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Milena Stillfried

Bio: Milena Stillfried is an academic researcher from Leibniz Association. The author has contributed to research in topics: Citizen science & Population. The author has an hindex of 6, co-authored 14 publications receiving 810 citations.

Papers
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Journal ArticleDOI
TL;DR: It is concluded that a substantial improvement in the quality of model predictions can be achieved if uneven sampling effort is taken into account, thereby improving the efficacy of species conservation planning.
Abstract: Aim Advancement in ecological methods predicting species distributions is a crucial precondition for deriving sound management actions. Maximum entropy (MaxEnt) models are a popular tool to predict species distributions, as they are considered able to cope well with sparse, irregularly sampled data and minor location errors. Although a fundamental assumption of MaxEnt is that the entire area of interest has been systematically sampled, in practice, MaxEnt models are usually built from occurrence records that are spatially biased towards better-surveyed areas. Two common, yet not compared, strategies to cope with uneven sampling effort are spatial filtering of occurrence data and background manipulation using environmental data with the same spatial bias as occurrence data. We tested these strategies using simulated data and a recently collated dataset on Malay civet Viverra tangalunga in Borneo. Location Borneo, Southeast Asia. Methods We collated 504 occurrence records of Malay civets from Borneo of which 291 records were from 2001 to 2011 and used them in the MaxEnt analysis (baseline scenario) together with 25 environmental input variables. We simulated datasets for two virtual species (similar to a range-restricted highland and a lowland species) using the same number of records for model building. As occurrence records were biased towards north-eastern Borneo, we investigated the efficacy of spatial filtering versus background manipulation to reduce overprediction or underprediction in specific areas. Results Spatial filtering minimized omission errors (false negatives) and commission errors (false positives). We recommend that when sample size is insufficient to allow spatial filtering, manipulation of the background dataset is preferable to not correcting for sampling bias, although predictions were comparatively weak and commission errors increased. Main Conclusions We conclude that a substantial improvement in the quality of model predictions can be achieved if uneven sampling effort is taken into account, thereby improving the efficacy of species conservation planning.

822 citations

Journal ArticleDOI
TL;DR: In this paper, the authors used 13 microsatellite loci to genotype 387 adult and subadult wild boars from four urban forests, adjacent built-up areas and the surrounding rural forests.
Abstract: Summary Urban sprawl has resulted in the permanent presence of large mammal species in urban areas, leading to human–wildlife conflicts. Wild boar Sus scrofa are establishing a permanent presence in many cities in Europe, with the largest German urban population occurring in Berlin. Despite their relatively long-term presence, there is little knowledge of colonization processes, dispersal patterns or connectivity of Berlin's populations, hampering the development of effective management plans. We used 13 microsatellite loci to genotype 387 adult and subadult wild boar from four urban forests, adjacent built-up areas and the surrounding rural forests. We applied genetic clustering algorithms to analyse the population genetic structure of the urban boar. We used approximate Bayesian computation to infer the boar's colonization history of the city. Finally, we used assignment tests to determine the origin of wild boar hunted in the urban built-up areas. The animals in three urban forests formed distinct genetic clusters, with the remaining samples all being assigned to one rural population. One urban cluster was founded by individuals from another urban cluster rather than by rural immigrants. The wild boar that had been harvested within urban built-up areas was predominantly assigned to the rural cluster surrounding the urban area, rather than to one of the urban clusters. Synthesis and applications. Our results are likely to have an immediate impact on management strategies for urban wild board populations in Berlin, because they show that there are not only distinct urban clusters, but also ongoing source–sink dynamics between urban and rural areas. It is therefore essential that the neighbouring Federal States of Berlin and Brandenburg develop common hunting plans to control the wild boar population and reduce conflicts in urban areas.

