Elsevier

Biological Conservation

Volume 86, Issue 2, November 1998, Pages 243-255
Biological Conservation

Predictors of avian and mammalian translocation success: reanalysis with phylogenetically independent contrasts

https://doi.org/10.1016/S0006-3207(97)00179-1Get rights and content

Abstract

We use the phylogenetically based statistical method of independent contrasts to reanalyze the Wolf et al., 1996translocation data set for 181 programs involving 17 mammalian and 28 avian species. Although still novel in conservation and wildlife biology, the incorporation of phylogenetic information into analyses of interspecific comparative data is widely accepted and routinely used in several fields. To facilitate application of independent contrasts, we converted the dichotomous (success/failure) dependent variable (Wolf et al., 1996, Griffith et al., 1989. Translocations as a species conservation tool: status and strategy. Science 245, 477–480) into a more descriptive, continuous variable with the incorporation of persistence of the translocated population beyond the last release year, relative to the species' longevity. For comparison, we present three models: nonphylogenetic multiple logistic regression with the dichotomous dependent variable (the method used by Wolf et al. 1996and Griffith et al. 1989), nonphylogenetic multiple regression with the continuous dependent variable, and multiple regression using phylogenetically independent contrasts with the continuous dependent variable. Results of the phylogenetically based multiple regression analysis indicate statistical significance of three independent variables: habitat quality of the release area, range of the release site relative to the historical distribution of the translocated species, and number of individuals released. Evidence that omnivorous species are more successful than either herbivores or carnivores is also presented. The results of our reanalysis support several of the more important conclusions of the Wolf et al. (1996)and Griffith et al. (1989)studies and increase our confidence that the foregoing variables should be considered carefully when designing a translocation program. However, the phylogenetically based analysis does not support either the Wolf et al. (1996)or Griffith et al. (1989)findings with respect to the statistical significance of taxonomic class (bird vs mammal) and status (game vs threatened, endangered, or sensitive), or the Griffith et al. (1989)findings with respect to the significance of reproductive potential of the species and program length.

Introduction

Numerous methodological, environmental, species-specific, and population-level factors influence whether the intentional release of wild-caught or captive-reared animals into new locations will result in the successful establishment, re-establishment, or augmentation of a wild population. Because specific causal factors and their relative importance vary widely among such release programs (herein referred to as translocations, following Griffith et al., 1989; see also Wolf et al., 1996), it is difficult to identify general trends associated with success. Nevertheless, both theoretical considerations and empirical evidence suggest that some methodological and biological factors are of general importance. For example, such methodological details as raise and release procedures, number and composition of animals released, and choice of source stock for the released animals were shown to influence translocation outcomes in single-species studies (Beck et al., 1991, Allen et al., 1993, Reed et al., 1993, Bright and Morris, 1994, Veltman et al., 1996). Environmental factors perceived as important to success include general habitat quality and climatic conditions (Lindenmayer, 1994, Veitch, 1994), as well as the absence of predators or competitors (Crawley, 1986, Short et al., 1992). Some species-specific and population characteristics considered favorable for successful invasion of a new location include a relatively high reproductive potential, an omnivorous diet, small body mass, and high genetic diversity (Mayr, 1965Laycock, 1966Berger, 1972Smith et al., 1976Crawley, 1986Ehrlich, 1986O'Connor, 1986). The question remains: how universal is the influence of such methodological and biological factors? Also, in light of substantial case-by-case variations, are generalizations across a range of taxa possible?

