Biogeographical and Taxonomic Biases in Tropical Forest Fragmentation Research

Despite several decades of research on the effects of fragmentation and habitat change on biodiversity, there remain strong biases in the geographical regions and taxonomic species studied. The knowledge gaps resulting from these biases are of particular concern if the forests most threatened with modification are also those for which the effects of such change are most poorly understood. To quantify the nature and magnitude of such biases, we conducted a systematic review of the published literature on forest fragmentation in the tropics for the period 1980–2012. Studies included focused on any type of response of single species, communities, or assemblages of any taxonomic group to tropical forest fragmentation and on fragmentation‐related changes to forests. Of the 853 studies we found in the SCOPUS database, 64% were conducted in the Neotropics, 13% in Asia, 10% in the Afrotropics, and 5% in Australasia. Thus, although the Afrotropics is subject to the highest rates of deforestation globally, it was the most disproportionately poorly studied biome. Significant taxonomic biases were identified. Of the taxonomic groups considered, herpetofauna was the least studied in the tropics, particularly in Africa. Research examining patterns of species distribution was by far the most common type (72%), and work focused on ecological processes (28%) was rare in all biomes, but particularly in the Afrotropics and for fauna. We suggest research efforts be directed toward less‐studied biogeographic regions, particularly where the threat of forest fragmentation continues to be high. Increased research investment in the Afrotropics will be important to build knowledge of threats and inform responses in a region where almost no efforts to restore its fragmented landscapes have yet begun and forest protection is arguably most tenuous. Sesgos Biogeográficos y Taxonómicos en la Investigación de la Fragmentación de Bosques Tropicales     

Effects of publication bias on conservation planning

Conservation planners need reliable information on spatial patterns of biodiversity. However, existing data sets are skewed because some ecosystems, taxa, and locations are underrepresented. We determined how many articles have been published in recent decades on the biodiversity of different countries and their constituent provinces. We searched the Web of Science catalogues Science Citation Index (SCI) and Social Science Citation Index (SSCI) for biodiversity-related articles published from 1993 to 2016 that included country and province names. We combined data on research publication frequency with other provincial-scale factors hypothesized to affect the likelihood of research activity (i.e., economic development, human presence, infrastructure, and remoteness). Areas that appeared understudied relative to the biodiversity expected based on site climate likely have been inaccessible to researchers for reasons, notably armed conflict. Geographic publication bias is of most concern in the most remote areas of sub-Saharan Africa and South America. Our provincial-scale model may help compensate for publication biases in conservation planning by revealing the spatial extent of research needs and the low cost of redoing this analysis annually.     

Improving inferences about private land conservation by accounting for incomplete reporting

Private lands provide key habitat for imperiled species and are core components of function protectected area networks; yet, their incorporation into national and regional conservation planning has been challenging. Identifying locations where private landowners are likely to participate in conservation initiatives can help avoid conflict and clarify trade-offs between ecological benefits and sociopolitical costs. Empirical, spatially explicit assessment of the factors associated with conservation on private land is an emerging tool for identifying future conservation opportunities. However, most data on private land conservation are voluntarily reported and incomplete, which complicates these assessments. We used a novel application of occupancy models to analyze the occurrence of conservation easements on private land. We compared multiple formulations of occupancy models with a logistic regression model to predict the locations of conservation easements based on a spatially explicit social–ecological systems framework. We combined a simulation experiment with a case study of easement data in Idaho and Montana (United States) to illustrate the utility of the occupancy framework for modeling conservation on private land. Occupancy models that explicitly accounted for variation in reporting produced estimates of predictors that were substantially less biased than estimates produced by logistic regression under all simulated conditions. Occupancy models produced estimates for the 6 predictors we evaluated in our case study that were larger in magnitude, but less certain than those produced by logistic regression. These results suggest that occupancy models result in qualitatively different inferences regarding the effects of predictors on conservation easement occurrence than logistic regression and highlight the importance of integrating variable and incomplete reporting of participation in empirical analysis of conservation initiatives. Failure to do so can lead to emphasizing the wrong social, institutional, and environmental factors that enable conservation and underestimating conservation opportunities in landscapes where social norms or institutional constraints inhibit reporting.     

