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Todd K. Fuller

Bio: Todd K. Fuller is an academic researcher from University of Massachusetts Amherst. The author has contributed to research in topics: Population & Predation. The author has an hindex of 40, co-authored 141 publications receiving 6942 citations.


Papers
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Journal ArticleDOI
TL;DR: In this article, the authors examined movements of two highly mobile carnivores across the Ventura Freeway near Los Angeles, one of the busiest highways in the United States, and found that carnivores can cross the freeway and that 5-32% of sampled carnivores crossed over a 7-year period.
Abstract: Roads present formidable barriers to dispersal. We examine movements of two highly mobile carnivores across the Ventura Freeway near Los Angeles, one of the busiest highways in the United States. The two species, bobcats and coyotes, can disappear from habitats isolated and fragmented by roads, and their ability to disperse across the Ventura Freeway tests the limits of vertebrates to overcome anthropogenic obstacles. We combine radiotelemetry data and genetically based assignments to identify individuals that have crossed the freeway. Although the freeway is a significant barrier to dispersal, we find that carnivores can cross the freeway and that 5–32% of sampled carnivores crossed over a 7-year period. However, despite moderate levels of migration, populations on either side of the freeway are genetically differentiated, and coalescent modelling shows their genetic isolation is consistent with a migration fraction less than 0.5% per generation. These results imply that individuals that cross the freeway rarely reproduce. Highways and development impose artificial home range boundaries on territorial and reproductive individuals and hence decrease genetically effective migration. Further, territory pile-up at freeway boundaries may decrease reproductive opportunities for dispersing individuals that do manage to cross. Consequently, freeways are filters favouring dispersing individuals that add to the migration rate but little to gene flow. Our results demonstrate that freeways can restrict gene flow even in wide-ranging species and suggest that for territorial animals, migration levels across anthropogenic barriers need to be an order of magnitude larger than commonly assumed to counteract genetic differentiation.