Griffith et al. (1989)(see also Griffith et al., 1990, Griffith et al., 1993) used a comparative approach to test for general patterns underlying the success vs failure of translocations among species of birds and mammals. They conducted surveys of translocation programs throughout North America, Australia, and New Zealand; coded the outcomes as either a success (reported establishment of a self-sustaining population), a failure, or incomplete; and used multiple logistic regression to identify seven statistically significant predictors of success: (1) taxonomic class (bird vs mammal), (2) legal status of the translocated species (native game vs threatened, endangered, or sensitive species), (3) habitat quality of the release area (excellent, good, or fair/poor), (4) location of the release area relative to the historical range of the species (core vs periphery or outside), (5) number of animals released (log10 transformed), (6) program length (number of years over which releases occurred), and (7) potential productivity of the translocated species (high vs low). Wolf et al. (1996)conducted a follow-up survey, in which they up-dated the status of the translocations in the Griffith et al. study, increased the number of programs available for multiple regression analyses from 155 to 181, and tested additional variables as predictors of success. Their results, using comparable analyses, were largely consistent with the findings of Griffith et al. (1989). Wolf et al. (1996)produced a model which contained the first five of the Griffith et al. variables (as listed earlier) plus adult diet of the species in the wild (herbivorous vs omnivorous vs carnivorous).

As was abundantly demonstrated in the last decade, interspecific comparisons are potentially compromised by statistical non-independence of species values (Felsenstein, 1985, Harvey and Pagel, 1991, Garland et al., 1993, Martins and Hansen, 1996). A simplified consensus view of the problem can be summarized as follows. Species are related to greater or lesser degrees, as indicated by their phylogenetic (evolutionary) relationships. Closely related species possess many characteristics, and sometimes much of their selective regime, that were inherited from common ancestors. As a result of such inheritance, data for a series of species may contain hierarchical resemblances (e.g. snakes look like snakes, elephants look like elephants) and cannot be assumed to represent independent data points, a key assumption of both traditional parametric and nonparametric statistical methods. Detailed discussions of why closely related species tend to be similar are available elsewhere (Grafen, 1989, Brooks and McLennan, 1991, Harvey and Pagel, 1991, Eggleton and Vane-Wright, 1994, Martins, 1996a). In general, common effects of violating the assumption of independence include an inflation of Type I error rates when hypothesis testing (claiming statistical significance when none actually exists) and poor estimation of relationships among variables (and hence diminished predictive accuracy). The reality of these adverse statistical effects was demonstrated both analytically and by computer simulation studies (Felsenstein, 1985, Grafen, 1989, Martins and Garland, 1991, Garland et al., 1992, Garland et al., 1993, Pagel, 1993, Purvis et al., 1994, Dı́az-Uriarte and Garland, 1996Dı́az-Uriarte and Garland, in pressMartins, 1996b).

Inclusion of phylogenetic information in statistical analyses is now routine in such fields as behavioral and physiological ecology (Miles and Dunham, 1993, Garland and Carter, 1994, Losos and Miles, 1994, Dı́az et al., 1996, Martin and Clobert, 1996, Martins, 1996a, Reynolds and Lee, 1996, Ricklefs and Starck, 1996, Williams, 1996, Abouheif and Fairbairn, 1997, Bauwens and Dı́az-Uriarte, 1997, Price, 1997Clobert et al., in press). Although an historical, evolutionary approach may not initially seem germane to questions pertaining to contemporary wildlife translocation success, all interspecific (and many interpopulation) comparisons are potentially subject to phylogenetic influence in statistical tests. To illustrate, one might expect a translocation of bighorn sheep in Nevada to share inherent similarities with a translocation of bighorn sheep conducted elsewhere. Part of this similarity would be caused by general capture and release methodology, whereas part of this similarity would also be caused by biological characteristics of bighorn sheep (e.g. reproductive potential, social systems, disease susceptibility). Likewise, one might expect similarities between translocations conducted with closely related species (e.g. Prairie Chicken and Sharp-tailed Grouse), as compared with translocations involving more distantly related species (e.g. Canada Goose).

Although comparative biologists have long recognized that species should not be treated as independent data points, early proposed solutions to this problem, such as averaging within genera or choosing only one species per genus, are inadequate because they ignore potentially informative variation within phylogenetic lineages (clades), ignore hierarchical relationships among clades, and diminish statistical power (review in Harvey and Pagel, 1991). State-of-the-art phylogenetically based analyses allow incorporation of phylogenetic information without loss of statistical power (Martins and Garland, 1991Garland and Adolph, 1994Purvis et al., 1994Martins, 1996b).

