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Large carnivores are expanding in Europe, and their return is associated with conflicts that often result in policies to regulate their population size through culling. Being wide‐ranging species, their populations are often distributed across several jurisdictions, which may vary in the extent to which they use lethal control. This creates the conditions for the establishment of source‐sink dynamics across borders, which may frustrate the ability of countries to reach their respective management objectives. To explore the consequences of this issue, we constructed a vec‐permutation projection model, applied to the case of wolverines in south‐central Scandinavia, shared between Norway (where they are culled) and Sweden (where they are protected). We evaluated the effect of compensatory immigration on wolverine population growth rates, and if the effect was influenced by the distance to the national border. We assessed to what extent compensatory immigration had an influence on the number of removals needed to keep the population at a given growth rate. In Norway, the model estimated a stable trend, whereas in Sweden it produced a 10% annual increase. The effect of compensatory immigration corresponded to a 0.02 reduction in population growth rate in Sweden and to a similar increase in Norway. This effect was strong closer to the Norwegian‐Swedish border, but weak when moving away from it. An average of 33 wolverines was shot per year in the Norwegian part of the study area. If no compensatory immigration from Sweden had occurred, 28 wolverines shot per year would have been sufficient to achieve the same goal. About 15.5% of all the individuals harvested in Norway between 2005 and 2012 were compensated by immigrants, causing a decrease in population growth rate in Sweden. Synthesis and applications. When a population is transboundary, the consequences of management decisions are also transboundary, even though the political bodies in charge of those decisions, the stakeholders who influence them, and the taxpayers who finance them are not. It is important that managers and citizens be informed that a difference in management goals can reduce the efficiency, and increase the costs, of wildlife management. When a population is transboundary, the consequences of management decisions are also transboundary, even though the political bodies in charge of those decisions, the stakeholders who influence them, and the taxpayers who finance them are not. It is important that managers and citizens be informed that a difference in management goals can reduce the efficiency, and increase the costs, of wildlife management.

New-energy vehicles (NEV), particularly electric vehicles, are globally popular with political and financial support from governments, which aim at reducing energy consumption and environmental pollution in the transportation sector. This paper designs a matrix model which identifies the factors influencing the decision-making process on purchasing NEVs, and applies the model on a Chinese case to investigate the impact of influencing factors on Chinese consumers’ purchase behavior to NEVs. The influencing factors are divided into five groups: economic, political, social, technological and environmental. Through a detailed questionnaire survey of 526 consumers in China, this paper conducts a correlation and difference analysis between these consumers’ status and the influencing factors on the purchase decision-making of NEVs through SPSS software. The results indicate that economic, technological and political factors have a strong influence on purchase behavior, while social and environmental factors have a weaker influence. Additional principal component analysis on influencing factors’ preference reveals that consumers’ preferences have two tendencies: practicability or appearance, with the former is attracting far more attention. Rather than practicability or appearance, consumers care more about economic and political factors. According to these results, this paper suggests several policy implications on enhancing the consumers’ willingness to purchase NEVs.

1. Matrix models are popular tools for plant demographic studies, but their application to long-lived, slow-growing species is hampered by the fact that (i) model output is highly sensitive to category width and (ii) growth variation between individuals can only be partially accounted for. Integral Projection Models (IPMs) - an extension of matrix models - offer a solution to these problems. 2. Here, we introduce a new method to parameterize IPMs for trees - the 'integration method'- which allows constructing IPMs for long-lived, slow-growing species. This approach is more suitable than the 'midpoint rule', which is customarily used. 3. We built IPMs for six tree species from Vietnamese (sub)tropical forests. For four of these species, population growth rate (λ) was highly sensitive to the number of categories in the transition matrix. Population growth stabilized for IPMs with 100-1000 categories, corresponding to categories of 0.1-1 cm in trunk diameter. This preferred width is much narrower than the 10-cm-wide categories customarily used in tree models. 4. The distribution of elasticity values over transition types (stasis, progression to next and further categories) is also highly sensitive to matrix dimension in IPMs. In addition, elasticity distribution is influenced by including or excluding growth variation. 5. Age estimates obtained from IPMs were also highly sensitive to matrix dimension: an IPM with 1000 size categories yielded 2-4 times higher age estimates for large trees than one with 10 size categories. Observed ages obtained from tree ring analyses for four of the study species allowed validating these estimates. IPMs with 10 categories strongly underestimated age, while those with 1000 categories yielded slight age overestimates. Underestimating age in small matrices is caused by the occurrence of unrealistically fast pathways through the life cycle and is probably widespread among tree models with broad categories. Overestimating ages in IPMs with narrow categories may be due to temporally autocorrelated growth or errors in fitting growth curves. 6. Synthesis. IPMs are highly suitable tools to analyse tree demography. We recommend that tree IPMs (and classical matrix models) apply narrow diameter categories (0.1-1 cm width) to obtain reliable model output.

