counts for ungulates in a temperate system. We further evaluate 7 different aggregation metrics to identify the most consistent and sensitive metrics for comparing density and distribution across time and populations. All three data sources detected higher densities and aggregation locations of elk during supplemental feeding than non-feeding at the NER. Kernel density estimates (KDEs), KDE polygon areas, and the first quantile of inter-elk distances detected differences with the highest sensitivity and were most highly correlated across data sources. Both UAS and satellite imagery provide snapshots of density and distribution patterns of most animals in the area for lower costs than GPS collars. While satellite-based counts were lower than traditional counts, aggregation metrics matched those from UAS and GPS data sources when animals appeared in high contrast to the landscape, including brown elk against new snow in open areas. UAS counts of elk were similar to traditional ground-based counts on feed grounds and are the best data source for assessing changes in small spatial extents. Satellite, UAS, or GPS data can provide appropriate data for assessing density and changes in density from adaptive management actions. For the NER, where high elk densities are beneath controlled airspace, GPS collar data will be most useful for evaluating how management actions, including changes in the dates of supplemental feeding, influence elk density and aggregation across large spatial extents. Using consistent and sensitive measures of density may improve research on the drivers and effects of density within and across a wide range of species.

These model objects are the outputs of three Boosted Regression Tree models (for three different time periods) to explore the role of climate change and variability in driving ecological change and transformation. Response variables were the proportion of sites in each ecoregion with peak rates of change at 100-year time steps. Predictor variables included temperature anomaly, temperature trend, temperature variability, precipitation anomaly, precipitation trend, precipitation variability and ecoregion, also at 100-yr time steps. Models focused on the most distant time periods (0-21000 BP and 7500 - 21000 BP) show that rapid vegetation change was initiated across these landscapes once a 2 ℃ temperature increase (positive temperature anomaly, relative to 21,000 yr BP) was realized. The model focused on the more recent time periods, 0-7500 BP, shows that rapid vegetation change was initiated across these landscapes again recently with reduced rainfall.

These model objects are the outputs of two Bayesian hierarchical models (one for the Middle Rockies and one for the Southern Rockies) to explore the role of landscape characteristics in climate-driven ecological change and transformation. We used the rate of change for each site at 100-yr time steps as the response variable, and included elevation, CHILI, aspect, slope, and TPI as fixed effects in the models, run separately for each ecoregion. We included a random intercept of site to quantify the magnitude of site-level variation in rate-of-change that may be unaccounted for by our covariates.

This database integrates a list of vegetation transformations that occurred across the Southern and Middle Rockies since 21,000 years ago, the age of occurrence, the type of vegetation switch that occurred, whether the rates of vegetation change peaked at that time, and when applicable, the duration of peak rates of vegetation change.

This project investigated how climate change over the last 21,000 years, which was characterized by significant warming, influenced vegetation in the Southern and Middle Rockies. We found that rapid vegetation change was initiated across these landscapes once a 2 ℃ temperature increase was realized and again recently with reduced rainfall. Southwesterly slopes in the Southern Rockies were prone to rapid change, otherwise landscape features didn’t have a strong effect. We also examined vegetation transformations (e.g., sagebrush steppe switches to a lodgepole pine forest) and identified between one and four vegetation transformations at each site, for a total of 60 transformations, over half of which occurred rapidly. This work provides a novel understanding of vegetation change that integrates climate change and landscape context, and helps to anticipate when (once our climate warms by 2 ℃ (before 2050)) and where (southwesterly slopes in the Southern Rockies) rapid vegetation change and transformation will be likely. The following details describe the scripts for the paleoecological portion of the NC CASC project 'Risk of ecological transformation across the US West and Pinyon woodlands' which are located in GitLab.