The Value of Using Feasibility Models in Systematic Conservation Planning to Predict Landholder Management Uptake

Understanding the social dimensions of conservation opportunity is crucial for conservation planning in multiple‐use landscapes. However, factors that influence the feasibility of implementing conservation actions, such as the history of landscape management, and landholders’ willingness to engage are often difficult or time consuming to quantify and rarely incorporated into planning. We examined how conservation agencies could reduce costs of acquiring such data by developing predictive models of management feasibility parameterized with social and biophysical factors likely to influence landholders’ decisions to engage in management. To test the utility of our best‐supported model, we developed 4 alternative investment scenarios based on different input data for conservation planning: social data only; biological data only; potential conservation opportunity derived from modeled feasibility that incurs no social data collection costs; and existing conservation opportunity derived from feasibility data that incurred collection costs. Using spatially explicit information on biodiversity values, feasibility, and management costs, we prioritized locations in southwest Australia to control an invasive predator that is detrimental to both agriculture and natural ecosystems: the red fox (Vulpes vulpes). When social data collection costs were moderate to high, the most cost‐effective investment scenario resulted from a predictive model of feasibility. Combining empirical feasibility data with biological data was more cost‐effective for prioritizing management when social data collection costs were low (<4% of the total budget). Calls for more data to inform conservation planning should take into account the costs and benefits of collecting and using social data to ensure that limited funding for conservation is spent in the most cost‐efficient and effective manner.     

Use of an Action‐Selection Framework for Human‐Carnivore Conflict in the Bangladesh Sundarbans

Abstract: Human–carnivore conflict is manifested in the death of humans, livestock, and carnivores. The resulting negative local attitudes and retribution killings imperil the future of many endangered carnivores. We tailored existing management tools to create a framework to facilitate the selection of actions to alleviate human–carnivore conflict and applied the framework to the human–tiger conflict in the Bangladesh Sundarbans. We identified potential actions that consider previous management efforts, local knowledge, cost‐effectiveness, fieldwork experience of authors and project staff, previous research on tiger ecology by the authors, and recommendations from human–carnivore conflict studies in other countries. Our framework includes creation of a profile to improve understanding of the nature of the conflict and its underlying causality. Identified actions include deterrents, education, direct tiger management, and response teams. We ranked actions by their potential to reduce conflict and the monetary cost of their implementation. We ranked tiger‐response teams and monitoring problem tigers as the two best actions because both had relatively high impact and cost‐effectiveness. We believe this framework could be used under a wide range of human–wildlife conflict situations because it provides a structured approach to selection of mitigating actions.     

Using Twitter to communicate conservation science from a professional conference

Scientists are increasingly using Twitter as a tool for communicating science. Twitter can promote scholarly discussion, disseminate research rapidly, and extend and diversify the scope of audiences reached. However, scientists also caution that if Twitter does not accurately convey science due to the inherent brevity of this media, misinformation could cascade quickly through social media. Data on whether Twitter effectively communicates conservation science and the types of user groups receiving these tweets are lacking. To address these knowledge gaps, we examined live tweeting as a means of communicating conservation science at the 2013 International Congress for Conservation Biology (ICCB). We quantified and compared the user groups sending and reading live tweets. We also surveyed presenters to determine their intended audiences, which we compared with the actual audiences reached through live tweeting. We also asked presenters how effectively tweets conveyed their research findings. Twitter reached 14 more professional audience categories relative to those attending and live tweeting at ICCB. However, the groups often reached through live tweeting were not the presenters’ intended audiences. Policy makers and government and non‐governmental organizations were rarely reached (0%, 4%, and 6% of audience, respectively), despite the intent of the presenters. Plenary talks were tweeted about 6.9 times more than all other oral or poster presentations combined. Over half the presenters believed the tweets about their talks were effective. Ineffective tweets were perceived as vague or missing the presenters’ main message. We recommend that presenters who want their science to be communicated accurately and broadly through Twitter should provide Twitter‐friendly summaries that incorporate relevant hashtags and usernames. Our results suggest that Twitter can be used to effectively communicate speakers’ findings to diverse audiences beyond conference walls.     

