Bayesian Networks and Adaptive Management of Wildlife Habitat

Abstract: Adaptive management is an iterative process of gathering new knowledge regarding a system's behavior and monitoring the ecological consequences of management actions to improve management decisions. Although the concept originated in the 1970s, it is rarely actively incorporated into ecological restoration. Bayesian networks (BNs) are emerging as efficient ecological decision‐support tools well suited to adaptive management, but examples of their application in this capacity are few. We developed a BN within an adaptive‐management framework that focuses on managing the effects of feral grazing and prescribed burning regimes on avian diversity within woodlands of subtropical eastern Australia. We constructed the BN with baseline data to predict bird abundance as a function of habitat structure, grazing pressure, and prescribed burning. Results of sensitivity analyses suggested that grazing pressure increased the abundance of aggressive honeyeaters, which in turn had a strong negative effect on small passerines. Management interventions to reduce pressure of feral grazing and prescribed burning were then conducted, after which we collected a second set of field data to test the response of small passerines to these measures. We used these data, which incorporated ecological changes that may have resulted from the management interventions, to validate and update the BN. The network predictions of small passerine abundance under the new habitat and management conditions were very accurate. The updated BN concluded the first iteration of adaptive management and will be used in planning the next round of management interventions. The unique belief‐updating feature of BNs provides land managers with the flexibility to predict outcomes and evaluate the effectiveness of management interventions.     

Digital data sources and methods for conservation culturomics

Ongoing loss of biological diversity is primarily the result of unsustainable human behavior. Thus, the long-term success of biodiversity conservation depends on a thorough understanding of human–nature interactions. Such interactions are ubiquitous but vary greatly in time and space and are difficult to monitor efficiently at large spatial scales. However, the Information Age also provides new opportunities to better understand human–nature interactions because many aspects of daily life are recorded in a variety of digital formats. The emerging field of conservation culturomics aims to take advantage of digital data sources and methods to study human–nature interactions and thus to provide new tools for studying conservation at relevant temporal and spatial scales. Nevertheless, technical challenges associated with the identification, access, and analysis of relevant data hamper the wider adoption of culturomics methods. To help overcome these barriers, we propose a conservation culturomics research framework that addresses data acquisition, analysis, and inherent biases. The main sources of culturomic data include web pages, social media, and other digital platforms from which metrics of content and engagement can be obtained. Obtaining raw data from these platforms is usually desirable but requires careful consideration of how to access, store, and prepare the data for analysis. Methods for data analysis include network approaches to explore connections between topics, time-series analysis for temporal data, and spatial modeling to highlight spatial patterns. Outstanding challenges associated with culturomics research include issues of interdisciplinarity, ethics, data biases, and validation. The practical guidance we offer will help conservation researchers and practitioners identify and obtain the necessary data and carry out appropriate analyses for their specific questions, thus facilitating the wider adoption of culturomics approaches for conservation applications.     

Use of Multicriteria Decision Analysis to Address Conservation Conflicts

Conservation conflicts are increasing on a global scale and instruments for reconciling competing interests are urgently needed. Multicriteria decision analysis (MCDA) is a structured, decision‐support process that can facilitate dialogue between groups with differing interests and incorporate human and environmental dimensions of conflict. MCDA is a structured and transparent method of breaking down complex problems and incorporating multiple objectives. The value of this process for addressing major challenges in conservation conflict management is that MCDA helps in setting realistic goals; entails a transparent decision‐making process; and addresses mistrust, differing world views, cross‐scale issues, patchy or contested information, and inflexible legislative tools. Overall we believe MCDA provides a valuable decision‐support tool, particularly for increasing awareness of the effects of particular values and choices for working toward negotiated compromise, although an awareness of the effect of methodological choices and the limitations of the method is vital before applying it in conflict situations. Uso de Análisis de Decisiones Multicriterio para Abordar Conflictos de Conservación     

Human–wildlife coexistence in a changing world

Human–wildlife conflict (HWC) is a key topic in conservation and agricultural research. Decision makers need evidence-based information to design sustainable management plans and policy instruments. However, providing objective decision support can be challenging because realities and perceptions of human–wildlife interactions vary widely between and within rural, urban, and peri-urban areas. Land users who incur costs through wildlife argue that wildlife-related losses should be compensated and that prevention should be subsidized. Supporters of human–wildlife coexistence policies, such as urban-dwelling people, may not face threats to their livelihoods from wildlife. Such spatial heterogeneity in the cost and benefits of living with wildlife is germane in most contemporary societies. This Special Section features contributions on wildlife-induced damages that range from human perspectives (land use, psychology, governance, local attitudes and perceptions, costs and benefits, and HWC and coexistence theory) to ecological perspectives (animal behavior). Building on current literature and articles in this section, we developed a conceptual model to help frame HWC and coexistence dimensions. The framework can be used to determine damage prevention implementation levels and approaches to HWC resolution. Our synthesis revealed that inter- and transdisciplinary approaches and multilevel governance approaches can help stakeholders and institutions implement sustainable management strategies that promote human–wildlife coexistence.     

Application of multiple-population viability analysis to evaluate species recovery alternatives

Population viability analysis (PVA) is a powerful conservation tool, but it remains impractical for many species, particularly species with multiple, broadly distributed populations for which collecting suitable data can be challenging. A recently developed method of multiple-population viability analysis (MPVA), however, addresses many limitations of traditional PVA. We built on previous development of MPVA for Lahontan cutthroat trout (LCT) (Oncorhynchus clarkii henshawi), a species listed under the U.S. Endangered Species Act, that is distributed broadly across habitat fragments in the Great Basin (U.S.A.). We simulated potential management scenarios and assessed their effects on population sizes and extinction risks in 211 streams, where LCT exist or may be reintroduced. Conservation populations (those managed for recovery) tended to have lower extinction risks than nonconservation populations (mean = 19.8% vs. 52.7%), but not always. Active management or reprioritization may be warranted in some cases. Eliminating non-native trout had a strong positive effect on overall carrying capacities for LCT populations but often did not translate into lower extinction risks unless simulations also reduced associated stochasticity (to the mean for populations without non-native trout). Sixty fish or 5–10 fish/km was the minimum reintroduction number and density, respectively, that provided near-maximum reintroduction success. This modeling framework provided crucial insights and empirical justification for conservation planning and specific adaptive management actions for this threatened species. More broadly, MPVA is applicable to a wide range of species exhibiting geographic rarity and limited availability of abundance data and greatly extends the potential use of empirical PVA for conservation assessment and planning.     

Evaluating pollution-related damage and restoration success in urban forests with participatory monitoring and digital tools

Peri-urban forest monitoring requires indicators of vegetation damage. An example is the sacred fir (Abies religiosa) forests surrounding Mexico City, which have been heavily exposed to tropospheric ozone, a harmful pollutant, for over 4 decades. We developed a participatory monitoring system with which local community members and scientists generated data on ozone tree damage. Santa Rosa Xochiac rangers (13) used the digital tool KoboToolBox to record ozone damage to trees, tree height, tree ages, tree condition, tree position, and whether the tree had been planted. Thirty-five percent of the trees (n = 1765) had ozone damage. Younger trees had a lower percentage of foliage damaged by ozone than older trees (p < 0.0001), and asymptomatic trees tended to be younger (p < 0.0001). Symptomatic trees were taller than asymptomatic trees of the same age (R²_c = 0.43, R²_m = 0.27). Involving local communities facilitated forest monitoring and using digital technology improved data quality. This participatory system can be used to monitor forest condition change over time and thus aids restoration efforts driven by government or local communities’ interests, facilitating local decision-making.