A protocol for eliciting nonmaterial values through a cultural ecosystem services frame

Stakeholders’ nonmaterial desires, needs, and values often critically influence the success of conservation projects. These considerations are challenging to articulate and characterize, resulting in their limited uptake in management and policy. We devised an interview protocol designed to enhance understanding of cultural ecosystem services (CES). The protocol begins with discussion of ecosystem‐related activities (e.g., recreation, hunting) and management and then addresses CES, prompting for values encompassing concepts identified in the Millennium Ecosystem Assessment (2005) and explored in other CES research. We piloted the protocol in Hawaii and British Columbia. In each location, we interviewed 30 individuals from diverse backgrounds. We analyzed results from the 2 locations to determine the effectiveness of the interview protocol in elucidating nonmaterial values. The qualitative and spatial components of the protocol helped characterize cultural, social, and ethical values associated with ecosystems in multiple ways. Maps and situational, or vignette‐like, questions helped respondents articulate difficult‐to‐discuss values. Open‐ended prompts allowed respondents to express a diversity of ecosystem‐related values and proved sufficiently flexible for interviewees to communicate values for which the protocol did not explicitly probe. Finally, the results suggest that certain values, those mentioned frequently throughout the interview, are particularly salient for particular populations. The protocol can provide efficient, contextual, and place‐based data on the importance of particular ecosystem attributes for human well‐being. Qualitative data are complementary to quantitative and spatial assessments in the comprehensive representation of people's values pertaining to ecosystems, and this protocol may assist in incorporating values frequently overlooked in decision making processes.     

Mechanistic understanding of human–wildlife conflict through a novel application of dynamic occupancy models

Crop and livestock depredation by wildlife is a primary driver of human–wildlife conflict, a problem that threatens the coexistence of people and wildlife globally. Understanding mechanisms that underlie depredation patterns holds the key to mitigating conflicts across time and space. However, most studies do not consider imperfect detection and reporting of conflicts, which may lead to incorrect inference regarding its spatiotemporal drivers. We applied dynamic occupancy models to elephant crop depredation data from India between 2005 and 2011 to estimate crop depredation occurrence and model its underlying dynamics as a function of spatiotemporal covariates while accounting for imperfect detection of conflicts. The probability of detecting conflicts was consistently <1.0 and was negatively influenced by distance to roads and elevation gradient, averaging 0.08–0.56 across primary periods (distinct agricultural seasons within each year). The probability of crop depredation occurrence ranged from 0.29 (SE 0.09) to 0.96 (SE 0.04). The probability that sites raided by elephants in primary period t would not be raided in primary period t + 1 varied with elevation gradient in different seasons and was influenced negatively by mean rainfall and village density and positively by distance to forests. Negative effects of rainfall variation and distance to forests best explained variation in the probability that sites not raided by elephants in primary period t would be raided in primary period t + 1. With our novel application of occupancy models, we teased apart the spatiotemporal drivers of conflicts from factors that influence how they are observed, thereby allowing more reliable inference on mechanisms underlying observed conflict patterns. We found that factors associated with increased crop accessibility and availability (e.g., distance to forests and rainfall patterns) were key drivers of elephant crop depredation dynamics. Such an understanding is essential for rigorous prediction of future conflicts, a critical requirement for effective conflict management in the context of increasing human–wildlife interactions.     

Improving credibility and transparency of conservation impact evaluations through the partial identification approach

The fundamental challenge of evaluating the impact of conservation interventions is that researchers must estimate the difference between the outcome after an intervention occurred and what the outcome would have been without it (counterfactual). Because the counterfactual is unobservable, researchers must make an untestable assumption that some units (e.g., organisms or sites) that were not exposed to the intervention can be used as a surrogate for the counterfactual (control). The conventional approach is to make a point estimate (i.e., single number along with a confidence interval) of impact, using, for example, regression. Point estimates provide powerful conclusions, but in nonexperimental contexts they depend on strong assumptions about the counterfactual that often lack transparency and credibility. An alternative approach, called partial identification (PI), is to first estimate what the counterfactual bounds would be if the weakest possible assumptions were made. Then, one narrows the bounds by using stronger but credible assumptions based on an understanding of why units were selected for the intervention and how they might respond to it. We applied this approach and compared it with conventional approaches by estimating the impact of a conservation program that removed invasive trees in part of the Cape Floristic Region. Even when we used our largest PI impact estimate, the program's control costs were 1.4 times higher than previously estimated. PI holds promise for applications in conservation science because it encourages researchers to better understand and account for treatment selection biases; can offer insights into the plausibility of conventional point‐estimate approaches; could reduce the problem of advocacy in science; might be easier for stakeholders to agree on a bounded estimate than a point estimate where impacts are contentious; and requires only basic arithmetic skills.     