71 citations

Journal ArticleDOI
TL;DR: The concept of the landscape of fear as discussed by the authors represents relative levels of predation risk as peaks and valleys that reflect the level of fear in different parts of its area of use, and is used to describe an animal's trade-off between access to food and predator avoidance on a spatial scale.
Abstract: 1 Introduction The landscape of fear describes an animal’s trade-off between access to food and predator avoidance on a spatial scale (Brown et al., 1999;Laundre J. W. et al., 2010;Laundre et al., 2014). The concept includes that the landscape of fear represents relative levels of predation risk as peaks and valleys that reflect the level of fear in different parts of its area of use (Laundre J. W. et al., 2010). Disturbance of wildlife by people is particularly frequent in urban environments and can exceed disturbance by natural predators. It therefore has the potential to shape prey behavior and should incite avoidance of such environments (Frid and Dill, 2002;Ciuti et al., 2012;Rosner et al., 2014;Stoen et al., 2015). The number of mammals living in urban environments increases (Bateman and Fleming, 2012;Magle et al., 2012). Hence, urban environments can support wildlife and provide various food sources: natural food (Stillfried et al., 2017b) or anthropogenic, easily accessible food (Cahill et al., 2012;Murray et al., 2015;Theimer et al., 2015;Tryjanowski et al., 2015), both of which can contain a high amount of energy (Ottoni et al., 2009;Maibeche et al., 2015). The urban landscape of fear should be worse than the rural one because the threat increases with human proximity per se, a high traffic volume and additional predators such as domestic dogs and other companion animals (Frid and Dill, 2002;Baker et alKinney, 2002;Lowry et al., 2013). Urban wildlife needs to perceive spatio-temporal variation in risk (Valeix et al., 2012). The urban landscape of fear should correspond to landscape features such as roads, because of vehicle and pedestrian traffic (Dowding et al., 2010;Bonnot et al., 2013;Lowry et al., 2013;Morelle et al., 2013;Murray and St Clair, 2015;Thurfjell et al., 2015;Gray et al., 2016), sealed built-up areas (= areas with a high density of housing (Bonnot et al., 2013;Magle et al., 2014;Beninde et al., 2015;Gray et al., 2016) and

70 citations

Journal ArticleDOI
TL;DR: These findings demonstrate that black bears are able to detect risky places and adjust their spatial movements accordingly, and can perceive changes in the level of risk from human hunting activities on a fine temporal scale.

62 citations

Journal ArticleDOI
TL;DR: The finding of PCV3 in both clusters suggests that the virus was introduced into the animal populations before Berlin was divided, and the methods used will be indispensable for screening for circoviruses in pigs genetically modified for xenotransplantation.
Abstract: Porcine circovirus 3 is a newly described circovirus circulating worldwide. PCV3 may play an etiologic role in different pig diseases. Two different genotypes of PCV3 were described, PCV3a and PCV3b. In order to analyse whether PCV3 is also present in wild boars, animals living in and near Berlin were studied. The animals had been analysed previously and were found to form two genetically distinct and geographically coherent clusters. To detect PCV3 in wild boars, a PCR was performed, to analyse the virus in detail, parts of the sequence of the capsid protein were sequenced. In addition, a screening for PCV1 and PCV2 was performed using PCR. For the first time, PCV3 was detected in German wild boars, with 50% of the animals infected in one genetic cluster, and 23% in the second cluster. In both populations which were divided in the years of division of Berlin, PCV3b was detected, in one case also PCV3a was detected. In some animals, co-infections with PCV1 and PCV2 or triple infections were detected. The data show a high prevalence of PCV3 and co-infections with PCV1 and PCV2 in German wild boars. The finding of PCV3 in both clusters suggests that the virus was introduced into the animal populations before Berlin was divided. Furthermore, the methods used will be indispensable for screening for circoviruses in pigs genetically modified for xenotransplantation.

42 citations


Cited by
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30 Apr 1984
TL;DR: A review of the literature on optimal foraging can be found in this article, with a focus on the theoretical developments and the data that permit tests of the predictions, and the authors conclude that the simple models so far formulated are supported by available data and that they are optimistic about the value both now and in the future.
Abstract: Beginning with Emlen (1966) and MacArthur and Pianka (1966) and extending through the last ten years, several authors have sought to predict the foraging behavior of animals by means of mathematical models. These models are very similar,in that they all assume that the fitness of a foraging animal is a function of the efficiency of foraging measured in terms of some "currency" (Schoener, 1971) -usually energy- and that natural selection has resulted in animals that forage so as to maximize this fitness. As a result of these similarities, the models have become known as "optimal foraging models"; and the theory that embodies them, "optimal foraging theory." The situations to which optimal foraging theory has been applied, with the exception of a few recent studies, can be divided into the following four categories: (1) choice by an animal of which food types to eat (i.e., optimal diet); (2) choice of which patch type to feed in (i.e., optimal patch choice); (3) optimal allocation of time to different patches; and (4) optimal patterns and speed of movements. In this review we discuss each of these categories separately, dealing with both the theoretical developments and the data that permit tests of the predictions. The review is selective in the sense that we emphasize studies that either develop testable predictions or that attempt to test predictions in a precise quantitative manner. We also discuss what we see to be some of the future developments in the area of optimal foraging theory and how this theory can be related to other areas of biology. Our general conclusion is that the simple models so far formulated are supported are supported reasonably well by available data and that we are optimistic about the value both now and in the future of optimal foraging theory. We argue, however, that these simple models will requre much modification, espicially to deal with situations that either cannot easily be put into one or another of the above four categories or entail currencies more complicated that just energy.

