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Detecting density‐dependence (DD) in population fluctuations is essential for population control, herd management and harvest programs. However, little information has been obtained for deer in Mediterranean ecosystems. We assess the effects of density, climate and harvesting on population growth fluctuations of two sympatric red (Cervus elaphus hispanicus) and fallow deer (Dama dama), in two hunting reserves located in a protected area of south‐east Spain. We use time series analysis of 16 consecutive years of abundance estimates and hunting extractions. We hypothesized a negative influence of density, deer hunting and drought on population growth rates. Through an integrative Bayesian state‐space modeling making possible a unified population analysis we have been able to not only estimate the effects of DD, climate and harvest extractions but also to evaluate their relative impacts on deer population in each location. We found deer populations exhibiting a very strong potential to grow from low density. We detected DD having a stronger effect than hunting and climatic factors, especially for overabundant deer populations close to their carrying capacity. Density‐dependent mechanisms compensate harvest extractions, reducing hunting effectiveness. Severe summer drought conditions had negative effects on growth rates and eventually led to delayed autumn deer deaths. The weather effect is more important the more abundant the deer are. In a climate change context, in Mediterranean environments the importance of weather factors could become much greater if both ungulates’ densities and aridity increase. Population size control here could modulate the negative effects of climatic constraints on deer and ecosystems conservation.

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Urbanization creates new ecological conditions that can affect biodiversity at all levels, including the diversity and prevalence of parasites of species that may occupy these environments. However, few studies have compared bird–ectoparasite interactions between urban and rural individuals. Here, we analyze the ectoparasite community and co-infection patterns of urban and rural burrowing owls, Athene cunicularia, to assess the influence of host traits (i.e., sex, age, and weight), and environmental factors (i.e., number of conspecifics per nest, habitat type and aridity) on its composition. Ectoparasites of burrowing owls included two lice, one flea, and one mite. The overall prevalence for mites, lice and fleas was 1.75%, 8.76% and 3.50%, respectively. A clear pattern of co-infection was detected between mites and fleas and, to less extent, between mites and lice. Adult owls harbored fewer ectoparasites than nestlings, and adult females harbored more lice than males. Our results also show that mite and flea numbers were higher when more conspecifics cohabited the same burrow, while lice showed the opposite pattern. Rural individuals showed higher flea parasitism and lower mite parasitism than urban birds. Moreover, mite numbers were negatively correlated with aridity and host weight. Although the ectoparasitic load of burrowing owls appears to be influenced by individual age, sex, number of conspecifics per nest, and habitat characteristics, the pattern of co-infection found among ectoparasites could also be mediated by unexplored factors such as host immune response, which deserves further research.

The objective of this paper is to find out possible patterns of failure occurrences in a repairable system. We develop a graphical exploratory tool and perform visual inference considering non‐parametric local linear kernel estimators for the rate of occurrence of failures (ROCOF) and its first derivative. The shape characteristics of the ROCOF function are distinguished from those which are merely an artefact of the sampling variability of the data through the construction of confidence intervals for the first derivative. The proposal is illustrated with several real data sets, and its performance is evaluated through an extensive simulation study.

In this article, the graphical exploratory tool SiZer Map is constructed for the inference of the rate of occurrences of events that follow a nonhomogeneous Poisson process. SiZer Map is defined considering nonparametric local linear kernel estimators for the rate function and its first derivative. The bandwidth parameter is the viewing scale, and the considered time interval is the localization space. The shape characteristics of the rate function are distinguished from those which are merely an artifact of the sampling variability of the data through confidence intervals for the first derivative. The proposal is illustrated with some real data sets that represent recurrent events in time.

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