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This article is a response to Oliver et al.'s Commentary 'The dark side of coproduction: do the costs outweigh the benefits for health research?' recently published in Health Research Policy and Systems (2019, 17:33). The original commentary raises some important questions about how and when to co-produce health research, including highlighting various professional costs to those involved. However, we identify four related limitations in their inquiry, as follows: (1) the adoption of a problematically expansive definition of co-production that fails to acknowledge key features that distinguish co-production from broader collaboration; (2) a strong focus on technocratic rationales for co-producing research and a relative neglect of democratic rationales; (3) the transposition of legitimate concerns relating to collaboration between researchers and practitioners onto work with patients, service users and marginalised citizens; and (4) the presentation of bad practice as an inherent flaw, or indeed 'dark side', of co-production without attending to the corrupting influence of contextual factors within academic research that facilitate and even promote such malpractice. The Commentary's limitations can be seen to reflect the contemporary use of the term 'co-production' more broadly. We describe this phenomenon as 'cobiquity' - an apparent appetite for participatory research practice and increased emphasis on partnership working, in combination with the related emergence of a plethora of 'co' words, promoting a conflation of meanings and practices from different collaborative traditions. This phenomenon commonly leads to a misappropriation of the term 'co-production'. Our main motivation is to address this imprecision and the detrimental impact it has on efforts to enable co-production with marginalised and disadvantaged groups. We conclude that Oliver et al. stray too close to 'the problem' of 'co-production' seeing only the dark side rather than what is casting the sha

We review the role of density dependence in the stochastic extinction of populations and the role density dependence has played in population viability analysis (PVA) case studies. In total, 32 approaches have been used to model density regulation in theoretical or applied extinction models, 29 of them are mathematical functions of density dependence, and one approach uses empirical relationships between density and survival, reproduction, or growth rates. In addition, quasi-extinction levels are sometimes applied as a substitute for density dependence at low population size. Density dependence further has been modelled via explicit individual spacing behaviour and/or dispersal. We briefly summarise the features of density dependence available in standard PVA software, provide summary statistics about the use of density dependence in PVA case studies, and discuss the effects of density dependence on extinction probability. The introduction of an upper limit for population size has the effect that the probability of ultimate extinction becomes 1. Mean time to extinction increases with carrying capacity if populations start at high density, but carrying capacity often does not have any effect if populations start at low numbers. In contrast, the Allee effect is usually strong when populations start at low densities but has only a limited influence on persistence when populations start at high numbers. Contrary to previous opinions, other forms of density dependence may lead to increased or decreased persistence, depending on the type and strength of density dependence, the degree of environmental variability, and the growth rate. Furthermore, effects may be reversed for different quasi-extinction levels, making the use of arbitrary quasi-extinction levels problematic. Few systematic comparisons of the effects on persistence between different models of density dependence are available. These effects can be strikingly different among models. Our understanding of the eff

Aspects of within-population spatial structure are often neglected in the modeling of population viability. To analyze the relevance of the spatial structure of single populations to population persistence, we compared the results of three models developed for the territorial, arboreal gecko Oedura reticulata: (1) a spatially structured model in which both low and high densities incur mortality costs due to increased movement, (2) a spatially structured model in which the Allee effect is removed, and (3) a spatially unstructured model in which there are no effects of density on mortality. Compared with nonspatial model populations, spatially structured populations exhibited reduced persistence. The Allee effect contributed only a small amount to the reduction in persistence. Increased mortality at high densities caused by difficulties in finding territories markedly reduced persistence in the spatially structured models compared with the density-independent nonspatial model. We argue that the inclusion of elements of spatial structure may considerably influence the estimation of extinction risk in population viability analyses. Resumen: Los aspectos de la estructura espacial dentro de una población son frecuentemente ignorados en el modelado de viabilidad poblacional. Para analizar la importancia de la estructura espacial de poblaciones individuales en la persistencia de una población, comparamos los resultados de tres modelos desarrollados para un gecko arbóreo territorial, Oedura reticulata: (1) un modelo estructurado espacialmente en el cual tanto las densidades bajas como altas incurren en costos de mortalidad debido a un incremento en el movimiento, (2) un modelo estructurado espacialmente en el cual el efecto Allee es removido y (3) un modelo estructurado espacialmente en el cual no hay efectos de la densidad sobre la mortalidad. Comparados con modelos de poblaciones no espaciales, las poblaciones estructuradas espacialmente exhibieron una persistencia reducid

We present a population viability model for an arboreal gecko (Oedura reticulata). This gecko needs a habitat of smooth-barked Eucalyptus woodlands. In Western Australia its distribution has declined dramatically, largely through clearance of woodlands, but populations persist within woodland remnants. Evidence from extensive field data suggests that the gecko was formerly distributed through much of the original eucalypt woodlands, and that geckos show little movement between patches. The populations in all woodland remnants seem to be isolated. We ask whether the present distribution of the gecko across remnants could have been produced solely by the extinction of populations through demographic stochasticity. To test this possibility, we developed a stochastic, individual-based model including environmental stochasticity and estimated the percentage of extinct populations of different size from known field characteristics and the time span since the clearing of the woodland. The model predicted a relationship between remnant size and gecko persistence, driven by demographic stochasticity, that is qualitatively similar to the observed pattern. Despite extensive testing, however, we found that the model predicted an incidence function much too optimistic for the observed distribution of populations in small remnants. This discrepancy between field data and our model is due to a series of implicit assumptions. Thus, our modeling exercise sheds light on the procedures commonly applied to population viability analyses of single populations of endangered species. The implicit assumptions involved in such models make many predictions vague. We suggest that for the study of declining species like O. reticulata it is essential to adequately test extinction models and therefore population viability analyses.Read More: http://www.esajournals.org/doi/abs/10.1890/1051-0761%282001%29011%5B1738%3ADSDNPP%5D2.0.CO%3B2 We present a population viability model for an arboreal gecko