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.     

Constituency Bias in a Federal Resource Management Agency: a Confirmatory Analysis

In a 1989 article, Ben Twight and Fremont Lyden compared the attitudes of national forest managers in the United States in 1981 with those of its major constituents to assess the extent to which the U.S. Forest Service was biased: were the beliefs and values of agency employees concerning resource management more representative of one of two major constituent groups, environmentalists and forest utilizers? The research tested Culhane's (Public Lands Politics: Interest Group Influence on the Forest Service and the Bureau of Land Management. Baltimore, Maryland: John Hopkins University Press. 1981) theory that the Forest Service occupies a middle ground in its attitudes relative to those of its environmental and utilization constituencies. They concluded that the agency did not; ideologically, district rangers were quite close to the Forest Service's utilizer constituency and relatively far from its environmentalist constituency. Given recent changes in the attitudes of Forest Services managers, the present study sought to answer the question: what do these changes reveal about the ideological position of the agency in 1981 vis a vis its position in 1990, and what are their implications for continuing concerns over the agency's representation of all interest groups? The response to survey questions of four groups—1990 district rangers and district rangers, environmentalists and forest utilizers in 1981—were combined for statistical comparison. Discriminant analyses were conducted to clarify the differences in the groups. Although the hypothesized bias of the Forest Service toward the traditional utilizer position was confirmed, the results also suggested that managers' values and attitudes had changed over the decade. The major issue underlying this bias—preservation versus utilization of resources—no longer adequately represented the agency's position, which has been fragmented into concerns with multiple issues.     

STATISTICAL AND GEOSTATISTICAL MODELLING OF PRELIMINARILY ADJUSTED DEFOLIATION ON AN EUROPEAN SCALE

Since 1987 tree crown condition is surveyed annually in large parts of Europe mainly in terms of defoliation. The plot-wise means of defoliation from 1994 to 2000 were evaluated by General Linear Models (GLM) in order to describe country-specific levels of defoliation and age-trends. Additional runs with estimates for influences of insects and fungi were performed. The amount of variance of defoliation explained by country, age and its interaction was between 35% and 59% for the main tree species, except for Quercus ilex. Additionally, up to 10% could be explained by the inclusion of estimates for infestations by insects and fungi. Residuals of the GLMs were taken as a measure of forest condition not biased by country or age effects and interpreted as preliminarily adjusted defoliation (PAD). PAD values were analysed using geostatistical methods. The modelled spatial autocorrelations were used for kriging. The resulting maps give an overview on regions with elevated defoliation, which may pinpoint regional causes of defoliation. The elimination of methodologically caused variance is a precondition of any cause–effect oriented analyses. The combination of explorative modelling and geostatistics will promote the choice of further promising predictors.     

Overcoming Endpoint Bias in Climate Change Communication: The Case of Arctic Sea Ice Trends

Unusually cold winters, a slowing in upward global temperatures, or an increase in Arctic sea ice extent are often falsely cast as here-and-now disconfirmation of the scientific consensus on climate change. Such conclusions are examples of “end point bias,” the well documented psychological tendency to interpret a recent short-term fluctuation as a reversal of a long-term trend. End point bias poses a challenge to those trying to communicate cross-decade climate warming trends. In this study, we demonstrate that exposure to misleading scientific information on FoxNews.com that evokes end point bias can affect the beliefs of liberals and moderates as well as conservatives. We also show that the leveraging-involving-visualizing-analogizing communication model can reduce the effects of endpoint bias among moderates and liberals at the same time as it dampens both the ideological and endpoint biasing of conservatives.     

Load Estimation Method Using Distributions with Covariates: A Comparison with Commonly Used Estimation Methods

Load estimates obtained using an approach based on statistical distributions with parameters expressed as a function of covariates (e.g., streamflow) (distribution with covariates hereafter called DC method) were compared to four load estimation methods: (1) flow‐weighted mean concentration; (2) integral regression; (3) segmented regression (the last two with Ferguson's correction factor); and (4) hydrograph separation methods. A total of 25 datasets (from 19 stations) of daily concentrations of total dissolved solids, nutrients, or suspended particulate matter were used. The selected stations represented a wide range of hydrological conditions. Annual flux errors were determined by randomly generating 50 monthly sample series from daily series. Annual and interannual biases and dispersions were evaluated and compared. The impact of sampling frequency was investigated through the generation of bimonthly and weekly surveys. Interannual uncertainty analysis showed that the performance of the DC method was comparable with those of the other methods, except for stations showing high hydrological variability. In this case, the DC method performed better, with annual biases lower than those characterizing the other methods. Results show that the DC method generated the smallest pollutant load errors when considering a monthly sampling frequency for rivers showing high variability in hydrological conditions and contaminant concentrations.     

