A subspecies is a collection of populations within a biological species that are diagnosably distinct from other such collections of populations. That infraspecific designation has motivated a litany of spirited debates over the past half-century, from impassioned pleas for its retention to heated outcries for its abolition. We believe that the vast majority of attacks on the subspecies concept have resulted from displeasure with its improper application, not from serious flaws in the concept itself. The recognition of diagnosable subspecies allows one to address many questions not easily answered otherwise, ranging from dispersal and migration to local selection and adaptation and biogeographic affinities, yet that goal was lost for many years. Many taxonomists in the late nineteenth century and first half of the twentieth century named subspecies on the basis of average differences between populations under study, a procedure at odds with identification of diagnosable populations. To resolve that dilemma, we make explicit the established 75% rule for subspecies recognition, including formalizing the rule and developing a simple statistic to test whether diagnosability is met. The equations can be adapted readily to any level of diagnosability. We apply the concept and the statistic to a revision of the subspecies of the Sage Sparrow (Amphispiza belli). Rather than the seven named subspecies or the five that are generally considered valid, we show that only three aggregates of populations are diagnosable, and thus only three subspecies should be recognized: (1) A. b. belli in chaparral and sage scrub of coastal California, northwestern Baja California, and San Clemente Island; (2) A. b. cinerea in desert scrub of west-central Baja California; and (3) A. b. nevadensis in sagebrush and saltbush of the Great Basin and interior California. Consistent application of the 75% rule will result in fewer trinomials and a more biologically meaningful and taxonomically useful subspecies concept.

Parental investment and sexual selection. Pages 136-179 in Sexual selection and the descent of man (B. Campbell, Ed.). Heinemann, London. WESTNEAT, D.F. 1990. Genetic parentage in the Indigo Bunting: A study using DNA fingerprinting. Behavioral Ecology and Sociobiology 27:67-76. WESTNEAT, D.F., P. W. SHERMAN, AND M. L. MORTON. 1990. The ecology and evolution of extra-pair copulations in birds. Current Ornithology 7:331-369. WETTON J. H., R. E. CARTER, D. T. PARKIN, AND D. WALTER. 1987. Demographic study of a wild House Sparrow population using DNA fingerprinting. Nature 327:147-149. WETTON, J. H., D. T. PARKIN, AND R. E. CARTR. 1992. The use of genetic markers for parentage analysis in Passer domesticus (House Sparrows). Heredity 69:243-254.

Summary1. Reliable methods to downscale species distributions from coarse to fine grain (equivalent to resolution or support) hold great potential benefit for ecology and conservation. Existing methods have been based on partially unrealistic assumptions and yield mixed results. 2. Here, we introduce a novel and simple approach for downscaling species distribution models based on a hierarchical Bayesian modelling (HBM) framework. Our approach treats putative (unknown) fine-grain presences/absences as latent variables, which are modelled as a function of observed fine-grain environmental variables and constrained by observed coarse-grain presences/absences using logistic regression. The aim is to produce downscaled fine-grain probabilities of species occurrence that (1) closely resemble the probabilities produced by a logistic model parameterized with the observed fine-grain data (the 'reference model') and (2) are improvements over conventional downscaling methods. We additionally test how fine-grain occupancy based on power-law scale-area relationships modifies the downscaling results. We test our approach on 127 bird species from the San Diego breeding atlas data surveyed at 5 km grain. 3. The HBM approach provides unbiased fine-grain probabilities of occurrence whilst the conventional methods (direct approach, point sampling) consistently over-predict occurrence probabilities. Incorporation of the downscaled occupancy further improves reliability of the models, but only in cases when the fine-grain occupancy is estimated accurately. 4. Summing predictions across grid cells and species, HBMs provide better estimates of fine-grain species richness than conventional methods. They also provide better estimates of fine-grain occupancy (prevalence). 5. The presented HBM-based downscaling approach offers improved predictions of fine-grain presence and absence compared with existing methods. The combination of the Bayesian approach with key macroecological relationships (specifically, the scale-area relationship) offers a promising general basis for downscaling distributions that may be extended, for example, using generalized linear or additive models. These approaches enable integrative predictions of spatial biodiversity patterns at fine grains.

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