556 citations

Journal ArticleDOI
TL;DR: Methods for estimating survival and cause-specific mortality rates from radiomarked animals from telemetry data are presented and potential biases arising from combining data from several individuals marked at different times within an interval or from combining rates from different intervals are identified.
Abstract: Methods are presented for estimating survival and cause-specific mortality rates from radiomarked animals. Time is partitioned into intervals during which the daily rates are assumed to be constant. The rates are estimated from the number of transmitter-days, the number of mortalities due to particular causes, and the number of days in the time intervals. Potential biases arising from combining data from several individuals marked at different times within an interval or from combining rates from different intervals are identified. Variances and confidence intervals for the estimators are presented. Hypothesis testing and sample-size considerations are also illustrated. Simulation showed that the influence of errors in date of death was small, but misdiagnosis of fate had serious consequences. A microcomputer program is available for performing the analyses. J. WILDL. MANAGE. 49(3):668-674 Important mortality agents for wildlife species are legal hunting, wounding loss, poaching, predation, weather, accidents, and disease. Most mark-and-recapture techniques allow investigators to, at most, partition mortality into hunting and "other" sources (Anderson 1975). Radiotelemetry techniques should enable the importance of cause-specific mortality factors to be determined because tagged animals can be located soon after death and the agent of mortality ascertained, regardless of the cause of death. In most radiotelemetry studies, however, the importance of a mortality factor is given as the number of deaths caused by the agent expressed as a percent (Dumke and Pils 1973, Trent and Rongstad 1974, Brand et al. 1975, Trainer et al. 1981). If a sample of animals is marked at the start of a period of interest, calculation of survival and cause-specific mortality rates as simple percentages is appropriate (Hessler et al. 1970). But more often, animals are radiomarked at more than one time, even during different periods within a year for which survival rates differ, and this may lead to two serious biases. First, when some animals are marked midway through a period, animals that died early are not available for sampling and this biases the observed survival rate upwards. And second, if daily survival rates are not constant between intervals, intervals within the period that have the largest sample sizes are most influential in determining the estimates. Methods that avoid these biases with regard to survival rates were first adapted by wildlife biologists to estimate nesting success (Mayfield 1961, 1975; Miller and Johnson 1978; Johnson 1979; Bart and Robson 1982) and have been This content downloaded from 157.55.39.35 on Tue, 30 Aug 2016 05:07:11 UTC All use subject to http://about.jstor.org/terms J. Wildl. Manage. 49(3):1985 SURVIVAL-MORTALITY RATES * Heisey and Fuller 669 Table 1. Definition of variables used in models to determine survival rates and cause-specific mortality rates from telemetry data. i = Interval number (i = 1, 2, ... , I) j = Cause of mortality (j = 1, 2, . , J) xi = Total number of transmitter-days during interval i yi = Total number of mortalities occurring during interval i yij = Total number of mortalities occurring during interval i due to cause j Li = Total number of days in interval i si = Daily survival rate during interval i Si = Survival rate for entire interval i S* = Survival rate for all I intervals mij = Daily mortality rate in interval i due to source j Mij = Interval mortality rate for entire interval i due to source j Mj* = Mortality rate for all I intervals due to source j applied to survival rates in wildlife radiotelemetry studies (Gilmer et al. 1974, Trent and Rongstad 1974, Brand et al. 1975, Trainer et al. 1981). Agent-specific mortality rates in telemetry studies have been evaluated only as simple percentages that are subject to the aforementioned biases. In this paper, we generalize the basic approach of Mayfield (1961, 1975) and Trent and Rongstad (1974) to determine unbiased estimates of cause-specific mortality rates. Standard statistical methodology for competing risk analysis is used (Chiang 1968, Kalbfleisch and Prentice 1980). Radiotelemetry applications are emphasized, although the techniques are applicable to any situation where the subjects can be relocated at will and the cause of death identified. A microcomputer program called MICROMORT is available which performs most of the described calculations for moderate size problems. Versions are available for the IBM PC. A diskette containing the program and documentation can be requested from T. K. Fuller. We are grateful to J. R. Cary, D. H. Johnson, and G. C. White for their constructive comments and encouragement. E. K. Fritzell, J. M. Hoenig, D. W. Kuehn, and R. E. Lake also made helpful suggestions. Reprint requests should be made to Todd K. Fuller.

518 citations

Book
01 Jan 2002
TL;DR: A critical review of the effects of marking on the biology of Vertebrates can be found in this article, with a focus on the effect of Marking on the behavior of animals.
Abstract: 1. Hypothesis Testing in Ecology, by Charles J. Krebs2: A Critical Review of the Effects of Marking on the Biology of Vertebrates, by Dennis L. Murray and Mark R. Fuller3. Animal Home Ranges and Territories and Home Range Estimators, by Roger A. Powell4. Delusions in Habitat Evaluation: Measuring Use, Selection, and Importance, by David L. Garshelis5. Investigating Food Habits of Terrestrial Vertebrates, by John A. Litvaitis6. Detecting Stability and Causes of Change in Population Density, by Joseph S. Elkinton7. Monitoring Populations, by James P. Gibbs8. Modeling Predator--Prey Dynamics, by Mark S. Boyce9. Population Viability Analysis: Data Requirements and Essential Analyses, by Gary C. White10. Measuring the Dynamics of Mammalian Societies: An Ecologist's Guide to Ethological Methods, by David W. Macdonald, Paul D. Stewart, Pavel Stopka, and Nobuyuki Yamaguchi11. Modeling Species Distribution with GIS, by Fabio Corsi, Jan de Leeuw, and Andrew K. Skidmore

