Guidelines for large herbivore translocation simplified: black rhinoceros case study

• Published in: The Journal of applied ecology Vol. 48; no. 2; pp. 493 - 502
• Main Authors: Linklater, Wayne L., Adcock, Keryn, du Preez, Pierre, Swaisgood, Ron R., Law, Peter R., Knight, Michael H., Gedir, Jay V., Kerley, Graham I.H.
• Format: Journal Article
• Language: English
• Published: Oxford, UK Blackwell Publishing 01-04-2011; Blackwell Publishing Ltd; Blackwell
• Subjects: Age; Age structure; Animal, plant and microbial ecology; Applied ecology; Bayesian inference; Biological and medical sciences; Calves; Carrying capacity; Comparative analysis; Conservation; Conservation biology; conservation management; Conservation, protection and management of environment and wildlife; DIC; Diceros bicornis; Ecological modeling; Fundamental and applied biological sciences. Psychology; General aspects; Habitats; Herbivores; hierarchical model; Information theory; Management responses; MCMC; meta‐population; Modeling; Mortality; Parametric models; post‐release mortality; Regression analysis; reintroduction; restocking; Species reintroduction; Translocation; Metapopulation; Herbivorous; meta-population; Mortality; Population translocation; MCMC; conservation management; Diceros bicornis; Environmental management; Reintroduction; Case study; Restocking; Vertebrata; Mammalia; Models; hierarchical model; Perissodactyla; Bayesian inference; Ungulata; Environmental protection; DIC; post-release mortality
• ISSN: 0021-8901; 1365-2664
• DOI: 10.1111/j.1365-2664.2011.01960.x

1. Most hypotheses for translocation success are elaborate, hierarchical, and untested combinations of socio-ecological predictors. Empirical support for those tested is vulnerable to spurious single-predictor relationships and does not account for the hierarchy amongst predictors and non-independence amongst individuals or cohorts. Testing hypotheses as a priori multi-level models promotes stronger inference. 2. We apply a 25-year (1981—2005) data base including 89 reintroduction and 102 restocking events that released 682 black rhinoceros Diceros bicornis into 81 reserves to test 24 hypotheses for translocation success, defined as survival to 1 year post-release. We made information-theoretic comparisons of hypotheses represented as hierarchical models incorporating random effects for reserve and release cohort predictors of death. 3. Mortality rates after restocking were higher than for reintroductions (13·4 cf. 7·9%, respectively) due largely to intraspecific fighting. No predictors strongly influenced reintroduction success, although cohorts consisting entirely of adult males were 8·2% of individuals but contributed 21·9% of deaths, and reserves with lowest carrying capacities (i.e. <0·1 rhino km⁻²) had a 16·3% mortality rate. Most models for restocking success were not supported. Only those including age class received substantial support. Age was the only predictor to strongly influence death rates. Predictors previously thought influential, like population density, reserve area and quality, and cohort size, were not supported. 4. Synthesis and applications. Simple rules succeeded where complex ecological and demographic hypotheses failed to predict survival after translocation of critically endangered black rhinoceros. Results support bold attempts by managers at translocations towards species recovery in most ways that they have historically occurred. Groups of rhinoceros of different size and composition can be successfully moved over large distances between different ecological contexts. Also, the release of cohorts into reserves that are relatively small, poorer habitat or already stocked need not be avoided so long as calves and all-male cohorts are not reintroduced, and only adults used for restocking. Our analysis demonstrates the importance of information-theoretic comparisons of a priori hierarchical models to test hypotheses for conservation management. We caution against interpreting simple correlations or regression amongst a large number of nested ecological and demographic variables.