Public Willingness to Pay for Recovering and Downlisting Threatened and Endangered Marine Species

Abstract: Nonmarket valuation research has produced economic value estimates for a variety of threatened, endangered, and rare species around the world. Although over 40 value estimates exist, it is often difficult to compare values from different studies due to variations in study design, implementation, and modeling specifications. We conducted a stated‐preference choice experiment to estimate the value of recovering or downlisting 8 threatened and endangered marine species in the United States: loggerhead sea turtle (Caretta caretta), leatherback sea turtle (Dermochelys coriacea), North Atlantic right whale (Eubalaena glacialis), North Pacific right whale (Eubalaena japonica), upper Willamette River Chinook salmon (Oncorhynchus tshawytscha), Puget Sound Chinook salmon (Oncorhynchus tshawytscha), Hawaiian monk seals (Monachus schauinslandi), and smalltooth sawfish (Pristis pectinata). In May 2009, we surveyed a random sample of U.S. households. We collected data from 8476 households and estimated willingness to pay for recovering and downlisting the 8 species from these data. Respondents were willing to pay for recovering and downlisting threatened and endangered marine taxa. Willingness‐to‐pay values ranged from $40/household for recovering Puget Sound Chinook salmon to $73/household for recovering the North Pacific right whale. Statistical comparisons among willingness‐to‐pay values suggest that some taxa are more economically valuable than others, which suggests that the U.S. public's willingness to pay for recovery may vary by species.     

Priority areas for conservation of and research focused on terrestrial vertebrates

Effective conservation policies require comprehensive knowledge of biodiversity. However, knowledge shortfalls still remain, hindering possibilities to improve decision making and built such policies. During the last 2 decades, conservationists have made great efforts to allocate resources as efficiently as possible but have rarely considered the idea that if research investments are also strategically allocated, it would likely fill knowledge gaps while simultaneously improving conservation actions. Therefore, prioritizing areas where both conservation and research actions could be conducted becomes a critical endeavor that can further maximize return on investment. We used Zonation, a conservation planning tool and geographical distributions of amphibians, birds, mammals, and reptiles to suggest and compare priority areas for conservation and research of terrestrial vertebrates worldwide. We also evaluated the degree of human disturbance in both types of priority areas by describing the value of the human footprint index within such areas. The spatial concordance between priority conservation and research areas was low: 0.36% of the world's land area. In these areas, we found it would be possible to protect almost half of the currently threatened species and to gather information on nearly 42% of data-deficient (DD) species. We also found that 6199 protected areas worldwide are located in such places, although only 35% of them have strict conservation purposes. Areas of consensus between conservation and research areas represent an opportunity for simultaneously conserving and acquiring knowledge of threatened and DD species of vertebrates. Although the picture is not the most encouraging, joint conservation and research efforts are possible and should be fostered to save vertebrate species from our own ignorance and extinction.     

Quantifying progress toward a conservation assessment for all plants

The Global Strategy for Plant Conservation (GSPC) set an ambitious target to achieve a conservation assessment for all known plant species by 2020. We consolidated digitally available plant conservation assessments and reconciled their scientific names and assessment status to predefined standards to provide a quantitative measure of progress toward this target. The 241,919 plant conservation assessments generated represent 111,824 accepted land plant species (vascular plants and bryophytes, not algae). At least 73,081 and up to 90,321 species have been assessed at the global scale, representing 21–26% of known plant species. Of these plant species, at least 27,148 and up to 32,542 are threatened. Eighty plant families, including some of the largest, such as Asteraceae, Orchidaceae, and Rubiaceae, are underassessed and should be the focus of assessment effort if the GSPC target is to be met by 2020. Our data set is accessible online (ThreatSearch) and is a baseline that can be used to directly support other GSPC targets and plant conservation action. Although around one‐quarter of a million plant assessments have been compiled, the majority of plants are still unassessed. The challenge now is to build on this progress and redouble efforts to document conservation status of unassessed plants to better inform conservation decisions and conserve the most threatened species.     