451 citations

Journal ArticleDOI
TL;DR: In this paper, the authors studied the ecology and behavior of domestic cats and coyotes relative to development in a fragmented landscape in southern California from 1996 to 2000, and determined home ranges for 35 bobcats and 40 coyotes, and measured their exposure to development (urban association) as the percentage of each home range composed of developed or modified areas.
Abstract: Urbanization and habitat fragmentation are major threats to wildlife populations, especially mammalian carnivores. We studied the ecology and behavior of bobcats ( Lynx rufus ) and coyotes ( Canis latrans ) relative to development in a fragmented landscape in southern California from 1996 to 2000. We captured and radiocollared 50 bobcats and 86 coyotes, determined home ranges for 35 bobcats and 40 coyotes, and measured their exposure to development ( “urban association” ) as the percentage of each home range composed of developed or modified areas. Both species occupied predominantly natural home ranges. Adult female bobcats had low levels of urban association, significantly lower than coyotes, adult male bobcats, and young female bobcats. Home-range size was positively correlated with urban association for coyotes and adult male and young female bobcats, suggesting that human-dominated areas were less suitable than natural areas in some important way. Animals more associated with non-natural areas had higher levels of night activity, and both bobcats and coyotes were more likely to be in developed areas at night than during the day. Survival rates were relatively high and were not related to urban association, at least for animals>6–9 months of age. Mortality rates from human-related causes such as vehicle collisions and incidental poisoning were also independent of urban association. In this region, even the few animals that had almost no human development within their home range were vulnerable to human-related mortality. Carnivore conservation in urban landscapes must account for these mortality sources that influence the entire landscape, including reserves. For bobcats, preserving open space of sufficient quantity and quality for adult females is necessary for population viability. Educating local residents about carnivores is also critical for conserving populations in urban areas.

434 citations

Journal ArticleDOI
TL;DR: It is proposed that coyotes limit the number and distribution of gray foxes in Santa Monica Mountains, and that those two carnivores exemplified a case in which the relationship between their body size and local abundance is governed by competitive dominance of the largest species rather than by energetic equivalences.
Abstract: We examined the relative roles of dominance in agonistic interactions and energetic constraints related to body size in determining local abundances of coyotes (Canis latrans, 8–20 kg), gray foxes (Urocyon cinereoargenteus, 3–5 kg) and bobcats (Felis rufus, 5–15 kg) at three study sites (hereafter referred to as NP, CP, and SP) in the Santa Monica Mountains of California. We hypothesized that the largest and behaviorally dominant species, the coyote, would exploit a wider range of resources (i.e., a higher number of habitat and/or food types) and, consequently, would occur in higher density than the other two carnivores. We evaluated our hypotheses by quantifying their diets, food overlap, habitat-specific abundances, as well as their overall relative abundance at the three study sites. We identified behavioral dominance of coyotes over foxes and bobcats in Santa Monica because 7 of 12 recorded gray fox deaths and 2 of 5 recorded bobcat deaths were due to coyote predation, and no coyotes died as a result of their interactions with bobcats or foxes. Coyotes and bobcats were present in a variety of habitats types (8 out of 9), including both open and brushy habitats, whereas gray foxes were chiefly restricted to brushy habitats. There was a negative relationship between the abundances of coyotes and gray foxes (P=0.020) across habitats, suggesting that foxes avoided habitats of high coyote predation risk. Coyote abundance was low in NP, high in CP, and intermediate in SP. Bobcat abundance changed little across study sites, and gray foxes were very abundant in NP, absent in CP, and scarce in SP; this suggests a negative relationship between coyote and fox abundances across study sites, as well. Bobcats were solely carnivorous, relying on small mammals (lagomorphs and rodents) throughout the year and at all three sites. Coyotes and gray foxes also relied on small mammals year-round at all sites, though they also ate significant amounts of fruit. Though there were strong overall interspecific differences in food habits of carnivores (P<0.0001), average seasonal food overlaps were high due to the importance of small mammals in all carnivore diets [bobcat-gray fox: 0.79±0.09 (SD), n=4; bobcat-coyote: 0.69±0.16, n=6; coyote-gray fox: 0.52±0.05, n=4]. As hypothesized, coyotes used more food types and more habitat types than did bobcats and gray foxes and, overall, coyotes were the most abundant of the three species and ranged more widely than did gray foxes. We propose that coyotes limit the number and distribution of gray foxes in Santa Monica Mountains, and that those two carnivores exemplified a case in which the relationship between their body size and local abundance is governed by competitive dominance of the largest species rather than by energetic equivalences. However, in the case of the intermediate-sized bobcat no such a pattern emerged, likely due to rarity or inconsistency of agonistic interactions and/or behavioral avoidance of encounters by subordinate species.

