Canine fecal contamination in public green areas is difficult to control. Our objectives were to assess: (i) the relationship between fecal contamination in public green spaces of Buenos Aires (Argentina) and the type of green space (boulevard/square/park), considering their area, presence/absence of control measures, and human population density in the surroundings (high or low); (ii) the perception of people of such contamination and (iii) the frequency of parasites in dog feces. In each green space (n = 26), feces were counted in thirty 25-m2 plots randomly located. The opinion survey consisted of asking people (358) what their perception of fecal contamination was, whether it bothered them and whether they thought it was hazardous to the dogs’ or people’ health. Canine fecal samples randomly collected (n = 112) were concentrated and examined microscopically. Mixed-effect generalized linear models were fitted to evaluate the effect of selected variables on fecal contamination. People’s perception of human and canine health risks were assessed by logistic regression. Canine fecal contamination was lower in squares with control measures and in parks with control measures located in densely populated areas, and higher in boulevards located in densely populated areas. The visitor’s perception was that feces were associated with dirtiness (77%) and odor (30.8%). Parasites were detected in 51.8% of fecal samples. Our results confirm that environmental control measures decrease canine fecal contamination of public green spaces, and that more than 65% of the people interviewed do not relate fecal contamination to risks to human/dog health.
1. Information on resource selection by a species is essential for understanding the species' ecology, distribution and requirements for survival. Research on habitat selection frequently relies on animal detection at point locations to determine which resource units are used. A variety of approaches and statistical tools can be employed for assessing selection based on habitat variables measured in those units. The aim of this work is to evaluate the reliability of common sampling designs and statistical methods in detecting habitat selection at fine scales based on point data 2. We reviewed literature on microhabitat selection to determine characteristics of typical studies and analysed simulated small mammal live-trapping data as a case study. We considered various scenarios differing in the number of sampled units and sampling duration. For each scenario, a set of simulated surveys was analysed through two univariate
Accepted ArticleThis article is protected by copyright. All rights reserved.tests (Welch's t-and Mann-Whitney U-test), generalised linear models (GLMs), mixed-effect models (GLMMs) and occupancy models (OMs).
3.Analysis of simulated data revealed that overall performance of all statistical methods improved with increased trapping effort. Univariate tests were especially sensitive to the number of sampling units, while modelling methods took also advantage of longer trapping sessions. Univariate tests and GLMs provided partially correct information in most cases, whereas GLMMs and OMs offered higher probabilities of fully describing simulated habitat preferences.
4.With typical sampling efforts, appropriate statistical analysis of point data is able to provide a moderately accurate description of habitat selection at small scales, in spite of the violation of closure and independence assumptions of applied models. Modelling approaches are proliferating; we encourage using models that can deal with multiple sources of variability, such as GLMMs and OMs, when data are hierarchically structured.There is no a priori best survey design; it should be chosen according to the scope and goals of the study, environment heterogeneity, species characteristics and practical constraints.Researchers should realise that sampling design and statistical methods likely affect conclusions regarding habitat selection.
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