Transforming the results of species distribution modelling from probabilities of or suitabilities for species occurrence to presences/absences needs a specific threshold. Even though there are many approaches to determining thresholds, there is no comparative study. In this paper, twelve approaches were compared using two species in Europe and artificial neural networks, and the modelling results were assessed using four indices: sensitivity, specificity, overall prediction success and Cohen's kappa statistic. The results show that prevalence approach, average predicted probability/suitability approach, and three sensitivity-specificity-combined approaches, including sensitivity-specificity sum maximization approach, sensitivity-specificity equality approach and the approach based on the shortest distance to the top-left corner (0,1) in ROC plot, are the good ones. The commonly used kappa maximization approach is not as good as the afore-mentioned ones, and the fixed threshold approach is the worst one. We also recommend using datasets with prevalence of 50% to build models if possible since most optimization criteria might be satisfied or nearly satisfied at the same time, and therefore it's easier to find optimal thresholds in this situation.

https://onlinelibrary.wiley.com/doi/epdf/10.1111/j.0906-7590.2005.03957.x

Selecting thresholds of occurrence in the prediction of species distributions

Selecting thresholds of occurrence in the prediction of species

Seasonal variations in temperature, rainfall and resource availability are ubiquitous and can exert strong pressures on population dynamics. Infectious diseases provide some of the best-studied examples of the role of seasonality in shaping population fluctuations. In this paper, we review examples from human and wildlife disease systems to illustrate the challenges inherent in understanding the mechanisms and impacts of seasonal environmental drivers. Empirical evidence points to several biologically distinct mechanisms by which seasonality can impact host-pathogen interactions, including seasonal changes in host social behaviour and contact rates, variation in encounters with infective stages in the environment, annual pulses of host births and deaths and changes in host immune defences. Mathematical models and field observations show that the strength and mechanisms of seasonality can alter the spread and persistence of infectious diseases, and that population-level responses can range from simple annual cycles to more complex multiyear fluctuations. From an applied perspective, understanding the timing and causes of seasonality offers important insights into how parasite-host systems operate, how and when parasite control measures should be applied, and how disease risks will respond to anthropogenic climate change and altered patterns of seasonality. Finally, by focusing on well-studied examples of infectious diseases, we hope to highlight general insights that are relevant to other ecological interactions.

/pdf/seasonality-and-the-dynamics-of-infectious-diseases-1nbx7q5bgi.pdf

Seasonality and the dynamics of infectious diseases.

Area of home range (H) can be related empirically to body weight (W) by the formu- lation H = a Wk. The computed values of exponent k have generated controversy concerning potential differences between trophic groups and whether they differ from 0.75 (the value expected if area of home range is a function of basal metabolic rate). When large mammals are considered, the empirical relationship assumes the form H = .002W1 02 for herbivores, H = .059W.92 for omnivores, and H = .11W136 for carnivores. By treating the animal's energetic requirements and the productivity of its habitat explicitly, empirical values of k > 0.75 are shown to result from declining rates of production of utilizable energy per unit area of habitat with increasing body weight. While trophic status and weight modify the utilizable proportion of energy in the habitat, broad correlations also exist between size of home range and surrogate variables for productivity (precipitation and latitude). Differences in weight alone account for a large portion of the differences between male and female or subadult and adult home ranges. Behavioral phenomena need not be invoked. Differences between herbivores and carnivores are in the direction suggested for birds and mammals. Criticisms regarding inter-class and inter-trophic comparisons appear resolved.

Home Range and Body Weight--A Reevaluation

The chronic toxicity of aluminium, cadmium, mercury, and lead in birds: a review.

Site and landscape conditions at white-tailed deer/vehicle collision locations in Illinois

Mortality of white-tailed deer fawns in Southern Illinois

Survival of white-tailed deer (Odocoileus virginianus) fawns has been quantified throughout much of North America. However, few studies have assessed the influence of intrinsic factors (e.g., fawn age and birth mass) and habitat on fawn survival. During 2002–2004, we captured and radiocollared 166 fawns in southern Illinois, USA, to estimate survival rates, determine causes of mortality, and identify factors influencing fawn survival. We used a known fates model in program MARK to estimate survival rates and compare explanatory models based on Akaike's Information Criterion corrected for small sample sizes (AICc). We developed 2 candidate sets of a priori models to quantify factors influencing fawn survival: model set 1 included intrinsic factors and model set 2 focused on habitat variables. We recorded 64 mortalities and the overall survival rate was 0.59 (95% CI = 0.51–0.68). Predation was the leading source of mortality (64%) and coyotes (Canis latrans) were the most prominent predator. For model set 1, model {Sage×year} had the lowest AICc value suggesting that the age at mortality varied among capture years. For model set 2, model {Slandscape forest} had the lowest AICc value and indicated that areas inhabited by surviving fawns were characterized by a few large (i.e., >5 ha) irregular forest patches adjacent to several small nonforest patches, and survival areas also contained more edge habitat than mortality areas. Due to the magnitude of coyote predation, survival areas could have represented landscapes where coyotes were less effective at locating and capturing fawns when compared to mortality areas. This study was the first account of macrohabitat characteristics directly influencing fawn survival. Wildlife managers can use this information to determine how habitat management activities may affect deer populations.

Survival of White‐Tailed Deer Fawns in Southern Illinois

Exurban development is nonmetropolitan, residential development characterized by a human population density and average property size intermediate between suburban and rural areas. Although growth in exurban areas is outpacing that of urban, suburban, or rural landscapes, studies of deer (Odocoileus spp.) ecology in exurban areas are nonexistent. During 2003–2005, we studied space use (i.e., seasonal home-range and core-area size and habitat use relative to human dwellings) and survival of 43 female white-tailed deer (O. virginianus) in an exurban setting near Carbondale, Illinois. Deer had larger home ranges than most suburban deer populations and generally smaller home ranges than rural deer populations. When we analytically controlled for habitat use, deer exhibited a subtle avoidance of human dwellings, especially during the fawning season. The annual survival rate was among the highest reported in the literature at 0.872 (SE = 0.048). Only 5 deer (cause-specific mortality rate = 0.091) were ...

/pdf/space-use-and-survival-of-white-tailed-deer-in-an-exurban-2kpwd9zieo.pdf

Alan Woolf

Papers

Site and landscape conditions at white-tailed deer/vehicle collision locations in Illinois

Mortality of white-tailed deer fawns in Southern Illinois

Survival of White‐Tailed Deer Fawns in Southern Illinois

Space Use and Survival of White-Tailed Deer in an Exurban Landscape

Statewide modeling of bobcat, Lynx rufus, habitat in Illinois, USA