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Jerrold L. Belant

Bio: Jerrold L. Belant is an academic researcher from State University of New York College of Environmental Science and Forestry. The author has contributed to research in topics: Population & Ursus. The author has an hindex of 34, co-authored 311 publications receiving 5879 citations. Previous affiliations of Jerrold L. Belant include State University of New York System & University of Alaska Fairbanks.
Topics: Population, Ursus, Odocoileus, Ecology, Carnivore


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
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 Blake16, Stephen Blake10, Niels Blaum17, Chloe Bracis1, Danielle D. Brown18, P J Nico de Bruyn19, Francesca Cagnacci20, Francesca Cagnacci21, Justin M. Calabrese2, Justin M. Calabrese22, 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. Fleming22, Christen H. Fleming2, Adam T. Ford36, Susanne A. Fritz1, Benedikt Gehr37, Jacob R. Goheen38, Eliezer Gurarie39, Eliezer Gurarie2, 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. LaPoint50, Scott D. LaPoint16, 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-Muñoz14, Samuel L. Díaz-Muñoz57, 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 Wittemyer32, George Wittemyer75, 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, Parks Victoria25, Monash University26, 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, University of Freiburg41, Bavarian Forest National Park42, University of Toulouse43, University of Veterinary Medicine Vienna44, University College Cork45, North Carolina State University46, North Carolina Museum of Natural Sciences47, 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 Bayreuth62, University of Grenoble63, University of New South Wales64, Pennsylvania Game Commission65, Princeton University66, University of Konstanz67, University of Haifa68, Polish Academy of Sciences69, University of Lisbon70, University of Porto71, Instituto Superior de Agronomia72, 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
TL;DR: A recent complete assessment of the conservation status of 5487 mammal species demonstrated that at least one-fifth of the species are at risk of extinction in the wild and that the rate of deterioration has been most accelerated in the Indomalayan and Australasian realms as discussed by the authors.
Abstract: A recent complete assessment of the conservation status of 5487 mammal species demonstrated that at least one-fifth are at risk of extinction in the wild. We retrospectively identified genuine changes in extinction risk for mammals between 1996 and 2008 to calculate changes in the International Union for Conservation of Nature (IUCN) Red List Index (RLI). Species-level trends in the conservation status of mammalian diversity reveal that extinction risk in large-bodied species is increasing, and that the rate of deterioration has been most accelerated in the Indomalayan and Australasian realms. Expanding agriculture and hunting have been the main drivers of increased extinction risk in mammals. Site-based protection and management, legislation, and captive-breeding and reintroduction programmes have led to improvements in 24 species. We contextualize these changes, and explain why both deteriorations and improvements may be under-reported. Although this study highlights where conservation actions are leading to improvements, it fails to account for instances where conservation has prevented further deteriorations in the status of the world's mammals. The continued utility of the RLI is dependent on sustained investment to ensure repeated assessments of mammals over time and to facilitate future calculations of the RLI and measurement against global targets.

179 citations

Journal ArticleDOI
23 Apr 2012-PLOS ONE
TL;DR: This study analysed black bear data collected with 123 hair snares with an SCR model accounting for differences in detection and movement between sexes and across the trapping occasions and found that results were similar across trap arrays, except when only 20% of the array was used.
Abstract: When estimating population density from data collected on non-invasive detector arrays, recently developed spatial capture-recapture (SCR) models present an advance over non-spatial models by accounting for individual movement. While these models should be more robust to changes in trapping designs, they have not been well tested. Here we investigate how the spatial arrangement and size of the trapping array influence parameter estimates for SCR models. We analysed black bear data collected with 123 hair snares with an SCR model accounting for differences in detection and movement between sexes and across the trapping occasions. To see how the size of the trap array and trap dispersion influence parameter estimates, we repeated analysis for data from subsets of traps: 50% chosen at random, 50% in the centre of the array and 20% in the South of the array. Additionally, we simulated and analysed data under a suite of trap designs and home range sizes. In the black bear study, we found that results were similar across trap arrays, except when only 20% of the array was used. Black bear density was approximately 10 individuals per 100 km2. Our simulation study showed that SCR models performed well as long as the extent of the trap array was similar to or larger than the extent of individual movement during the study period, and movement was at least half the distance between traps. SCR models performed well across a range of spatial trap setups and animal movements. Contrary to non-spatial capture-recapture models, they do not require the trapping grid to cover an area several times the average home range of the studied species. This renders SCR models more appropriate for the study of wide-ranging mammals and more flexible to design studies targeting multiple species.

173 citations

Journal ArticleDOI
TL;DR: In this paper, an integrated, landscape-level management approach is proposed to ensure an overall reduction in conflict between gulls and people in urban environments, which is necessary to ensure the overall reduction of conflict between birds and humans in urban areas.

173 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: Given the influence of deer on other organisms and natural processes, ecologists should actively participate in efforts to understand, monitor, and reduce the impact of deer in natural ecosystems.
Abstract: ▪ Abstract Deer have expanded their range and increased dramatically in abundance worldwide in recent decades. They inflict major economic losses in forestry, agriculture, and transportation and contribute to the transmission of several animal and human diseases. Their impact on natural ecosystems is also dramatic but less quantified. By foraging selectively, deer affect the growth and survival of many herb, shrub, and tree species, modifying patterns of relative abundance and vegetation dynamics. Cascading effects on other species extend to insects, birds, and other mammals. In forests, sustained overbrowsing reduces plant cover and diversity, alters nutrient and carbon cycling, and redirects succession to shift future overstory composition. Many of these simplified alternative states appear to be stable and difficult to reverse. Given the influence of deer on other organisms and natural processes, ecologists should actively participate in efforts to understand, monitor, and reduce the impact of deer on ...

1,779 citations

Journal ArticleDOI
TL;DR: The following tables highlight daily diet dry matter and nutrient density requirements for diffferent classes of cattle at various stages of production based on the National Research Council’s Nutrient Requirements of Beef Cattle.

1,123 citations

Journal ArticleDOI
TL;DR: The goals for this review are to lay the groundwork on supporting services to facilitate future efforts to estimate their economic value, to highlight gaps in knowledge, and to point to future directions for additional research.
Abstract: Ecosystem services are natural processes that benefit humans. Birds contribute the four types of services recognized by the UN Millennium Ecosystem Assessment-provisioning, regulating, cultural, and supporting services. In this review, we concentrate primarily on supporting services, and to a lesser extent, provisioning and regulating services. As members of ecosystems, birds play many roles, including as predators, pollinators, scavengers, seed dispersers, seed predators, and ecosystem engineers. These ecosystem services fall into two subcategories: those that arise via behavior (like consumption of agricultural pests) and those that arise via bird products (like nests and guano). Characteristics of most birds make them quite special from the perspective of ecosystem services. Because most birds fly, they can respond to irruptive or pulsed resources in ways generally not possible for other vertebrates. Migratory species link ecosystem processes and fluxes that are separated by great distances and times. Although the economic value to humans contributed by most, if not all, of the supporting services has yet to be quantified, we believe they are important to humans. Our goals for this review are 1) to lay the groundwork on these services to facilitate future efforts to estimate their economic value, 2) to highlight gaps in our knowledge, and 3) to point to future directions for additional research.

1,051 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