The curse of observer experience: Error in noninvasive genetic sampling

Exploring local perceptions and drivers of engagement in biodiversity monitoring among participants in payments for ecosystem services schemes in southeastern Mexico

Payments for ecosystem services (PES) are widely applied incentive-based instruments with diverse objectives that increasingly include biodiversity conservation. Yet, there is a gap in understanding of how to best assess and monitor programs' biodiversity outcomes. We examined perceptions and drivers of engagement related to biodiversity monitoring through surveys among current PES participants in 7 communities in Mexico's Selva Lacandona. We conducted workshops among survey participants that included training and field deployment of tools used to monitor biodiversity and land cover, including visual transects, camera traps, acoustic recorders, and forest cover satellite images. We conducted pre- and postworkshop surveys in each community to evaluate changes in respondents' perceptions following exposure to biodiversity monitoring training and related field activities. We also reviewed existing research on participatory environmental management and monitoring approaches. One quarter of current PES participants in the study area participated in our surveys and workshops. The majority stated interest in engaging in diverse activities related to the procedural aspects of biodiversity monitoring (e.g., planning, field data collection, results dissemination) and acknowledged multiple benefits of introducing biodiversity monitoring into PES (e.g., knowledge and capacity building, improved natural resource management, and greater support for conservation). Household economic reliance on PES was positively associated with willingness to engage in monitoring. Technical expertise, time, and monetary constraints were deterrents. Respondents were most interested in monitoring mammals, birds, and plants and using visual transects, camera traps, and forest cover satellite images. Exposure to monitoring enhanced subsequent interest in monitoring by providing respondents with new insights from their communities related to deforestation and species' abundance and diversity. Respondents identified key strengths and weaknesses of applying different monitoring tools, which suggests that deploying multiple tools simultaneously can increase local engagement and produce complementary findings and data. Overall, our findings support the relevance and usefulness of incorporating participatory biodiversity monitoring into PES.

Reducing the impact of cognitive bias in decision making: Practical actions for forensic science practitioners

Previously published methods for reducing the impact of cognitive bias in forensic decision making have focused primarily on actions at the laboratory or organizational levels. This paper presents generalized and specific actions that forensic science practitioners can take to reduce the impact of cognitive bias in their work. Practical examples illustrating ways that practitioners can implement many of the specific actions are also provided, along with some suggestions for handling court testimony about cognitive bias. The actions presented in this paper provide a means through which individual practitioners can take ownership for minimizing cognitive bias in their work. Such actions can provide supporting evidence to stakeholders that forensic practitioners acknowledge the existence of cognitive bias and its potential influence on their work, and they can also stimulate implementation of methods that focus on solutions at the laboratory and organizational levels.

The importance of evaluating standard monitoring methods: Observer bias and detection probabilities for moose pellet group surveys

Counting is not always a simple exercise. Specimens can be misidentified or not detected when they are present, giving rise to unidentified sources of error. Deer pellet group counts are a common method to monitor abundance, density, and population trend. Yet, detection errors and observer bias could introduce error into sometimes very large (spatially, temporally) datasets. For example, in Scandinavia, moose (Alces alces) pellet group counts are conducted by volunteer hunters and students, but it is unknown how much uncertainty observer error introduces into these datasets. Our objectives were to 1) estimate the detection probability of moose pellet groups; 2) identify the primary variables leading to detection errors including prior observer experience; and 3) compare density estimates using single and double observer counts. We selected a subset of single observer plots from a long-term monitoring project to be conducted as dependent double observer surveys, where primary and secondary observers worked simultaneously in the field. We did this to quantify detection errors for moose pellet groups, which were previously unknown in Scandinavia, and to identify covariates which introduced variation into our estimates. Our study area was in the boreal forests of southern Norway where we had a nested grid of 100-m2 plots that we surveyed each spring. Our observers were primarily inexperienced. We found that when pellet groups were detected by the primary observer, the secondary observer saw additional pellet groups 42% of the time. We found search time was the primary covariate influencing detection. We also found density estimates from double observer counts were 1.4 times higher than single observer counts, for the same plots. This density underestimation from single observer surveys could have consequences to managers, who sometimes use pellet counts to set harvest quotas. We recommend specific steps to improve future moose pellet counts.

Individual Identification of Large Felids in Field Studies: Common Methods, Challenges, and Implications for Conservation Science

Large felids represent some of the most threatened large mammals on Earth, critical for both tourism economies and ecosystem function. Most populations are in a state of decline, and their monitoring and enumeration is therefore critical for conservation. This typically rests on the accurate identification of individuals within their populations. We review the most common and current survey methods used in individual identification studies of large felid ecology (body mass > 25 kg). Remote camera trap photography is the most extensively used method to identify leopards, snow leopards, jaguars, tigers, and cheetahs which feature conspicuous and easily identifiable coat patterning. Direct photographic surveys and genetic sampling are commonly used for species that do not feature easily identifiable coat patterning such as lions. We also discuss the accompanying challenges encountered in several field studies, best practices that can help increase the precision and accuracy of identification and provide generalised ratings for the common survey methods used for individual identification.

Application of the community dialogues method to identify ethical values and priorities related to pharmacogenomics

Given the expansion of genetics in medicine, there is a growing need to develop approaches to engage patients in understanding how genetics affects their health. Various qualitative methods have been applied to gain a deeper understanding of patient perspectives in topics related to genetics. Community dialogues (CD) are a bi-directional research method that invites community members to discuss a pertinent, challenging topic over the course of a multi-week period and the community members openly discuss their positions on the topic. Authors discuss the first application of the CD method to the topic of pharmacogenetics testing. Additional CD are needed to engage diverse participant populations on this topic to improve genetics literacy, enhance physician engagement and drive policy change.

Cognitive and Human Factors in Expert Decision Making: Six Fallacies and the Eight Sources of Bias

Fallacies about the nature of biases have shadowed a proper cognitive understanding of biases and their sources, which in turn lead to ways that minimize their impact. Six such fallacies are presented: it is an ethical issue, only applies to "bad apples", experts are impartial and immune, technology eliminates bias, blind spot, and the illusion of control. Then, eight sources of bias are discussed and conceptualized within three categories: (A) factors that relate to the specific case and analysis, which include the data, reference materials, and contextual information, (B) factors that relate to the specific person doing the analysis, which include past experience base rates, organizational factors, education and training, and personal factors, and lastly, (C) cognitive architecture and human nature that impacts all of us. These factors can impact what the data are (e.g., how data are sampled and collected, or what is considered as noise and therefore disregarded), the actual results (e.g., decisions on testing strategies, how analysis is conducted, and when to stop testing), and the conclusions (e.g., interpretation of the results). The paper concludes with specific measures that can minimize these biases.