Scientific reasoning skills predict topic-specific knowledge after participation in a citizen science project on urban wildlife ecology

In citizen science (CS) projects, citizens who are not professional scientists participate in scientific research. Besides serving research purposes, CS projects provide participants opportunities for inquiry-based learning to promote their topic-specific knowledge and scientific reasoning skills. Previous research suggests that participants need scientific reasoning skills to engage in scientific activities and to learn from inquiry in CS projects. Participants' scientific reasoning skills, therefore, might enhance the resulting topic-specific knowledge at the end of a CS project. On the other hand, scientific reasoning skills themselves are a learning outcome of CS projects. Hence, they might play a double role in CS projects: as a learning outcome and as a prerequisite for acquiring knowledge. In the informal education context of CS, it has not yet been investigated whether scientific reasoning skills predict topic-specific knowledge or vice versa. To address this question, the research presented here used a cross-lagged panel design in two longitudinal field studies of a CS project on urban wildlife ecology (N = 144 participants). The results indicated that participants' scientific reasoning skills positively influenced their topic-specific knowledge at the end of the project, but not vice versa. Extending previous research on individual learning outcomes of CS projects, the results showed that inquiry-based learning in CS projects depends on certain prerequisites, such as participants' proficiency in scientific reasoning. We discuss the implications for future research on inquiry-based learning in CS projects and for further training of CS participants in acquiring scientific reasoning skills

To know about science is to love it? Unraveling cause–effect relationships between knowledge and attitudes toward science in citizen science on urban wildlife ecology

Nowadays, citizens collaborate increasingly with scientists in citizen science (CS) projects on environmental issues. CS projects often have educational goals and aim to increase citizens' knowledge with the ultimate goal of fostering positive attitudes toward science. To date, little is known about the extent to which CS projects strengthen the positive interrelationship between knowledge and attitudes. Based on previous research, it has been suggested that the knowledge–attitude relationship could be further examined by focusing on different aspects: (1) different attitudinal domains, (2) topic-specific knowledge, and (3) its direction. Our study contributes to the clarification of the interrelation between scientific knowledge and attitudes toward science within the specific domain of urban wildlife ecology using cross-lagged panel analyses. We collected survey data on five attitudinal domains, topic-specific knowledge, scientific reasoning abilities, and epistemological beliefs from N = 303 participants before and after they participated in a CS project on urban wildlife ecology. Participants collected and analyzed data on terrestrial mammals in a German metropolitan city. Our results provide evidence for the relationship between knowledge and attitudes due to the topic-specificity of knowledge in CS projects (e.g., wildlife ecology). Our method provided a rigorous assessment of the direction of the knowledge–attitude relationship and showed that topic-specific knowledge was a predictor of more positive attitudes toward science. © 2021 The Authors. Journal of Research in Science Teaching published by Wiley Periodicals LLC on behalf of National Association for Research in Science Teaching

Landscape Structures Affect Risk of Canine Distemper in Urban Wildlife

Urbanization rapidly changes landscape structure worldwide, thereby enlarging the human-wildlife interface. The emerging urban structures should have a key influence on the spread and distribution of wildlife diseases such as canine distemper, by shaping density, distribution and movements of wildlife. However, little is known about the role of urban structures as proxies for disease prevalence. To guide management, especially in densely populated cities, assessing the role of landscape structures in hampering or promoting disease prevalence is thus of paramount importance. Between 2008 and 2013, two epidemic waves of canine distemper hit the urban red fox (Vulpes vulpes) population of Berlin, Germany. The directly transmitted canine distemper virus (CDV) causes a virulent disease infecting a range of mammals with high host mortality, particularly in juveniles. We extracted information about CDV serological state (seropositive or seronegative), sex and age for 778 urban fox carcasses collected by the state laboratory Berlin Brandenburg. To assess the impact of urban landscape structure heterogeneity (e.g., richness) and shares of green and gray infrastructures at different spatial resolutions (areal of 28 ha, 78 ha, 314 ha) on seroprevalence we used Generalized Linear Mixed-Effects Models with binomial distributions. Our results indicated that predictors derived at a 28 ha resolution were most informative for describing landscape structure effects (AUC = 0.92). The probability to be seropositive decreased by 66% (0.6 to 0.2) with an increasing share of gray infrastructure (40 to 80%), suggesting that urbanization might hamper CDV spread in urban areas, owing to a decrease in host density (e.g., less foxes or raccoons) or an absence of wildlife movement corridors in strongly urbanized areas. However, less strongly transformed patches such as close-to-nature areas in direct proximity to water bodies were identified as high risk areas for CDV transmission. Therefore, surveillance and disease control actions targeting urban wildlife or human-wildlife interactions should focus on such areas. The possible underlying mechanisms explaining the prevalence distribution may be increased isolation, the absence of alternative hosts or an abiotic environment, all impairing the ability of CDV to persist without a host

