Tilting at wildlife: reconsidering human-wildlife conflict

Conflicts between people over wildlife are widespread and damaging to both the wildlife and people involved. Such issues are often termed human–wildlife conflicts. We argue that this term is misleading and may exacerbate the problems and hinder resolution. A review of 100 recent articles on human–wildlife conflicts reveals that 97 were between conservation and other human activities, particularly those associated with livelihoods. We suggest that we should distinguish between human–wildlife impacts and human–human conflicts and be explicit about the different interests involved in conflict. Those representing conservation interests should not only seek technical solutions to deal with the impacts but also consider their role and objectives, and focus on strategies likely to deliver long-term solutions for the benefit of biodiversity and the people involved

What's on the horizon for community-based conservation? Emerging threats and opportunities

Community-based conservation can support livelihoods and biodiversity, while reinforcing local and Indigenous values, cultures, and institutions. Its delivery can help address cross-cutting global challenges, such as climate change, conservation, poverty, and food security. Therefore, understanding trends in community-based conservation is pertinent to setting and implementing global goals. We undertook a horizon scan to prioritize 15 emerging threats and opportunities expected to impact the future effectiveness of community-based conservation. Topics relate to global biodiversity policy; human rights; shifting human geography; inclusion, diversity, equity, and access; conservation finance and income; and economic reforms. Our findings offer guidance on strengthening community-based conservation to achieve global environmental and development goals

Predicting microbial response to anthropogenic environmental disturbances using artificial neural network and multiple linear regression

A mathematical model that quantitively describes the combined effect of different environmental variables can be used to predict the growth dynamics of a microorganism. This study evaluates the potential of an artificial neural network (ANN) model to predict the growth of a bacterial strain, Klebsiella sp., during the treatment of diclofenac sodium contaminated wastewaters. Input parameters, temperature, pH, time, agitation and diclofenac sodium concentration were randomly combined to conduct the batch experiments. Experimental data sets obtained were used for the training and optimization of programme learning. The efficiency of the ANN model was demonstrated by comparing it with the multiple linear regression (MLR) model. Root mean squared error (RMSE) and coefficient of determination (R2) were used as model performance parameters. The results obtained depict that the ANN model with RMSE 0.0124 and R2 value 0.926 in the test phase exhibited higher prediction performance. In contrast, low prediction performance was exhibited by the MLR model with RMSE 0.0230 and R2 value of 0.710. The results of this study are expected to guide the development of appropriate operational conditions for bioremediation of wastewater and industrial scale-up of the process

Data from: Multiscale factors affecting human attitudes toward snow leopards and wolves

The threat posed by large carnivores to livestock and humans makes peaceful coexistence between them difficult. Effective implementation of conservation laws and policies depends on the attitudes of local residents toward the target species. There are many known correlates of human attitudes toward carnivores, but they have only been assessed at the scale of the individual. Because human societies are organized hierarchically, attitudes are presumably influenced by different factors at different scales of social organization, but this scale dependence has not been examined. We used structured interview surveys to quantitatively assess the attitudes of a Buddhist pastoral community toward snow leopards (Panthera uncia) and wolves (Canis lupus). We interviewed 381 individuals from 24 villages within 6 study sites across the high-elevation Spiti Valley in the Indian Trans-Himalaya. We gathered information on key explanatory variables that together captured variation in individual and village-level socioeconomic factors. We used hierarchical linear models to examine how the effect of these factors on human attitudes changed with the scale of analysis from the individual to the community. Factors significant at the individual level were gender, education, and age of the respondent (for wolves and snow leopards), number of income sources in the family (wolves), agricultural production, and large-bodied livestock holdings (snow leopards). At the community level, the significant factors included the number of smaller-bodied herded livestock killed by wolves and mean agricultural production (wolves) and village size and large livestock holdings (snow leopards). Our results show that scaling up from the individual to higher levels of social organization can highlight important factors that influence attitudes of people toward wildlife and toward formal conservation efforts in general. Such scale-specific information can help managers apply conservation measures at appropriate scales. Our results reiterate the need for conflict management programs to be multipronged

Multiscale factors affecting human attitudes toward snow leopards and wolves-Dataset

Sheet 1: Contains the meta data describing all the column heads of sheet 2. Sheet 2 contains the raw data. Column heads describe the values contained within them. Rows are individual observations (interviews)

Assessing the Vulnerability of Cancer Patients for COVID-19

Severe acute respiratory syndrome involving corona virus-2 (SARS-CoV-2) has been implied to cause COVID-19 disease, leading to an unprecedented health emergency across the globe with a staggering figure of mortality rate. Measures to control the pandemic are pushing the economy into a tailspin, putting burden not only on the individuals but also on the nations. Despite the widespread infection rates, young people have shown better recovery rate while COVID-19 symptoms are more pronounced in elderly and people with comorbid conditions such as diabetes, cardiac and respiratory diseases. Cancer is a highly prevalent disease affecting millions of individuals. In this study, we analyzed the expression status of genes that are required for SARS-CoV-2 infectivity and its propagation to assess the susceptibility of certain cancer patients to infection and subsequent complications. Our data indicate that patients with colon, rectum, cholangiocarcinoma, lung adenoma, kidney renal papillary cell carcinoma and kidney renal clear cell carcinoma are more at risk for COVID-19. Genes that are responsible for severe COVID-19 are also highly expressed in many cancer types. We also carried out the association rule mining analysis which is helpful in predicting the expression of proviral genes in various cancers