Joel Hartter

Characterizing compositional and structural aspects of vegetation is critical to effectively assessing land function. When priorities are placed on ecological integrity, remotely sensed estimates of fractional vegetation components (FVCs) are useful for measuring landscape-level habitat structure and function. In this study, we address whether FVC estimates, stratified by dominant vegetation type, vary with different classification approaches applied to very-high-resolution small unoccupied aerial system (UAS)-derived imagery. Using Parrot Sequoia imagery, flown on a DJI Mavic Pro micro-quadcopter, we compare pixel- and segment-based random forest classifiers alongside a vegetation height-threshold model for characterizing the FVC in a southern African dryland savanna. Results show differences in agreement between each classification method, with the most disagreement in shrub-dominated sites. When compared to vegetation classes chosen by visual identification, the pixel-based random forest classifier had the highest overall agreement and was the only classifier not to differ significantly from the hand-delineated FVC estimation. However, when separating out woody biomass components of tree and shrub, the vegetation height-threshold performed better than both random-forest approaches. These findings underscore the utility and challenges represented by very-high-resolution multispectral UAS-derived data (~10 cm ground resolution) and their uses to estimate FVC. Semi-automated approaches statistically differ from by-hand estimation in most cases; however, we present insights for approaches that are applicable across varying vegetation types and structural conditions. Importantly, characterization of savanna land function cannot rely only on a “greenness” measure but also requires a structural vegetation component. Underscoring these insights is that the spatial heterogeneity of vegetation structure on the landscape broadly informs land management, from land allocation, wildlife habitat use, natural resource collection, and as an indicator of overall ecosystem function.

Parks are essential for protecting biodiversity and finding ways to improve park effectiveness is an important topic. We contributed to this debate by examining spatial and temporal changes in illegal activities in Kibale National Park, Uganda between 2006 and 2016 and used existing data to evaluate how the changes were correlated with the living conditions of people in neighboring communities, as well as patrolling effort. We explore the effectiveness of conservation strategies implemented in Kibale, by quantifying changes in the abundance of nine animal species over two to five decades. While uncertainty in such animal survey data are inherently large and it is hard to generalize across a 795‐km² area that encompasses diverse habitat types, data suggest an increase in animal abundance in the National Park. An increase in patrolling effort by park guards over the decade was correlated with a decline in the number of traps and snares found, which suggests patrolling helped limit resource extraction from the park. The park’s edge was extensively used for illegal forest product extraction, while the setting of snares occurred more often deeper in the forest. Perhaps counter‐intuitively, increased community wealth or park‐related employment in a village next to the park were positively correlated with increased illegal forest product extraction. Overall, our results suggest that the portfolio of conservation strategies used over the last two to five decades were effective for protecting the park and its animals, although understanding the impact of these efforts on local human populations and how to mitigate any losses and suffering they sustain remains an important area of research and action. It is evident that complex social, political and economic drivers impact conservation success and more interdisciplinary studies are required to quantify and qualify these dimensions.

Remote sensing analyses focused on non-timber forest product (NTFP) collection and grazing are current research priorities of land systems science. However, mapping these particular land use patterns in rural heterogeneous landscapes is challenging because their potential signatures on the landscape cannot be positively identified without fine-scale land use data for validation. Using field-mapped resource areas and household survey data from participatory mapping research, we combined various Landsat-derived indices with ancillary data associated with human habitation to model the intensity of grazing and NTFP collection activities at 100-m spatial resolution. The study area is situated centrally within a transboundary southern African landscape that encompasses community-based organization (CBO) areas across three countries. We conducted four iterations of pixel-based random forest models, modifying the variable set to determine which of the covariates are most informative, using the best fit predictions to summarize and compare resource use intensity by resource type and across communities. Pixels within georeferenced, field-mapped resource areas were used as training data. All models had overall accuracies above 60% but those using proxies for human habitation were more robust, with overall accuracies above 90%. The contribution of Landsat data as utilized in our modeling framework was negligible, and further research must be conducted to extract greater value from Landsat or other optical remote sensing platforms to map these land use patterns at moderate resolution. We conclude that similar population proxy covariates should be included in future studies attempting to characterize communal resource use when traditional spectral signatures do not adequately capture resource use intensity alone. This study provides insights into modeling resource use activity when leveraging both remotely sensed data and proxies for human habitation in heterogeneous, spectrally mixed rural land areas.