Post-Crackdown Effectiveness of Field-Based Forest Law Enforcement in the Brazilian Amazon

<div><p>Regulatory enforcement of forest conservation laws is often dismissed as an ineffective approach to reducing tropical forest loss. Yet, effective enforcement is often a precondition for alternative conservation measures, such as payments for environmental services, to achieve desired outcomes. Fair and efficient policies to reducing emissions from deforestation and forest degradation (REDD) will thus crucially depend on understanding the determinants and requirements of enforcement effectiveness. Among potential REDD candidate countries, Brazil is considered to possess the most advanced deforestation monitoring and enforcement infrastructure. This study explores a unique dataset of over 15 thousand point coordinates of enforcement missions in the Brazilian Amazon during 2009 and 2010, after major reductions of deforestation in the region. We study whether local deforestation patterns have been affected by field-based enforcement and to what extent these effects vary across administrative boundaries. Spatial matching and regression techniques are applied at different spatial resolutions. We find that field-based enforcement operations have not been universally effective in deterring deforestation during our observation period. Inspections have been most effective in reducing large-scale deforestation in the states of Mato Grosso and Pará, where average conservation effects were 4.0 and 9.9 hectares per inspection, respectively. Despite regional and actor-specific heterogeneity in inspection effectiveness, field-based law enforcement is highly cost-effective on average and might be enhanced by closer collaboration between national and state-level authorities.</p></div

Wild inside: Urban wild boar select natural, not anthropogenic food resources

Most wildlife species are urban avoiders, but some became urban utilizers and dwellers successfully living in cities. Often, they are assumed to be attracted into urban areas by easily accessible and highly energetic anthropogenic food sources. We macroscopically analysed stomachs of 247 wild boar (Sus scrofa, hereafter WB) from urban areas of Berlin and from the surrounding rural areas. From the stomach contents we determined as predictors of food quality modulus of fineness (MOF,), percentage of acid insoluble ash (AIA) and macronutrients such as amount of energy and percentage of protein, fat, fibre and starch. We run linear mixed models to test: (1) differences in the proportion of landscape variables, (2) differences of nutrients consumed in urban vs. rural WB and (3) the impact of landscape variables on gathered nutrients. We found only few cases of anthropogenic food in the qualitative macroscopic analysis. We categorized the WB into five stomach content categories but found no significant difference in the frequency of those categories between urban and rural WB. The amount of energy was higher in stomachs of urban WB than in rural WB. The analysis of landscape variables revealed that the energy of urban WB increased with increasing percentage of sealing, while an increased human density resulted in poor food quality for urban and rural WB. Although the percentage of protein decreased in areas with a high percentage of coniferous forests, the food quality increased. High percentage of grassland decreased the percentage of consumed fat and starch and increased the percentage of fibre, while a high percentage of agricultural areas increased the percentage of consumed starch. Anthropogenic food such as garbage might serve as fallback food when access to natural resources is limited. We infer that urban WB forage abundant, natural resources in urban areas. Urban WB might use anthropogenic resources (e.g. garbage) if those are easier to exploit and more abundant than natural resources. This study shows that access to natural resources still is mandatory and drives the amount of protein, starch, fat or fibre in wild boar stomachs in urban as well as rural environments

Data sources.

<p>Data sources.</p

Study area and 2007 forest cover in the Legal Amazon.

<p>Study area and 2007 forest cover in the Legal Amazon.</p

Average treatment effects of inspections and inspection intensity.

<p>Average treatment effects of inspections and inspection intensity.</p

Counts of geo-referenced inspections across categories and observation periods.

<p>Counts of geo-referenced inspections across categories and observation periods.</p