Using perceptions as evidence to improve conservation and environmental management

The conservation community is increasingly focusing on the monitoring and evaluation of management, governance, ecological, and social considerations as part of a broader move toward adaptive management and evidence‐based conservation. Evidence is any information that can be used to come to a conclusion and support a judgment or, in this case, to make decisions that will improve conservation policies, actions, and outcomes. Perceptions are one type of information that is often dismissed as anecdotal by those arguing for evidence‐based conservation. In this paper, I clarify the contributions of research on perceptions of conservation to improving adaptive and evidence‐based conservation. Studies of the perceptions of local people can provide important insights into observations, understandings and interpretations of the social impacts, and ecological outcomes of conservation; the legitimacy of conservation governance; and the social acceptability of environmental management. Perceptions of these factors contribute to positive or negative local evaluations of conservation initiatives. It is positive perceptions, not just objective scientific evidence of effectiveness, that ultimately ensure the support of local constituents thus enabling the long‐term success of conservation. Research on perceptions can inform courses of action to improve conservation and governance at scales ranging from individual initiatives to national and international policies. Better incorporation of evidence from across the social and natural sciences and integration of a plurality of methods into monitoring and evaluation will provide a more complete picture on which to base conservation decisions and environmental management.     

Trait differences between naturalized and invasive plant species independent of residence time and phylogeny

The ability to predict which alien plants will transition from naturalized to invasive prior to their introduction to novel regions is a key goal for conservation and has the potential to increase the efficacy of weed risk assessment (WRA). However, multiple factors contribute to plant invasion success (e.g., functional traits, range characteristics, residence time, phylogeny), and they all must be taken into account simultaneously in order to identify meaningful correlates of invasion success. We compiled 146 pairs of phylogenetically paired (congeneric) naturalized and invasive plant species in Australia with similar minimum residence times (i.e., time since introduction in years). These pairs were used to test for differences in 5 functional traits (flowering duration, leaf size, maximum height, specific leaf area [SLA], seed mass) and 3 characteristics of species’ native ranges (biome occupancy, mean annual temperature, and rainfall breadth) between naturalized and invasive species. Invasive species, on average, had larger SLA, longer flowering periods, and were taller than their congeneric naturalized relatives. Invaders also exhibited greater tolerance for different environmental conditions in the native range, where they occupied more biomes and a wider breadth of rainfall and temperature conditions than naturalized congeners. However, neither seed mass nor leaf size differed between pairs of naturalized and invasive species. A key finding was the role of SLA in distinguishing between naturalized and invasive pairs. Species with high SLA values were typically associated with faster growth rates, more rapid turnover of leaf material, and shorter lifespans than those species with low SLA. This suite of characteristics may contribute to the ability of a species to transition from naturalized to invasive across a wide range of environmental contexts and disturbance regimes. Our findings will help in the refinement of WRA protocols, and we advocate the inclusion of quantitative traits, in particular SLA, into the WRA schemes.     

Site‐Occupancy Distribution Modeling to Correct Population‐Trend Estimates Derived from Opportunistic Observations

Abstract: Species’ assessments must frequently be derived from opportunistic observations made by volunteers (i.e., citizen scientists). Interpretation of the resulting data to estimate population trends is plagued with problems, including teasing apart genuine population trends from variations in observation effort. We devised a way to correct for annual variation in effort when estimating trends in occupancy (species distribution) from faunal or floral databases of opportunistic observations. First, for all surveyed sites, detection histories (i.e., strings of detection–nondetection records) are generated. Within‐season replicate surveys provide information on the detectability of an occupied site. Detectability directly represents observation effort; hence, estimating detectablity means correcting for observation effort. Second, site‐occupancy models are applied directly to the detection‐history data set (i.e., without aggregation by site and year) to estimate detectability and species distribution (occupancy, i.e., the true proportion of sites where a species occurs). Site‐occupancy models also provide unbiased estimators of components of distributional change (i.e., colonization and extinction rates). We illustrate our method with data from a large citizen‐science project in Switzerland in which field ornithologists record opportunistic observations. We analyzed data collected on four species: the widespread Kingfisher (Alcedo atthis) and Sparrowhawk (Accipiter nisus) and the scarce Rock Thrush (Monticola saxatilis) and Wallcreeper (Tichodroma muraria). Our method requires that all observed species are recorded. Detectability was <1 and varied over the years. Simulations suggested some robustness, but we advocate recording complete species lists (checklists), rather than recording individual records of single species. The representation of observation effort with its effect on detectability provides a solution to the problem of differences in effort encountered when extracting trend information from haphazard observations. We expect our method is widely applicable for global biodiversity monitoring and modeling of species distributions.     

A moral panic over cats

Some conservationists believe that free-ranging cats pose an enormous risk to biodiversity and public health and therefore should be eliminated from the landscape by any means necessary. They further claim that those who question the science or ethics behind their arguments are science deniers (merchants of doubt) seeking to mislead the public. As much as we share a commitment to conservation of biodiversity and wild nature, we believe these ideas are wrong and fuel an unwarranted moral panic over cats. Those who question the ecological or epidemiological status of cats are not science deniers, and it is a false analogy to compare them with corporate and right-wing special interests that perpetrate disinformation campaigns over issues, such as smoking and climate change. There are good conservation and public-health reasons and evidence to be skeptical that free-ranging cats constitute a disaster for biodiversity and human health in all circumstances. Further, there are significant and largely unaddressed ethical and policy issues (e.g., the ethics and efficacy of lethal management) relative to how people ought to value and coexist with cats and native wildlife. Society is better served by a collaborative approach to produce better scientific and ethical knowledge about free-ranging cats.     