A Bayesian hierarchical approach to quantifying stakeholder attitudes toward conservation in the presence of reporting error

Stakeholder support is vital for achieving conservation success, yet there are few reliable mechanisms to monitor stakeholder attitudes toward conservation. Approaches used to assess attitudes rarely account for bias arising from reporting error, which can lead to falsely reporting a positive attitude toward conservation (false-positive error) or not reporting a positive attitude when the respondent has a positive attitude toward conservation (false-negative error). Borrowing from developments in applied conservation science, we used a Bayesian hierarchical model to quantify stakeholder attitudes as the probability of having a positive attitude toward wildlife notionally (or in abstract terms) and at localized scales while accounting for reporting error. We compared estimates from our model, Likert scores, and naïve estimates (i.e., proportion of respondents reporting a positive attitude in at least 1 question that was only susceptible to false-negative error) with true stakeholder attitudes through simulations. We then applied the model in a survey of tea estate staff on their attitudes toward Asian elephants (Elephas maximus) in the Kaziranga–Karbi Anglong landscape of northeast India. In simulations, Bayesian model estimates of stakeholder attitudes toward wildlife were less biased than naïve estimates or Likert scores. After accounting for reporting errors, we estimated the probability of having a positive attitude toward elephants notionally as 0.85 in the Kaziranga landscape, whereas the proportion of respondents who had positive attitudes toward elephants at a localized scale was 0.50. In comparison, without accounting for reporting errors, naïve estimates of proportions of respondents with positive attitudes toward elephants were 0.69 and 0.23 notionally and at local scales, respectively. False (positive and negative) reporting probabilities were consistently not 0 (0.22–0.68). Regular and reliable assessment of stakeholder attitudes–combined with inference on drivers of positive attitudes–can help assess the success of initiatives aimed at facilitating human behavioral change and inform conservation decision making.     

Effects of site-selection bias on estimates of biodiversity change

Estimates of biodiversity change are essential for the management and conservation of ecosystems. Accurate estimates rely on selecting representative sites, but monitoring often focuses on sites of special interest. How such site-selection biases influence estimates of biodiversity change is largely unknown. Site-selection bias potentially occurs across four major sources of biodiversity data, decreasing in likelihood from citizen science, museums, national park monitoring, and academic research. We defined site-selection bias as a preference for sites that are either densely populated (i.e., abundance bias) or species rich (i.e., richness bias). We simulated biodiversity change in a virtual landscape and tracked the observed biodiversity at a sampled site. The site was selected either randomly or with a site-selection bias. We used a simple spatially resolved, individual-based model to predict the movement or dispersal of individuals in and out of the chosen sampling site. Site-selection bias exaggerated estimates of biodiversity loss in sites selected with a bias by on average 300–400% compared with randomly selected sites. Based on our simulations, site-selection bias resulted in positive trends being estimated as negative trends: richness increase was estimated as 0.1 in randomly selected sites, whereas sites selected with a bias showed a richness change of −0.1 to −0.2 on average. Thus, site-selection bias may falsely indicate decreases in biodiversity. We varied sampling design and characteristics of the species and found that site-selection biases were strongest in short time series, for small grains, organisms with low dispersal ability, large regional species pools, and strong spatial aggregation. Based on these findings, to minimize site-selection bias, we recommend use of systematic site-selection schemes; maximizing sampling area; calculating biodiversity measures cumulatively across plots; and use of biodiversity measures that are less sensitive to rare species, such as the effective number of species. Awareness of the potential impact of site-selection bias is needed for biodiversity monitoring, the design of new studies on biodiversity change, and the interpretation of existing data.     

Eliciting expert knowledge in conservation science

Expert knowledge is used widely in the science and practice of conservation because of the complexity of problems, relative lack of data, and the imminent nature of many conservation decisions. Expert knowledge is substantive information on a particular topic that is not widely known by others. An expert is someone who holds this knowledge and who is often deferred to in its interpretation. We refer to predictions by experts of what may happen in a particular context as expert judgments. In general, an expert-elicitation approach consists of five steps: deciding how information will be used, determining what to elicit, designing the elicitation process, performing the elicitation, and translating the elicited information into quantitative statements that can be used in a model or directly to make decisions. This last step is known as encoding. Some of the considerations in eliciting expert knowledge include determining how to work with multiple experts and how to combine multiple judgments, minimizing bias in the elicited information, and verifying the accuracy of expert information. We highlight structured elicitation techniques that, if adopted, will improve the accuracy and information content of expert judgment and ensure uncertainty is captured accurately. We suggest four aspects of an expert elicitation exercise be examined to determine its comprehensiveness and effectiveness: study design and context, elicitation design, elicitation method, and elicitation output. Just as the reliability of empirical data depends on the rigor with which it was acquired so too does that of expert knowledge.     

Five lessons to guide more effective biodiversity conservation message framing

Communication and advocacy approaches that influence attitudes and behaviors are key to addressing conservation problems, and the way an issue is framed can affect how people view, judge, and respond to an issue. Responses to conservation interventions can also be influenced by subtle wording changes in statements that may appeal to different values, activate social norms, influence a person's affect or mood, or trigger certain biases, each of which can differently influence the resulting engagement, attitudes, and behavior. We contend that by strategically considering how conservation communications are framed, they can be made more effective with little or no additional cost. Key framing considerations include, emphasizing things that matter to the audience, evoking helpful social norms, reducing psychological distance, leveraging useful biases, and, where practicable, testing messages. These lessons will help communicators think strategically about how to frame messages for greater effect.     