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.     

The bean method as a tool to measure sensitive behavior

Conservationists need to measure human behavior to guide decisions and evaluate their impact. However, activities can be misreported and reporting accuracy may change following conservation interventions, making it hard to verify any apparent changes. Techniques for asking sensitive questions are increasingly integrated into survey designs to improve data quality, but some can be costly or hard for nonexperts to implement. We demonstrate a straightforward, low-cost approach, the bean method in which respondents give anonymous answers by adding a colored bean to a jar to denote a yes or no response. We applied the bean method to measure wild-meat hunting and trading over 2 years at a conservation-project (hunting reduction) site in Gola Forest, Liberia. We extended the technique to accommodate questions about hunting and meat-selling frequency. We compared responses given using the bean method and direct questioning for groups that did and did not participate in conservation interventions. Results from the bean method corresponded to those from direct questioning, and there was no indication of change in question sensitivity following conservation interventions. Estimates from both methods indicated that wild-meat trading decreased in project and nonproject households (from 36% to 20%) and that hunting decreased in 1 project group (38–28%). Where inconsistent answers were given (2–6% of respondents), differences were in both directions and were most likely attributable to measurement error. The bean method was quick and straightforward to administer in a low-literacy setting. We showed how it can be modified for answers of more than 2 categories and consider it a valuable tool that could be adapted for a wide range of conservation settings.     

Same stimuli,different responses: a pilot study assessing air pollution visibility impacts on emotional well-being in a controlled environment

● Emotional responses to visibility-reducing haze was assessed in a controlled lab. ● Valence and arousal have non-linear responses to pollution-caused low visibility. ● Repetitive exposure aggravates negative emotions in severely polluted conditions. ● Emotional bias to pollution relates with gender, decisiveness, attitude to clean air. A growing number of studies have shown that impaired visibility caused by particulate matter pollution influences emotional wellbeing. However, evidence is still scant on how this effect varies across individuals and over repetitive visual exposure in a controlled environment. Herein, we designed a lab-based experiment (41 subjects, 6 blocks) where participants were presented with real-scene images of 12 different PM_2.5 concentrations in each block. Emotional valence (negative to positive) and arousal (calm to excited) were self-rated by participants per image, and the response time for each rating was recorded. We find that as pollution level increases from 10 to 260 µg/m³, valence scores decrease, whereas arousal scores decline first and then bounce back, following a U-shaped trend. When air quality deteriorates, individual variability decreases in hedonic valence but increases in arousal. Over blocks, repetitive visual exposure increases valence at a moderate pollution level but aggravates negative emotions in severely polluted conditions (> 150 µg/m³). Finally, we find females, people who are slow in making responses, and those who are highly aroused by clean air tend to express more negative responses (so-called negativity bias) to ambient pollution than their respective counterparts. These results provide deeper insights into individual-level emotional responses to dirty air in a controlled environment. Although the findings in our pilot study should only be directly applied to the conditions assessed herein, we introduce a framework that can be replicated in different regions to assess the impact of air pollution on local emotional wellbeing.     

A GIS-based protocol for the collection and use of local knowledge in fisheries management planning

Despite a heavy reliance on scientific knowledge as the primary source of information in resource management, many resources are in decline, particularly in fisheries. To try and combat this trend, researchers have drawn upon the knowledge of local resource users as an important supplement to scientific knowledge in designing and implementing management strategies. The integration of local knowledge with scientific knowledge for marine species management, however, is problematic stemming primarily from conflicting data types. This paper considers the use of spatial information technology as a medium to integrate and visualise spatial distributions of both quantitative scientific data and qualitative local knowledge for the purposes of producing valid and locally relevant fisheries management plans. In this context, the paper presents a detailed protocol for the collection and subsequent use of local knowledge in fisheries management planning using geographic information systems (GIS). Particular attention is paid to the use of local knowledge in resource management, accuracy issues associated with the incorporation of qualitative data into a quantitative environment, base map selection and construction, and map bias or errors associated with the accuracy of recording harvest locations on paper map sheets, given the complications of map scale.