2,709 citations

Journal ArticleDOI
TL;DR: This work provides a worked example of spatial thinning of species occurrence records for the Caribbean spiny pocket mouse, where the results obtained match those of manual thinning.
Abstract: Spatial thinning of species occurrence records can help address problems associated with spatial sampling biases. Ideally, thinning removes the fewest records necessary to substantially reduce the effects of sampling bias, while simultaneously retaining the greatest amount of useful information. Spatial thinning can be done manually; however, this is prohibitively time consuming for large datasets. Using a randomization approach, the ‘thin’ function in the spThin R package returns a dataset with the maximum number of records for a given thinning distance, when run for sufficient iterations. We here provide a worked example for the Caribbean spiny pocket mouse, where the results obtained match those of manual thinning.

1,016 citations

Journal ArticleDOI
12 May 2014-PLOS ONE
TL;DR: The ability of methods to correct the initial sampling bias varied greatly depending on bias type, bias intensity and species, but the simple systematic sampling of records consistently ranked among the best performing across the range of conditions tested, whereas other methods performed more poorly in most cases.
Abstract: MAXENT is now a common species distribution modeling (SDM) tool used by conservation practitioners for predicting the distribution of a species from a set of records and environmental predictors. However, datasets of species occurrence used to train the model are often biased in the geographical space because of unequal sampling effort across the study area. This bias may be a source of strong inaccuracy in the resulting model and could lead to incorrect predictions. Although a number of sampling bias correction methods have been proposed, there is no consensual guideline to account for it. We compared here the performance of five methods of bias correction on three datasets of species occurrence: one “virtual” derived from a land cover map, and two actual datasets for a turtle (Chrysemys picta) and a salamander (Plethodon cylindraceus). We subjected these datasets to four types of sampling biases corresponding to potential types of empirical biases. We applied five correction methods to the biased samples and compared the outputs of distribution models to unbiased datasets to assess the overall correction performance of each method. The results revealed that the ability of methods to correct the initial sampling bias varied greatly depending on bias type, bias intensity and species. However, the simple systematic sampling of records consistently ranked among the best performing across the range of conditions tested, whereas other methods performed more poorly in most cases. The strong effect of initial conditions on correction performance highlights the need for further research to develop a step-by-step guideline to account for sampling bias. However, this method seems to be the most efficient in correcting sampling bias and should be advised in most cases.