In biodiversity hotspots, there is often tension between human needs and conservation, exacerbated when protected areas prevent access to natural resources. Forest-dependent people may compensate for exclusion by managing unprotected forests or cultivating planted woodlots. Outside Bwindi Impenetrable National Park in Uganda, household wood product needs are high and population growth puts pressure on the environment. We investigated the role of privately and collectively managed woodlots in provisioning wood products and supporting local livelihoods. We found that households relied heavily on woodlots for daily needs and as resources during time of need. We also found that locally relevant social institutions, called stretcher groups, played a role in the management of woodlots, providing shared community resources. Privately and collectively owned woodlots support local livelihoods and wood product needs in the region. Long-term management of forests in Uganda should consider the value of woodlots and the mechanisms required to support them.

Gray wolves ( Canis lupus ) were eliminated from the state of Oregon in the middle of the 20th century. By the early 21st century, wolves had returned to the northeast corner of the state, dispersing from populations reintroduced in Idaho and Wyoming. On a series of random‐sample telephone surveys (2011–2018), we asked more than 3,000 northeast Oregon residents about their preferences concerning wolf management strategies. One‐third of the respondents said that wolves should be eliminated from this region. Sociopolitical identity dominated other individual characteristics including age, education, years resident, and forestland ownership in predicting wolf‐management views. Political effects appear even stronger when our indicator distinguishes the most conservative, and further intensify when most of the respondent's friends belong to the same party. This strong influence of sociopolitical identity echoes findings from the broader literature on environmental concern, but adds a new and policy‐relevant element to wolf‐attitude research. As wolves expand throughout the west, and new states consider reintroduction, state and federal wildlife managers face deeply rooted opposition. Managers must consider a range of strategies to manage wolves while working with community leaders in wolf‐occupied areas to determine management options.

Wildfire is a growing threat in the western US, driven by high fuel loads, a warming climate, and rising human activity in the wildland urban interface. Diverse stakeholders must collaborate to mitigate risk and adapt to changing conditions. Communication strategies in collaborative efforts may be most effective if they align with local perspectives on wildfire and climate change. We investigate drivers of residents’ subjective perceptions regarding both issues in eastern Oregon using 2018 survey data, and examine objective evidence regarding local fuel loads, climate, and wildfire to identify trends and contextualize residents’ perceptions. We find that sociopolitical identity strongly predicts climate change beliefs, and that identity and climate beliefs predict both perceptions of recent past climate and likely future trends. Political influences on climate perceptions are strongest among people whose friends mostly belong to the same party. In contrast, perceptions about future wildfire risks are largely independent of climate-change beliefs, and of individual or peer-group politics. Most people accurately perceive the rising frequency of large wildfires, and expect this trend to continue. Decision makers have an opportunity to engage diverse stakeholders in developing policies to mitigate increasing wildfire risk without invoking climate change, which remains politically polarizing in some communities.

Conducting research on coupled social-ecological systems (SESs) presents inherent challenges, such as coordination across disparate disciplines or integrating across multiple scales and levels of governance. To overcome these common challenges, we propose that structuring the research design itself according to SES principles provides for integrative execution of SES science. First, starting with pilot work, human and natural science researchers should work as a team to identify and access multi-level entry points (i.e. points of direct engagement) within the system, relative to the spatiotemporal scales under investigation. Second, teams should implement an adaptive process that begins with the proposed research design and uses shared experiences from pilot work to refine protocols prior to subsequent data collection. We provide examples of multi-level and multi-scale entry points, and show that adaptive management of research design through coordinated iteration allows for better research integration and applicable outcomes.

Understanding how individuals, communities, and populations vary in their vulnerability requires defining and identifying vulnerability with respect to a condition, and then developing robust methods to reliably measure vulnerability. In this study, we illustrate how a conceptual model translated via simulation can guide the real-world implementation of data collection and measurement of a model system. We present a generalizable statistical framework that specifies linkages among interacting social and biophysical components in complex landscapes to examine vulnerability. We use the simulated data to present a case study in which households are vulnerable to conditions of land function, which we define as the provision of goods and services from the surrounding environment. We use an example of a transboundary region of Southern Africa and apply a set of hypothesized, simulated data to illustrate how one might use the framework to assess vulnerability based on empirical data. We define vulnerability as the predisposition of being adversely affected by environmental variation and its impacts on land uses and their outcomes as exposure (E), mediated by sensitivity (S), and mitigated by adaptive capacity (AC). We argue that these are latent, or hidden, characteristics that can be measured through a set of observable indicators. Those indicators and the linkages between latent variables require model specification prior to data collection, critical for applying the type of modeling framework presented. We discuss the strength and directional pathways between land function and vulnerability components, and assess their implications for identifying potential leverage points within the system for the benefit of future policy and management decisions.