371 citations


Cited by
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Journal ArticleDOI
TL;DR: The American Society of Mammalogists (ASM) published guidelines for the use of wild mammal species in research as mentioned in this paper, which provide a broad and comprehensive understanding of the biology of nondomesticated mammals in their natural environments.
Abstract: General guidelines for use of wild mammal species are updated from the 1998 version approved by the American Society of Mammalogists (ASM) and expanded to include additional resources. Included are details on marking, housing, trapping, and collecting mammals. These guidelines cover current professional techniques and regulations involving mammals used in research. Institutional animal care and use committees, regulatory agencies, and investigators should review and approve procedures concerning use of vertebrates at any particular institution. These guidelines were prepared and approved by the ASM, whose collective expertise provides a broad and comprehensive understanding of the biology of nondomesticated mammals in their natural environments.

3,979 citations

Journal ArticleDOI
TL;DR: A form of k -fold cross validation for evaluating prediction success is proposed for presence/available RSF models, which involves calculating the correlation between RSF ranks and area-adjusted frequencies for a withheld sub-sample of data.

2,107 citations

Journal ArticleDOI
TL;DR: Maxent was the most capable of the four modeling methods in producing useful results with sample sizes as small as 5, 10 and 25 occurrences, a result that should encourage conservationists to add distribution modeling to their toolbox.
Abstract: Species distribution models should provide conservation practioners with estimates of the spatial distributions of species requiring attention. These species are often rare and have limited known occurrences, posing challenges for creating accurate species distribution models. We tested four modeling methods (Bioclim, Domain, GARP, and Maxent) across 18 species with different levels of ecological specialization using six different sample size treatments and three different evaluation measures. Our assessment revealed that Maxent was the most capable of the four modeling methods in producing useful results with sample sizes as small as 5, 10 and 25 occurrences. The other methods compensated reasonably well (Domain and GARP) to poorly (Bioclim) when presented with datasets of small sample sizes. We show that multiple evaluation measures are necessary to determine accuracy of models produced with presence-only data. Further, we found that accuracy of models is greater for species with small geographic ranges and limited environmental tolerance, ecological characteristics of many rare species. Our results indicate that reasonable models can be made for some rare species, a result that should encourage conservationists to add distribution modeling to their toolbox.