Inferring extinction of mammals from sighting records, threats, and biological traits

For species with five or more sightings, quantitative techniques exist to test whether a species is extinct on the basis of distribution of sightings. However, 70% of purportedly extinct mammals are known from fewer than five sightings, and such models do not include some important indicators of the likelihood of extinction such as threats, biological traits, search effort, and demography. Previously, we developed a quantitative method that we based on species' traits in which we used Cox proportional hazards regression to calculate the probability of rediscovery of species regarded as extinct. Here, we used two versions of the Cox regression model to determine the probability of extinction in purportedly extinct mammals and compared the results of these two models with those of stationary Poisson, nonparametric, and Weibull sighting-distribution models. For mammals with five or more sightings, the stationary Poisson model categorized all but two critically endangered (flagged as possibly extinct) species in our data set as extinct, and results with this model were consistent with current categories of the International Union for the Conservation of Nature. The scores of probability of rediscovery for individual species in one version of our Cox regression model were correlated with scores assigned by the stationary Poisson model. Thus, we used this Cox regression model to determine the probability of extinction of mammals with sparse records. On the basis of the Cox regression model, the most likely mammals to be rediscovered were the Montane monkey-faced bat (Pteralopex pulchra), Armenian myotis (Myotis hajastanicus), Alcorn's pocket gopher (Pappogeomys alcorni), and Wimmer's shrew (Crocidura wimmeri). The Cox model categorized two species that have recently disappeared as extinct: the baiji (Lipotes vexillifer) and the Christmas Island pipistrelle (Pipistrellus murrayi). Our new method can be used to test whether species with few records or recent last-sighting dates are likely to be extinct.     

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.     

Knowledge gain and behavioral change in citizen-science programs

Citizen-science programs are often touted as useful for advancing conservation literacy, scientific knowledge, and increasing scientific-reasoning skills among the public. Guidelines for collaboration among scientists and the public are lacking and the extent to which these citizen-science initiatives change behavior is relatively unstudied. Over two years, we studied 82 participants in a three-day program that included education about non-native invasive plants and collection of data on the occurrence of those plants. Volunteers were given background knowledge about invasive plant ecology and trained on a specific protocol for collecting invasive plant data. They then collected data and later gathered as a group to analyze data and discuss responsible environmental behavior with respect to invasive plants. We tested whether participants without experience in plant identification and with little knowledge of invasive plants increased their knowledge of invasive species ecology, participation increased knowledge of scientific methods, and participation affected behavior. Knowledge of invasive plants increased on average 24%, but participation was insufficient to increase understanding of how scientific research is conducted. Participants reported increased ability to recognize invasive plants and increased awareness of effects of invasive plants on the environment, but this translated into little change in behavior regarding invasive plants. Potential conflicts between scientific goals, educational goals, and the motivation of participants must be considered during program design.     

A field experiment characterizing variable detection rates during plant surveys

Surveys aimed at finding threatened and invasive species can be challenging due to individual rarity and low and variable individual detection rates. Detection rate in plant surveys typically varies due to differences among observers, among the individual plants being surveyed (targets), and across background environments. Interactions among these 3 components may occur but are rarely estimated due to limited replication and control during data collection. We conducted an experiment to investigate sources of variation in detection of 2 Pilosella species that are invasive and sparsely distributed in the Alpine National Park, Australia. These species are superficially similar in appearance to other yellow-flowered plants occurring in this landscape. We controlled the presence and color of flowers on target Pilosella plants and controlled their placement in plots, which were selected for their variation in cover of non-target yellow flowers and dominant vegetation type. Observers mimicked Pilosella surveys in the plots and reported 1 categorical and 4 quantitative indicators of their survey experience level. We applied survival analysis to detection data to model the influence of both controlled and uncontrolled variables on detection rate. Orange- and yellow-flowering Pilosella in grass- and heath-dominated vegetation were detected at a higher rate than nonflowering Pilosella. However, this detection gain diminished as the cover of other co-occurring yellow-flowering species increased. Recent experience with Pilosella surveys improved detection rate. Detection experiments are a direct and accessible means of understanding detection processes and interpreting survey data for threatened and invasive species. Our detection findings have been used for survey planning and can inform progress toward eradication. Interaction of target and background characteristics determined detection rate, which enhanced predictions in the Pilosella eradication program and demonstrated the difficulty of transferring detection findings into untested environments.