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Predicting how populations may respond to climate change and anthropogenic pressures requires detailed knowledge of demographic traits, such as survival and reproduction. However, the availability of these data varies greatly across space and taxa. Therefore, it is common practice to conduct population assessments by filling in missing values from surrogate species or other populations of the same species. Using these independent surrogate values concurrently with observed data neglects the life‐history trade‐offs that connect the different aspects of a population's demography. Consequently, this approach introduces biases that could ultimately lead to erroneous management decisions.We use a Bayesian hierarchical framework to combine fragmented multi‐population data with established life‐history theory and reconstruct population‐specific demographic data across a substantial part of a species breeding range. We apply our analysis to a long‐lived colonial species, the black‐legged kittiwake Rissa tridactyla, that is classified as globally Vulnerable and is highly threatened by increasing anthropogenic pressures, including offshore renewable energy development. We then use a projection analysis to examine how the reconstructed demographic parameters may improve population assessments, compared to models that combine observed data with independent surrogate values.Demographic parameters reconstructed using a hierarchical framework can be utilised in a range of population modelling approaches. They can also be used as reference estimates to assess whether independent surrogate values are likely to over or underestimate missing demographic parameters. We show that surrogate values from independent sources are often used to fill in missing parameters that have large potential demographic impact, and that resulting biases are driven in unpredictable directions thus precluding assessments from being consistently precautionary.Synthesis and applications. Our study dramatically increases the spatial coverage of population‐specific demographic data for black‐legged kittiwakes. The reconstructed demographic parameters presented can also be used immediately to reduce uncertainty in the consenting process for offshore wind development in the United Kingdom and Ireland. More broadly, we show that the reconstruction approach used here provides a new avenue for improving evidence‐based management and policy action for animal and plant populations with fragmented and error prone demographic data. [ABSTRACT FROM AUTHOR]

Measuring the demographic parameters of exploited populations is central to predicting their vulnerability and extinction risk. However, current rates of population decline and species loss greatly outpace our ability to empirically monitor all populations that are potentially threatened.The scale of this problem cannot be addressed through additional data collection alone, and therefore it is a common practice to conduct population assessments based on surrogate data collected from similar species. However, this approach introduces biases and imprecisions that are difficult to quantify. Recent developments in hierarchical modelling have enabled missing values to be reconstructed based on the correlations between available life‐history data, linking similar species based on phylogeny and environmental conditions.However, these methods cannot resolve life‐history variability among populations or species that are closely placed spatially or taxonomically. Here, theoretically motivated constraints that align with life‐history theory offer a new avenue for addressing this problem. We describe a Bayesian hierarchical approach that combines fragmented, multispecies and multi‐population data with established life‐history theory, in order to objectively determine similarity between populations based on trait correlations (life‐history trade‐offs) obtained from model fitting.We reconstruct 59 unobserved life‐history parameters for 23 populations of tuna that sustain some of the world's most valuable fisheries. Testing by cross‐validation across different scenarios indicated that life‐histories were accurately reconstructed when information was available for other populations of the same species. The reconstruction of several traits was also accurate for species represented by a single population, although credible intervals increased dramatically.Synthesis and applications. The described Bayesian hierarchical method provides access to life‐history traits that are difficult to measure directly and reconstructs missing life‐history information useful for assessing populations and species that are directly or indirectly affected by human exploitation of natural resources. The method is particularly useful for examining populations that are spatially or taxonomically similar, and the reconstructed life‐history strategies described for the principal market tunas have immediate application to the world‐wide management of these fisheries. The described Bayesian hierarchical method provides access to life‐history traits that are difficult to measure directly and reconstructs missing life‐history information useful for assessing populations and species that are directly or indirectly affected by human exploitation of natural resources. The method is particularly useful for examining populations that are spatially or taxonomically similar, and the reconstructed life‐history strategies described for the principal market tunas have immediate application to the world‐wide management of these fisheries. [ABSTRACT FROM AUTHOR]

Although density-dependent regulation of population growth is thought to be relatively widespread in nature, density-independent models are often used to project the population response to drivers of change. Such models are often considered to provide a maximum estimate of mortality and therefore offer a precautionary approach to impact assessment. However, this perception assumes that density dependence operates as compensatory (negative density dependence), and overlooks that other forms of density dependence, such as depensation (positive density dependence), would generate a contrasting population response., Currently, there is debate about including density-dependent mechanisms in models that assess the impact of offshore wind farms on marine bird populations. Density dependence is considered poorly understood for this group of species. Consequently, it is either excluded from assessments, or incorporated in a compensatory form that has little empirical validation., We reviewed the evidence for compensatory and depensatory regulation of 31 marine bird species, and conducted a meta-analysis to examine the functional shape of density-dependent population growth. The evidence was also evaluated in relation to established species-specific indices of wind farm vulnerability in order to assess whether compensatory mechanisms are likely to offset losses associated with collision or displacement., Compensatory regulation was reported across all of the demographic processes and focal groups considered, and was attributed to a variety of causal mechanisms. The strength of compensatory population growth appeared consistent between colonies; however, the regulation of productivity was highly context-dependent with a similar number of studies reporting compensatory, depensatory and insignificant effects. Depensation was consistently attributed to increased rates of predation at lower population densities., Synthesis and applications. We conclude that among marine bird species with high vulnerability to wind farms, compensatory regulation is unlikely to offset large and sustained losses from the breeding population. In addition, depensation has the potential to accelerate population declines and generate local or regional extinctions, especially in smaller colonial species. Consequently, density-independent models will not offer a consistently precautionary approach for assessing the potential impact of wind farms on marine bird populations. Instead, assessments should examine the potential population response using a range of density-dependent structures. [ABSTRACT FROM AUTHOR]