The present study, therefore, employs phylogenetically based statistical methods to reanalyze the Wolf et al. (1996)data set (104 avian and 77 mammalian translocation programs, representing 28 avian and 17 mammalian species). Felsenstein, (1985)method of phylogenetically independent contrasts, the first fully phylogenetic method to be proposed, is the best understood of available methods (Grafen, 1989, Martins and Garland, 1991, Garland et al., 1992, Pagel, 1993, Purvis and Garland, 1993, Purvis et al., 1994, Dı́az-Uriarte and Garland, 1996Dı́az-Uriarte and Garland, in pressMartins, 1996b, Martins and Hansen, 1996). Moreover, several free computer programs are available which implement the method (e.g. Joe Felsenstein's PHYLIP package, the PDAP package (Garland et al., 1993), CAIC (Purvis and Rambaut, 1995), COMPARE (Martins and Hansen, 1996)). In brief, the method of independent contrasts uses phylogenetic information (topology and branch lengths) to transform interspecific data (i.e. estimates of mean values for a series of species) into values (standardized independent contrasts) that, in principle, are independent and identically distributed, and hence can be analyzed with standard statistical methods (see Methods section for more details).

To facilitate application of phylogenetically independent contrasts, we used a more detailed indicator of translocation outcome as a dependent variable, rather than the dichotomous measure (success vs failure) used previously (Griffith et al., 1989, Wolf et al., 1996). We developed a continuous outcome variable that was a composite (see Methods section) of the following information: (1) the translocated population persistence (in years) in the field; (2) the classification of the population as self-sustaining, declining, or gone; and (3) the maximum potential life span (years) of each species as a scaling factor. The continuous dependent variable better meets assumptions of phylogenetically independent contrasts, and the inclusion of population persistence information provides a more quantitative measure of the population's ability to persist through time. In principle, this more inclusive dependent variable should increase statistical power to detect significant predictors of `success.'

Section snippets

The data

The up-dated avian and mammalian translocation data used by Wolf et al. (1996)in the multiple logistic regression models were also used in this study. Of the 421 targeted avian and mammalian translocations throughout North America, Australia, and New Zealand, 181 programs were used by Wolf et al. (1996)in the logistic analyses. Within the sample of 181 translocations, 122 were classified as successful and 59 as unsuccessful; 104 involved translocations of birds and 77 of mammals; and 24

Results

After converting the dichotomous dependent variable (success vs failure) into a composite, continuous variable (see Methods section), the nonphylogenetic ordinary stepwise regression produced a model with five main-effect variables and one first-order interaction term: habitat quality (B=0.252, p=0.0176, where B equals the partial regression coefficient), migratory behavior (B=−0.504, p=0.0021), status (B=0.551, p=0.0002), range (B=2.332, p<0.0001), number of animals (B=0.954, p<0.0001), and

Discussion

Complicated biological questions and associated data sets can be analyzed in many different ways. Often, little consensus exists as to the `best' way to analyze a particular type of data. The application of different analytical methods to a single data set is, therefore, prudent. To the extent that different analytical techniques lead to similar conclusions, then we may have enhanced confidence in those conclusions.

With respect to interspecific comparative studies, a flurry of research activity

Conclusion

Although routinely employed in other disciplines, phylogenetically based statistical analyses are virtually nonexistent within such fields as conservation biology and wildlife management. Yet conservation and wildlife biologists often make interspecific comparisons. The same resemblances among closely related species that are often exploited by wildlife biologists for guidance in developing methodological protocols can confound traditional correlational and regression analyses of multi-species

Acknowledgements

The authors thank M.R. Dohm for assistance with statistical analyses and comments on manuscript drafts; R.E. Bleiweiss, J.A.W. Kirsch, and S. Moore helped with derivation of the phylogeny. Also greatly appreciated were C.M.W's. graduate committee members, P. Arcese, T.C. Moermond, and S.A. Temple. This study was funded with grants from the Zoological Society of Milwaukee County, the Chicago Zoological Society, and the Idaho Department of Fish and Game; T.G. is supported by National Science

References (79)

  • R Bleiweiss et al.