2,046 citations

Journal ArticleDOI
TL;DR: Guidelines for use of wild mammal species in research are updated from Sikes et al. (2011), and include details on capturing, marking, housing, and humanely killing wild mammals.
Abstract: Guidelines for use of wild mammal species in research are updated from [Sikes et al. (2011)][1]. These guidelines cover current professional techniques and regulations involving the use of mammals in research and teaching; they also incorporate new resources, procedural summaries, and reporting requirements. Included are details on capturing, marking, housing, and humanely killing wild mammals. It is recommended that Institutional Animal Care and Use Committees (IACUCs), regulatory agencies, and investigators use these guidelines as a resource for protocols involving wild mammals, whether studied in the field or in captivity. These guidelines were prepared and approved by the American Society of Mammalogists (ASM), in consultation with professional veterinarians experienced in wildlife research and IACUCs, whose collective expertise provides a broad and comprehensive understanding of the biology of nondomesticated mammals. The current version of these guidelines and any subsequent modifications are available online on the Animal Care and Use Committee page of the ASM website ( ). Additional resources pertaining to the use of wild animals in research are available at: . Resumen Los lineamientos para el uso de especies de mamiferos de vida silvestre en la investigacion con base en [Sikes et al. (2011)][1] se actualizaron. Dichos lineamientos cubren tecnicas y regulaciones profesionales actuales que involucran el uso de mamiferos en la investigacion y ensenanza; tambien incorporan recursos nuevos, resumenes de procedimientos y requisitos para reportes. Se incluyen detalles acerca de captura, marcaje, manutencion en cautiverio y eutanasia de mamiferos de vida silvestre. Se recomienda que los comites institucionales de uso y cuidado animal (cifras en ingles: IACUCs), las agencias reguladoras y los investigadores se adhieran a dichos lineamientos como fuente base de protocolos que involucren mamiferos de vida silvestre, ya sea investigaciones de campo o en cautiverio. Dichos lineamientos fueron preparados y aprobados por la ASM, en consulta con profesionales veterinarios experimentados en investigaciones de vida silvestre y IACUCS, de quienes cuya experiencia colectiva provee un entendimiento amplio y exhaustivo de la biologia de mamiferos no-domesticados. La presente version de los lineamientos y modificaciones posteriores estan disponibles en linea en la pagina web de la ASM, bajo Cuidado Animal y Comite de Uso: ( ). Recursos adicionales relacionados con el uso de animales de vida silvestre para la investigacion se encuentran disponibles en ( ). [1]: #ref-69

1,728 citations

Journal ArticleDOI
TL;DR: The author guides the reader in about 350 pages from descriptive and basic statistical methods over classification and clustering to (generalised) linear and mixed models to enable researchers and students alike to reproduce the analyses and learn by doing.
Abstract: The complete title of this book runs ‘Analyzing Linguistic Data: A Practical Introduction to Statistics using R’ and as such it very well reflects the purpose and spirit of the book. The author guides the reader in about 350 pages from descriptive and basic statistical methods over classification and clustering to (generalised) linear and mixed models. Each of the methods is introduced in the context of concrete linguistic problems and demonstrated on exciting datasets from current research in the language sciences. In line with its practical orientation, the book focuses primarily on using the methods and interpreting the results. This implies that the mathematical treatment of the techniques is held at a minimum if not absent from the book. In return, the reader is provided with very detailed explanations on how to conduct the analyses using R [1]. The first chapter sets the tone being a 20-page introduction to R. For this and all subsequent chapters, the R code is intertwined with the chapter text and the datasets and functions used are conveniently packaged in the languageR package that is available on the Comprehensive R Archive Network (CRAN). With this approach, the author has done an excellent job in enabling researchers and students alike to reproduce the analyses and learn by doing. Another quality as a textbook is the fact that every chapter ends with Workbook sections where the user is invited to exercise his or her analysis skills on supplemental datasets. Full solutions including code, results and comments are given in Appendix A (30 pages). Instructors are therefore very well served by this text, although they might want to balance the book with some more mathematical treatment depending on the target audience. After the introductory chapter on R, the book opens on graphical data exploration. Chapter 3 treats probability distributions and common sampling distributions. Under basic statistical methods (Chapter 4), distribution tests and tests on means and variances are covered. Chapter 5 deals with clustering and classification. Strangely enough, the clustering section has material on PCA, factor analysis, correspondence analysis and includes only one subsection on clustering, devoted notably to hierarchical partitioning methods. The classification part deals with decision trees, discriminant analysis and support vector machines. The regression chapter (Chapter 6) treats linear models, generalised linear models, piecewise linear models and a substantial section on models for lexical richness. The final chapter on mixed models is particularly interesting as it is one of the few text book accounts that introduce the reader to using the (innovative) lme4 package of Douglas Bates which implements linear mixed-effects models. Moreover, the case studies included in this

1,679 citations