The potential for citizen science to produce reliable and useful information in ecology

We examined features of citizen science that influence data quality, inferential power, and usefulness in ecology. As background context for our examination, we considered topics such as ecological sampling (probability based, purposive, opportunistic), linkage between sampling technique and statistical inference (design based, model based), and scientific paradigms (confirmatory, exploratory). We distinguished several types of citizen science investigations, from intensive research with rigorous protocols targeting clearly articulated questions to mass-participation internet-based projects with opportunistic data collection lacking sampling design, and examined overarching objectives, design, analysis, volunteer training, and performance. We identified key features that influence data quality: project objectives, design and analysis, and volunteer training and performance. Projects with good designs, trained volunteers, and professional oversight can meet statistical criteria to produce high-quality data with strong inferential power and therefore are well suited for ecological research objectives. Projects with opportunistic data collection, little or no sampling design, and minimal volunteer training are better suited for general objectives related to public education or data exploration because reliable statistical estimation can be difficult or impossible. In some cases, statistically robust analytical methods, external data, or both may increase the inferential power of certain opportunistically collected data. Ecological management, especially by government agencies, frequently requires data suitable for reliable inference. With standardized protocols, state-of-the-art analytical methods, and well-supervised programs, citizen science can make valuable contributions to conservation by increasing the scope of species monitoring efforts. Data quality can be improved by adhering to basic principles of data collection and analysis, designing studies to provide the data quality required, and including suitable statistical expertise, thereby strengthening the science aspect of citizen science and enhancing acceptance by the scientific community and decision makers.     

On allegations of invasive species denialism

Science denialism retards evidenced-based policy and practice and should be challenged. It has been a particular concern for mitigating global environmental issues, such as anthropogenic climate change. But allegations of science denialism must also be well founded and evidential or they risk eroding public trust in science and scientists. Recently, 77 published works by scholars, scientists, and science writers were identified as containing invasive species denialism (ISD; i.e., rejection of well-supported facts about invasive species, particularly the global scientific consensus about their negative impacts). We reevaluated 75 of these works but could find no examples of refutation of scientific facts and only 5 articles with text perhaps consistent with one of the 5 characteristics of science denialism. We found, therefore, that allegations of ISD were misplaced. These accusations of science denialism may have arisen because invasion biology defines its subjects—invasive species—based on multiple subjective and normative judgments. Thus, more than other applied sciences its consensus is one of shared values as much as agreed knowledge. Criticisms of invasion biology have largely targeted those subjective and normative judgments and their global imposition, not the knowledge on which the discipline is based. Regrettably, a few invasion biologists have misinterpreted the critique of their values-based consensus as a denial of their science when it is not. To make invasion biology a more robust and widely accepted science and to avoid unnecessary misunderstandings and conflicts, invasion biologists could be more accepting of perspectives originating from other disciplines and more open to values-based critique from scholars and scientists outside their field. This recommendation applies to all conservation sciences, especially those addressing global challenges, because these sciences must serve and be relevant to communities with an extraordinary diversity of cultures and values.     

Conservation Planning as a Transdisciplinary Process

Abstract: Despite substantial growth in the field of conservation planning, the speed and success with which conservation plans are converted into conservation action remains limited. This gap between science and action extends beyond conservation planning into many other applied sciences and has been linked to complexity of current societal problems, compartmentalization of knowledge and management sectors, and limited collaboration between scientists and decision makers. Transdisciplinary approaches have been proposed as a possible way to address these challenges and to bridge the gap between science and action. These approaches move beyond the bridging of disciplines to an approach in which science becomes a social process resolving problems through the participation and mutual learning of stakeholders. We explored the principles of transdisciplinarity, in light of our experiences as conservation‐planning researchers working in South Africa, to better understand what is required to make conservation planning transdisciplinary and therefore more effective. Using the transdisciplinary hierarchy of knowledge (empirical, pragmatic, normative, and purposive), we found that conservation planning has succeeded in integrating many empirical disciplines into the pragmatic stakeholder‐engaged process of strategy development and implementation. Nevertheless, challenges remain in engagement of the social sciences and in understanding the social context of implementation. Farther up this knowledge hierarchy, at the normative and purposive levels, we found that a lack of integrated land‐use planning and policies (normative) and the dominant effect of national values (purposive) that prioritize growth and development limit the effectiveness and relevance of conservation plans. The transdisciplinary hierarchy of knowledge highlighted that we need to move beyond bridging the empirical and pragmatic disciplines into the complex normative world of laws, policies, and planning and become engaged in the purposive processes of decision making, behavior change, and value transfer. Although there are indications of progress in this direction, working at the normative and purposive levels requires time, leadership, resources, skills that are absent in conservation training and practice, and new forms of recognition in systems of scientific reward and funding.