775 citations

Journal ArticleDOI
Marlee A. Tucker1, Katrin Böhning-Gaese1, William F. Fagan2, John M. Fryxell3, Bram Van Moorter, Susan C. Alberts4, Abdullahi H. Ali, Andrew M. Allen5, Andrew M. Allen6, Nina Attias7, Tal Avgar8, Hattie L. A. Bartlam-Brooks9, Buuveibaatar Bayarbaatar10, Jerrold L. Belant11, Alessandra Bertassoni12, Dean E. Beyer13, Laura R. Bidner14, Floris M. van Beest15, Stephen Blake10, Stephen Blake16, Niels Blaum17, Chloe Bracis1, Danielle D. Brown18, P J Nico de Bruyn19, Francesca Cagnacci20, Francesca Cagnacci21, Justin M. Calabrese22, Justin M. Calabrese2, Constança Camilo-Alves23, Simon Chamaillé-Jammes24, André Chiaradia25, André Chiaradia26, Sarah C. Davidson16, Sarah C. Davidson27, Todd E. Dennis28, Stephen DeStefano29, Duane R. Diefenbach30, Iain Douglas-Hamilton31, Iain Douglas-Hamilton32, Julian Fennessy, Claudia Fichtel33, Wolfgang Fiedler16, Christina Fischer34, Ilya R. Fischhoff35, Christen H. Fleming2, Christen H. Fleming22, Adam T. Ford36, Susanne A. Fritz1, Benedikt Gehr37, Jacob R. Goheen38, Eliezer Gurarie2, Eliezer Gurarie39, Mark Hebblewhite40, Marco Heurich41, Marco Heurich42, A. J. Mark Hewison43, Christian Hof, Edward Hurme2, Lynne A. Isbell14, René Janssen, Florian Jeltsch17, Petra Kaczensky44, Adam Kane45, Peter M. Kappeler33, Matthew J. Kauffman38, Roland Kays46, Roland Kays47, Duncan M. Kimuyu48, Flávia Koch49, Flávia Koch33, Bart Kranstauber37, Scott D. LaPoint16, Scott D. LaPoint50, Peter Leimgruber22, John D. C. Linnell, Pascual López-López51, A. Catherine Markham52, Jenny Mattisson, Emília Patrícia Medici53, Ugo Mellone54, Evelyn H. Merrill8, Guilherme Miranda de Mourão55, Ronaldo Gonçalves Morato, Nicolas Morellet43, Thomas A. Morrison56, Samuel L. Díaz-Muñoz57, Samuel L. Díaz-Muñoz14, Atle Mysterud58, Dejid Nandintsetseg1, Ran Nathan59, Aidin Niamir, John Odden, Robert B. O'Hara60, Luiz Gustavo R. Oliveira-Santos7, Kirk A. Olson10, Bruce D. Patterson61, Rogério Cunha de Paula, Luca Pedrotti, Björn Reineking62, Björn Reineking63, Martin Rimmler, Tracey L. Rogers64, Christer Moe Rolandsen, Christopher S. Rosenberry65, Daniel I. Rubenstein66, Kamran Safi67, Kamran Safi16, Sonia Saïd, Nir Sapir68, Hall Sawyer, Niels Martin Schmidt15, Nuria Selva69, Agnieszka Sergiel69, Enkhtuvshin Shiilegdamba10, João P. Silva70, João P. Silva71, João P. Silva72, Navinder J. Singh6, Erling Johan Solberg, Orr Spiegel14, Olav Strand, Siva R. Sundaresan, Wiebke Ullmann17, Ulrich Voigt44, Jake Wall32, David W. Wattles29, Martin Wikelski67, Martin Wikelski16, Christopher C. Wilmers73, John W. Wilson74, George Wittemyer75, George Wittemyer32, Filip Zięba, Tomasz Zwijacz-Kozica, Thomas Mueller22, Thomas Mueller1 
Goethe University Frankfurt1, University of Maryland, College Park2, University of Guelph3, Duke University4, Radboud University Nijmegen5, Swedish University of Agricultural Sciences6, Federal University of Mato Grosso do Sul7, University of Alberta8, Royal Veterinary College9, Wildlife Conservation Society10, Mississippi State University11, Sao Paulo State University12, Michigan Department of Natural Resources13, University of California, Davis14, Aarhus University15, Max Planck Society16, University of Potsdam17, Middle Tennessee State University18, Mammal Research Institute19, Edmund Mach Foundation20, Harvard University21, Smithsonian Conservation Biology Institute22, University of Évora23, University of Montpellier24, Monash University25, Parks Victoria26, Ohio State University27, Fiji National University28, University of Massachusetts Amherst29, United States Geological Survey30, University of Oxford31, Save the Elephants32, German Primate Center33, Technische Universität München34, Institute of Ecosystem Studies35, University of British Columbia36, University of Zurich37, University of Wyoming38, University of Washington39, University of Montana40, Bavarian Forest National Park41, University of Freiburg42, University of Toulouse43, University of Veterinary Medicine Vienna44, University College Cork45, North Carolina Museum of Natural Sciences46, North Carolina State University47, Karatina University48, University of Lethbridge49, Lamont–Doherty Earth Observatory50, University of Valencia51, Stony Brook University52, International Union for Conservation of Nature and Natural Resources53, University of Alicante54, Empresa Brasileira de Pesquisa Agropecuária55, University of Glasgow56, New York University57, University of Oslo58, Hebrew University of Jerusalem59, Norwegian University of Science and Technology60, Field Museum of Natural History61, University of Grenoble62, University of Bayreuth63, University of New South Wales64, Pennsylvania Game Commission65, Princeton University66, University of Konstanz67, University of Haifa68, Polish Academy of Sciences69, University of Lisbon70, Instituto Superior de Agronomia71, University of Porto72, University of California, Santa Cruz73, University of Pretoria74, Colorado State University75
26 Jan 2018-Science
TL;DR: Using a unique GPS-tracking database of 803 individuals across 57 species, it is found that movements of mammals in areas with a comparatively high human footprint were on average one-half to one-third the extent of their movements in area with a low human footprint.
Abstract: Animal movement is fundamental for ecosystem functioning and species survival, yet the effects of the anthropogenic footprint on animal movements have not been estimated across species. Using a unique GPS-tracking database of 803 individuals across 57 species, we found that movements of mammals in areas with a comparatively high human footprint were on average one-half to one-third the extent of their movements in areas with a low human footprint. We attribute this reduction to behavioral changes of individual animals and to the exclusion of species with long-range movements from areas with higher human impact. Global loss of vagility alters a key ecological trait of animals that affects not only population persistence but also ecosystem processes such as predator-prey interactions, nutrient cycling, and disease transmission.

