Abstract: The social and spatial organization of urban fox groups prior to and during an outbreak of sarcoptic mange was compared with predictions derived from the resource dispersion hypothesis (RDH). We investigated the availability of three key resources. Neither daytime rest sites nor breeding sites appeared to be limited in availability. The availability of food deliberately supplied by local householders was examined by questionnaire surveys. The daily and weekly amount of food supplied was greatly in excess of the minimum requirements of a pair of foxes, but was consistent between territories. The availability of this food source increased markedly as a result of more people feeding the foxes. In agreement with the RDH, group size prior to the outbreak of mange increased from 2.25 animals (N=4) to 6.57 animals (N=7). Before the outbreak of mange, two territories were divided. Increased scavenge availability on smaller territories may have promoted these changes. Excluding these spatial changes, territories were very stable between years. After the outbreak of mange, group size declined as a direct result of mange-induced mortality. Surviving animals increased their ranges only after neighbouring groups had died out. Ranges did not increase in size in response to a decline in food availability. Nor were the increases in range size associated with the relinquishment of parts of the existing territory. These postmange changes are contrary to the RDH. Three factors may have promoted these changes: the elimination of interstitial space, the forced dispersal of young or future division of the territory.

Abstract: The innovation process is currently undergoing significant change in many industries. The World Wide Web has created a virtual world of collective intelligence and helped large groups of people connect and collaborate in the innovation process [1]. Von Hippel [2], for instance, states that a large number of users of a given technology will come up with innovative ideas. This process, originating in business, is now also being observed in science. Discussions around “Citizen Science” [3] and “Science 2.0” [4] suggest the same effects are relevant for fundamental research practices. “Crowdsourcing” [5] and “Open Innovation” [6] as well as other names for those paradigms, like Peer Production, Wikinomics, Swarm Intelligence etc., have become buzzwords in recent years. However, serious academic research efforts have also been started in many disciplines. In essence, these buzzwords all describe a form of collective intelligence that is enabled by new technologies, particularly internet connectivity. The focus of most current research on this topic is in the for-profit domain, i.e. organizations willing (and able) to pay large sums to source innovation externally, for instance through innovation contests. Our research is testing the applicability of Crowdsourcing and some techniques from Open Innovation to the scientific method and basic science in a non-profit environment (e.g., a traditional research university). If the tools are found to be useful, this may significantly change how some research tasks are conducted: While large, apriori unknown crowds of “irrational agents” (i.e. humans) are used to support scientists (and teams thereof) in several research tasks through the internet, the usefulness and robustness of these interactions as well as scientifically important factors like quality and validity of research results are tested in a systematic manner. The research is highly interdisciplinary and is done in collaboration with scientists from sociology, psychology, management science, economics, computer science, and artificial intelligence. After a pre-study, extensive data collection has been conducted and the data is currently being analyzed. The paper presents ideas and hypotheses and opens the discussion for further input.

Abstract: Summary Many publications documenting large-scale trends in the distribution of species make use of opportunistic citizen data, that is, observations of species collected without standardized field protocol and without explicit sampling design. It is a challenge to achieve reliable estimates of distribution trends from them, because opportunistic citizen science data may suffer from changes in field efforts over time (observation bias), from incomplete and selective recording by observers (reporting bias) and from geographical bias. These, in addition to detection bias, may lead to spurious trends. We investigated whether occupancy models can correct for the observation, reporting and detection biases in opportunistic data. Occupancy models use detection/nondetection data and yield estimates of the percentage of occupied sites (occupancy) per year. These models take the imperfect detection of species into account. By correcting for detection bias, they may simultaneously correct for observation and reporting bias as well. We compared trends in occupancy (or distribution) of butterfly and dragonfly species derived from opportunistic data with those derived from standardized monitoring data. All data came from the same grid squares and years, in order to avoid any geographical bias in this comparison. Distribution trends in opportunistic and monitoring data were well-matched. Strong trends observed in monitoring data were rarely missed in opportunistic data. Synthesis and applications. Opportunistic data can be used for monitoring purposes if occupancy models are used for analysis. Occupancy models are able to control for the common biases encountered with opportunistic data, enabling species trends to be monitored for species groups and regions where it is not feasible to collect standardized data on a large scale. Opportunistic data may thus become an important source of information to track distribution trends in many groups of species.

Abstract: Monitoring of the wolf Canis lupus is a demanding task as it lives in low densities, utilizes vast home ranges and disperses over large areas. These factors make obtaining accurate data about population parameters over the whole distribution area of the species impossible. Thus detailed local studies on socio-spatial organization are essential to calibrate information obtained over a larger area. We applied GPS/GSM telemetry, non-invasive genetic sampling, year-round tracking, camera trapping and howling stimulations to determine the number of family groups, population density and home-range sizes of wolves in the Drawa Forest (DF, western Poland, 2500 km2), an area recently recolonized by the species. Home ranges of three collared male wolves ranged from 321.8 to 420.6 km2 (MCP 100%) and from 187.5 to 277.5 km2 (Kernel 95%), but core areas had a size of 30.5-84.7 km2 (MCP50%) and 35.0-88.8 km2 (Kernel 50%). Mean near neighbour distance between centres of 6 tracked pack homesites was 15.3 km. The number of wolves in DF increased from 14 individuals in 2013/2014 to 30 in 2016/2017. The annual rate of increase varied from 43% in 2014/2015 to 7% in the final year. Population density for the whole study area was relatively low (1.2 indiv./100 km2 in 2016/2017), but densities within territories of two packs studied with telemetry were 1.9 and 1.5 indiv./100 km2. Mean pack size varied between 3.5 and 5.6 individuals, with the largest pack comprising 8 wolves. Mean number of pups observed in summers (June-August) was 4.5. Differences in home range sizes between wolves in western and eastern Poland indicate that results of regional studies cannot be freely extrapolated despite close genetic relationships. Thus, decisions related to management of wolf habitats should be based on intensive local studies.