Summary1. The two most common approaches for analysing count data are to use a generalized linear model (GLM), or transform data, and use a linear model (LM). The latter has recently been advocated to more reliably maintain control of type I error rates in tests for no association, while seemingly losing little in power. We make three points on this issue. 2. Point 1 -Choice of statistical model should primarily be made on the grounds of data properties. Choice of testing procedure should be considered and addressed as a separate issue, after model choice. If models with the appropriate data properties nonetheless have statistical problems such as type I error control (i.e. type I error rate greatly exceeds the intended significance level), the best solution is to keep the model but fix the problems. 3. Point 2 -When a test has problems with type I error control, it can usually be corrected, but this may require departure from software default approaches. In particular, resampling is a good solution for small samples that can be easy to implement. 4. Point 3 -Tests based on models that better fit the data (e.g. a negative binomial for overdispersed count data) tend to have better power properties and in some instances have considerably higher power. 5. We illustrate these issues for a 2 9 2 experiment with a count response. This seemingly simple problem becomes hard when the experimental design is unbalanced, and software default procedures using LMs or GLMs can have difficulties, although in both cases the issues can be fixed. 6. We conclude that, when GLMs are thought to fit count data well, and when any necessary steps are taken to correct type I error rates, they should be used rather than LMs. Nonetheless, standard LM tests are often robust and can have good type I error control, so there is an argument for their use for counts when diagnostics are difficult and statistical models are complex, although at some risk of loss of power and interpretability.

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BackgroundAs increasingly fragmented and isolated populations of threatened species become subjected to climate change, invasive species and other stressors, there is an urgent need to consider adaptive potential when making conservation decisions rather than focussing on past processes. In many cases, populations identified as unique and currently managed separately suffer increased risk of extinction through demographic and genetic processes. Other populations currently not at risk are likely to be on a trajectory where declines in population size and fitness soon appear inevitable.ResultsUsing datasets from natural Australian mammal populations, we show that drift processes are likely to be driving uniqueness in populations of many threatened species as a result of small population size and fragmentation. Conserving and managing such remnant populations separately will therefore often decrease their adaptive potential and increase species extinction risk.ConclusionsThese results highlight the need for a paradigm shift in conservation biology practise; strategies need to focus on the preservation of genetic diversity at the species level, rather than population, subspecies or evolutionary significant unit. The introduction of new genetic variants into populations through in situ translocation needs to be considered more broadly in conservation programs as a way of decreasing extinction risk by increasing neutral genetic diversity which may increase the adaptive potential of populations if adaptive variation is also increased.Electronic supplementary materialThe online version of this article (doi:10.1186/s12983-016-0163-z) contains supplementary material, which is available to authorized users.

Genetic rescue has now been attempted in several threatened species, but the contribution of genetics per se to any increase in population health can be hard to identify. Rescue is expected to be particularly useful when individuals are introduced into small isolated populations with low levels of genetic variation. Here we consider such a situation by documenting genetic rescue in the mountain pygmy possum, Burramys parvus. Rapid population recovery occurred in the target population after the introduction of a small number of males from a large genetically diverged population. Initial hybrid fitness was more than two-fold higher than non-hybrids; hybrid animals had a larger body size, and female hybrids produced more pouch young and lived longer. Genetic rescue likely contributed to the largest population size ever being recorded at this site. These data point to genetic rescue as being a potentially useful option for the recovery of small threatened populations.

A functional traits-based theory of organismal communities is critical for understanding the principles underlying community assembly, and predicting responses to environmental change. This is particularly true for terrestrial arthropods, of which only 20% are described. Using epigaeic ant assemblages, we asked: (1) can we use morphological variation among species to predict trophic position or preferred microhabitat; (2) does the strength of morphological associations suggest recent trait divergence; (3) do environmental variables at site scale predict trait sets for whole assemblages? We pitfall-trapped ants from a revegetation chronosequence and measured their morphology, trophic position [using C:N stoichiometry and stable isotope ratios (δ)] and characteristics of microhabitat and macrohabitat. We found strong associations between high trophic position (low C:N and high δ(15)N) in body tissue and morphological traits: predators were larger, had more laterally positioned eyes, more physical protection and tended to be monomorphic. In addition, morphological traits were associated with certain microhabitat features, e.g. smaller heads were associated with the bare ground microhabitat. Trait-microhabitat relationships were more pronounced when phylogenetic adjustments were used, indicating a strong influence of recent trait divergences. At the assemblage level, our fourth corner analysis revealed associations between the prevalence of traits and macrohabitat, although these associations were not the same as those based on microhabitat associations. This study shows direct links between species-level traits and both diet and habitat preference. Trait-based prediction of ecological roles and community structure is thus achievable when integrating stoichiometry, morphology and phylogeny, but scale is an important consideration in such predictions.