719 citations

Journal ArticleDOI
03 Nov 2017-Science
TL;DR: The suite of pressures that urban environments exert, the ways in which species may (or may not) adapt, and the larger impact of these evolutionary events on natural processes and human populations are reviewed.
Abstract: BACKGROUND The extent of urban areas is increasing around the world, and most humans now live in cities. Urbanization results in dramatic environmental change, including increased temperatures, more impervious surface cover, altered hydrology, and elevated pollution. Urban areas also host more non-native species and reduced abundance and diversity of many native species. These environmental changes brought by global urbanization are creating novel ecosystems with unknown consequences for the evolution of life. Here, we consider how early human settlements led to the evolution of human commensals, including some of the most notorious pests and disease vectors. We also comprehensively review how contemporary urbanization affects the evolution of species that coinhabit cities. ADVANCES A recent surge of research shows that urbanization affects both nonadaptive and adaptive evolution. Some of the clearest results of urban evolution show that cities elevate the strength of random genetic drift (stochastic changes in allele frequencies) and restrict gene flow (the movement of alleles between populations due to dispersal and mating). Populations of native species in cities often represent either relicts that predate urbanization or populations that established after a city formed. Both scenarios frequently result in a loss of genetic diversity within populations and increased differentiation between populations. Fragmentation and urban infrastructure also create barriers to dispersal, and consequently, gene flow is often reduced among city populations, which further contributes to genetic differentiation between populations. The influence of urbanization on mutation and adaptive evolution are less clear. A small number of studies suggest that industrial pollution can elevate mutation rates, but the pervasiveness of this effect is unknown. A better studied phenomenon are the effects of urbanization on evolution by natural selection. A growing number of studies show that plant and animal populations experience divergent selection between urban and nonurban environments. This divergent selection has led to adaptive evolution in life history, morphology, physiology, behavior, and reproductive traits. These adaptations typically evolve in response to pesticide use, pollution, local climate, or the physical structure of cities. Despite these important results, the genetic basis of adaptive evolution is known from only a few cases. Most studies also examine only a few populations in one city, and experimental validation is rare. OUTLOOK The study of evolution in urban areas provides insights into both fundamental and applied problems in biology. The thousands of cities throughout the world share some features while differing in other aspects related to their age, historical context, governmental policies, and local climate. Thus, the phenomenon of global urbanization represents an unintended but highly replicated global study of experimental evolution. We can harness this global urban experiment to understand the repeatability and pace of evolution in response to human activity. Among the most important unresolved questions is, how often do native and exotic species adapt to the particular environmental challenges found in cities? Such adaptations could be the difference as to whether a species persists or vanishes from urban areas. In this way, the study of urban evolution can help us understand how evolution in populations may contribute to conservation of rare species, and how populations can be managed to facilitate the establishment of resilient and sustainable urban ecosystems. In a similar way, understanding evolution in urban areas can lead to improved human health. For example, human pests frequently adapt to pesticides and evade control efforts because of our limited understanding of the size of populations and movement of individuals. Applied evolutionary studies could lead to more effective mitigation of pests and disease agents. The study of urban evolution has rapidly become an important frontier in biology, with implications for healthy and sustainable human populations in urban ecosystems.

568 citations