    Confirmation of a portion of the Sibley–Ahlquist “tapestry”

    Auk

    (1995)
  • P.W Bright et al.

    Animal translocation for conservation: performance of dormice in relation to release methods, origin and season

    Journal of Applied Ecology

    (1994)
  • Brooks, D.R., McLennan, D.A., 1991. Phylogeny, Ecology, and Behavior: a research program in comparative biology....
  • Catzeflis, F.M., Dickerman, A.W., Michaux, J., Kirsch, J.A.W., 1993. DNA hybridization and rodent phylogeny. In:...
  • Clobert, J., Garland, T., Jr, Barbault, R., 1998. The evolution of demographic tactics in lizards: a test of some...
  • Copley, P.B., 1994. Translocations of native vertebrates in South Australia: a review. In: Serena, M. (Ed.),...
  • M.J Crawley

    The population biology of invaders

    Philosophical Transactions of the Royal Society of London

    (1986)
  • J.A Dı́az et al.

    A comparative study of the relation between heating rates and ambient temperatures in lacertid lizards

    Physiological Zoology

    (1996)
  • R Dı́az-Uriarte et al.

    Testing hypotheses of correlated evolution using phylogenetically independent contrasts: sensitivity to deviations from Brownian motion

    Systematic Biology

    (1996)
  • Dı́az-Uriarte, R., Garland, T. Jr, In press. Effects of branch lengths errors on performance of phylogenetically...
  • C.K Dodd et al.

    Relocation, repatriation, and translocation of amphibians and reptiles: are they conservation strategies that work?

    Herpetologica

    (1991)
  • Draper, N., Smith, H., 1981. Applied Regression Analysis, 2nd edition. John Wiley and Sons, New...
  • S Easteal

    The pattern of mammalian evolution and the relative rate of molecular evolution

    Genetics

    (1990)
  • Eggleton, P., Vane-Wright, R.I. (Eds.), 1994. Phylogenetics and Ecology. Linnean Society Symposium Series Number 17....
  • Ehrlich, P.R., 1986. Which animal will invade? In: Mooney, H.A., Drake, J.A. (Eds.), Ecology of Biological Invasions of...
  • J Felsenstein

    Phylogenies and the comparative method

    American Naturalist

    (1985)
  • T Garland

    Rate tests for phenotypic evolution using phylogenetically independent contrasts

    American Naturalist

    (1992)
  • T Garland et al.

    Why not to do two-species comparative studies: limitations on inferring adaptation

    Physiological Zoology

    (1994)
  • T Garland et al.

    Evolutionary physiology

    Annual Review of Physiology

    (1994)
  • T Garland et al.

    Procedures for the analysis of comparative data using phylogenetically independent contrasts

    Systematic Biology

    (1992)
  • T Garland et al.

    Phylogenetic analysis of covariance by computer simulation

    Systematic Biology

    (1993)
  • A Grafen

    The phylogenetic regression

    Philosophical Transactions of the Royal Society of London

    (1989)
  • B Griffith et al.

    Translocations as a species conservation tool: status and strategy

    Science

    (1989)
  • B Griffith et al.

    Translocations of captive-reared terrestrial vertebrates, 1973–1986

    Endangered Species UPDATE

    (1990)
  • B Griffith et al.

    Animal translocations and potential disease transmission

    Journal of Zoo and Wildlife Medicine

    (1993)
  • Hafner, D.J., 1984. Evolutionary relationships of the Nearctic Sciuridae. In: Murie, J.O., Michener, G.R. (Eds.), The...
  • Harvey, P.H., Pagel, M.D., 1991. The Comparative Method in Evolutionary Biology. Oxford University Press,...
  • Jackson, J.A., 1994. The red-cockaded woodpecker recovery program: professional obstacles to cooperation. In: Clark,...
  • A Janke et al.

    The marsupial mitochondrial genome and the evolution of placental mammals

    Genetics

    (1994)
  • Cited by (214)

    View all citing articles on Scopus
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    Present address: Alaska Cooperative Fish and Wildlife Research Unit, 216 Irving I Building, University of Alaska, Fairbanks, AK 99775.

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