Recent miniaturization and weight reductions of Global Positioning System (GPS) collars have opened up deployment opportunities on a new array of terrestrial animal species, but the performance of lightweight (,90 g) GPS collars has not been evaluated. I examined the success of 42 GPS collars from 3 manufacturers (Televilt/TVP Positioning, AB, Lindesburg, Sweden; Sirtrack Ltd., Havelock North, New Zealand; H.A.B.I.T [HABIT] Research Ltd., Victoria, BC, Canada) in stationary, open-sky conditions and during deployments on brushtail possums (Trichosurus vulpecula), a nocturnal arboreal marsupial. I assessed performance of these collars in terms of technical malfunctions, fix-success rates, battery longevity, and aspects of location quality. Technical malfunctions occurred in .50% of HABIT and Televilt collars, whereas all Sirtrack collars operated normally. Fix-success rates for all brands were significantly higher during stationary tests than when deployed on brushtail possums. HABIT and Televilt brands functioned poorly in field conditions, with success rates of 16.2% and 2.1%, respectively. Sirtrack collars had the highest fix rate when deployed (64.8%). I modified several HABIT collars by changing the GPS antenna location, with a resultant substantial increase in field fix success (92.6%). Most collars ceased working before they reached 50% of their manufacturer-estimated life expectancy. Suboptimal placement of GPS antenna, combined with short satellite acquisition times and long fix intervals, were a likely cause of low fix-success rates and premature battery failures. Researchers wanting to employ lightweight GPS collars must be aware of current limitations and should carefully consider prospects of low fix rates and limited battery lives before deciding whether these units are capable of meeting study objectives.

The success of research in integrated environmental and natural resource management relies on the participation and involvement of different disciplines and stakeholders. This can be difficult to achieve in practice because many initiatives fail to address the underlying social processes required for successful engagement and social learning. We used an action research approach to support a research-based group with a range of disciplinary and stakeholder expertise to critically reflect on their engagement practice and identify lessons around how to collaborate more effectively. This approach is provided here as a guide that can be used to support reflective research practice for engagement in other integration-based initiatives. This paper is set in the context of an integrated wildlife management research case study in New Zealand. We illustrate how multi-, inter- and trans-disciplinary approaches can provide a framework for considering the different conversations that need to occur in an integrated research program. We then outline rubrics that list the criteria required in inter- and trans-disciplinary collaborations, along with examples of effective engagement processes that directly support integration through such efforts. Finally, we discuss the implications of these experiences for other researchers and managers seeking to improve engagement and collaboration in integrated science, management and policy initiatives. Our experiences reaffirm the need for those involved in integrative initiatives to attend to the processes of engagement in both formal and informal settings, to provide opportunities for critical reflective practice, and to look for measures of success that acknowledge the importance of effective social process.

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Computing "quasi-AIC" (QAIC), in R is a minor pain, because the R Core team (or at least the ones who wrote glm, glmmPQL, etc.) are purists and don't believe that quasi-models should report a likelihood. As far as I know, there are three R packages that compute/handle QAIC: bbmle, AICcmodavg and MuMIn.The basic problem is that quasi-model fits with glm return an NA for the log-likelihood, while the dispersion parameter (ĉ, φ, whatever you want to call it) is only reported for quasi-models. Various ways to get around this are:• fit the model twice, once with a regular likelihood model (family=binomial, poisson, etc.) and once with the quasi-variant -extract the loglikelihood from the former and the dispersion parameter from the latter• only fit the regular model; extract the overdispersion parameter manually with dfun <-function(object) { with(object,sum((weights * residuals^2)[weights > 0])/df.residual) }• use the fact that quasi-fits still contain a deviance, even if they set the log-likelihood to NA. The deviance is twice the negative log-likelihood (it's offset by some constant which I haven't figured out yet, but it should still work fine for model comparisons)The whole problem is worse for MASS::glmmPQL, where (1) the authors have gone to greater efforts to make sure that the (quasi-)deviance is no longer preserved anywhere in the fitted model, and (2) they may have done it for good reason -it is not clear whether the number that would get left in the 'deviance' slot at the end of glmmPQL's alternating lme and glm fits is even meaningful to the extent that regular QAICs are. (For discussion of a similar situation, see the WARNING section of ?gamm in the mgcv package.)Example: use the values from one of the examples in ?glm:1

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