Trends in anecdotal fox sightings in Tasmania accounted for by psychological factors

There has been little evaluation of anecdotal sightings as a means to confirm new incursions of invasive species. This paper explores the potential for equivocal information communicated by the media to account for patterns of anecdotal reports. In 2001, it was widely reported that red foxes (Vulpes vulpes) had been deliberately released in the island state of Tasmania (Australia), although this claim was later revealed to be baseless. Regardless, by 2013 a total of 3153 anecdotal fox sightings had been reported by members of the public, which implied their distribution was wide. For each month in 2001–2003, we defined a monthly media index (MMI) of fox‐related media coverage, an index of their relative seasonal abundance (abundance), and a factor denoting claims of fox evidence (claimed evidence) regardless of its evidentiary quality. We fitted a generalized linear model with Poisson error for monthly totals of anecdotal sightings with factors of year and claimed evidence and covariates of MMI, abundance, and hours of darkness. The collective effect of psychological factors (MMI, claimed evidence, and year) relative to biophysical factors (photoperiod and abundance) was highly significant (χ² = 122.1, df = 6, p < 0.0001), whereas anticipated changes in abundance had no significant influence on reported sightings (p = 0.15). An annual index of fox media from 2001 to 2010 was strongly associated with the yearly tally of anecdotal sightings (p = 0.018). The odds ratio of sightings ranked as reliable by the fox eradication program in any year decreased exponentially at a rate of 0.00643 as the total number of sightings increased (p < 0.0001) and was indicative of an observer‐expectancy bias. Our results suggest anecdotal sightings are highly susceptible to cognitive biases and when used to qualify and quantify species presence can contribute to flawed risk assessments.     

Automated conservation assessment of the orchid family with deep learning

International Union for Conservation of Nature (IUCN) Red List assessments are essential for prioritizing conservation needs but are resource intensive and therefore available only for a fraction of global species richness. Automated conservation assessments based on digitally available geographic occurrence records can be a rapid alternative, but it is unclear how reliable these assessments are. We conducted automated conservation assessments for 13,910 species (47.3% of the known species in the family) of the diverse and globally distributed orchid family (Orchidaceae), for which most species (13,049) were previously unassessed by IUCN. We used a novel method based on a deep neural network (IUC-NN). We identified 4,342 orchid species (31.2% of the evaluated species) as possibly threatened with extinction (equivalent to IUCN categories critically endangered [CR], endangered [EN], or vulnerable [VU]) and Madagascar, East Africa, Southeast Asia, and several oceanic islands as priority areas for orchid conservation. Orchidaceae provided a model with which to test the sensitivity of automated assessment methods to problems with data availability, data quality, and geographic sampling bias. The IUC-NN identified possibly threatened species with an accuracy of 84.3%, with significantly lower geographic evaluation bias relative to the IUCN Red List and was robust even when data availability was low and there were geographic errors in the input data. Overall, our results demonstrate that automated assessments have an important role to play in identifying species at the greatest risk of extinction.     

Global protected-area coverage and human pressure on tidal flats

Tidal flats are a globally distributed coastal ecosystem important for supporting biodiversity and ecosystem services. Local to continental-scale studies have documented rapid loss of tidal habitat driven by human impacts, but assessments of progress in their conservation are lacking. With an internally consistent estimate of distribution and change, based on Landsat satellite imagery, now available for the world's tidal flats, we examined tidal flat representation in protected areas (PAs) and human pressure on tidal flats. We determined tidal flat representation and its net change in PAs by spatially overlaying tidal flat maps with the World Database of Protected Areas. Similarly, we overlaid the most recent distribution map of tidal flats (2014–2016) with the human modification map (HM_c) (range from 0, no human pressure, to 1, very high human pressure) to estimate the human pressure exerted on this ecosystem. Sixty-eight percent of the current extent of tidal flats is subject to moderate to very high human pressure (HM_c > 0.1), but 31% of tidal flat extent occurred in PAs, far exceeding PA coverage of the marine (6%) and terrestrial (13%) realms. Net change of tidal flat extent inside PAs was similar to tidal flat net change outside PAs from 1999 to 2016. Substantial shortfalls in protection of tidal flats occurred across Asia, where large intertidal extents coincided with high to very high human pressure (HM_c > 0.4–1.0) and net tidal flat losses up to 86.4 km² (95% CI 83.9–89.0) occurred inside individual PAs in the study period. Taken together, our results show substantial progress in PA designation for tidal flats globally, but that PA status alone does not prevent all habitat loss. Safeguarding the world's tidal flats will thus require deeper understanding of the factors that govern their dynamics and effective policy that promotes holistic coastal and catchment management strategies.