Impacts of climate change on the future dynamics of Central African forests are still largely unknown, despite the acuteness of the expected climate changes and the extent of these forests. The high diversity of species and the potentially equivalent diversity of responses to climate modifications are major difficulties encountered when using predictive models to evaluate these impacts. In this study, we applied a mixture of inhomogeneous matrix models to a long-term experimental site located in M'Baïki forests, in the Central African Republic. This model allows the clustering of tree species into processes-based groups while simultaneously selecting explanatory climate and stand variables at the group-level. Using downscaled outputs of 10 general circulation models (Gcm), we projected the future forest dynamics up to the end of the century, under constant climate and Representative Concentration Pathways 4.5 and 8.5. Through comparative analyses across Gcm versions, we identified tree species meta-groups, which are more adapted than ecological guilds to describe the diversity of tree species dynamics and their responses to climate change. Projections under constant climate were consistent with a forest ageing phenomenon, with a slowdown in tree growth and a reduction of the relative abundance of short-lived pioneers. Projections under climate change showed a general increase in growth, mortality and recruitment. This acceleration in forest dynamics led to a strong natural thinning effect, with different magnitudes across species. These differences caused a compositional shift in favour of long-lived pioneers, at the detriment of shade-bearers. Consistent with other field studies and projections, our results show the importance of elucidating the diversity of tree species responses when considering the general sensitivity of Central African forests dynamics to climate change.

A meaningful solution to an inversion problem should be composed of the preferred inversion model and its uncertainty and resolution estimates. The model uncertainty estimate describes an equivalent model domain in which each model generates responses which fit the observed data to within a threshold value. The model resolution matrix measures to what extent the unknown true solution maps into the preferred solution. However, most current geophysical electromagnetic (also gravity, magnetic and seismic) inversion studies only offer the preferred inversion model and ignore model uncertainty and resolution estimates, which makes the reliability of the preferred inversion model questionable. This may be caused by the fact that the computation and analysis of an inversion model depend on multiple factors, such as the misfit or objective function, the accuracy of the forward solvers, data coverage and noise, values of trade-off parameters, the initial model, the reference model and the model constraints. Depending on the particular method selected, large computational costs ensue. In this review, we first try to cover linearised model analysis tools such as the sensitivity matrix, the model resolution matrix and the model covariance matrix also providing a partially nonlinear description of the equivalent model domain based on pseudo-hyperellipsoids. Linearised model analysis tools can offer quantitative measures. In particular, the model resolution and covariance matrices measure how far the preferred inversion model is from the true model and how uncertainty in the measurements maps into model uncertainty. We also cover nonlinear model analysis tools including changes to the preferred inversion model (nonlinear sensitivity tests), modifications of the data set (using bootstrap re-sampling and generalised cross-validation), modifications of data uncertainty, variations of model constraints (including changes to the trade-off parameter, reference model and matrix regularisation operator), the edgehog method, most-squares inversion and global searching algorithms. These nonlinear model analysis tools try to explore larger parts of the model domain than linearised model analysis and, hence, may assemble a more comprehensive equivalent model domain. Then, to overcome the bottleneck of computational cost in model analysis, we present several practical algorithms to accelerate the computation. Here, we emphasise linearised model analysis, as efficient computation of nonlinear model uncertainty and resolution estimates is mainly determined by fast forward and inversion solvers. In the last part of our review, we present applications of model analysis to models computed from individual and joint inversions of electromagnetic data; we also describe optimal survey design and inversion grid design as important applications of model analysis. The currently available model uncertainty and resolution analyses are mainly for 1D and 2D problems due to the limitations in computational cost. With significant enhancements of computing power, 3D model analyses are expected to be increasingly used and to help analyse and establish confidence in 3D inversion models.

A bstract We compute individual distributions of low-lying eigenvalues of massive chiral random matrix ensembles by the Nyström-type quadrature method for evaluating the Fredholm determinant and Pfaffian that represent the analytic continuation of the Janossy densities (conditional gap probabilities). A compact formula for individual eigenvalue distributions suited for precise numerical evaluation by the Nyström-type method is obtained in an explicit form, and the k th smallest eigenvalue distributions are numerically evaluated for chiral unitary and symplectic ensembles in the microscopic limit. As an application of our result, the low-lying Dirac spectra of the SU(2) lattice gauge theory with N F = 8 staggered flavors are fitted to the numerical prediction from the chiral symplectic ensemble, leading to a precise determination of the chiral condensate of a two-color QCD-like system in the future.

1. Understanding the evolution of life histories requires an assessment of the process that generates variation in life histories. Within-population heterogeneity of life histories can be dynamically generated by stochastic variation of reproduction and survival or be generated by individual differences that are fixed at birth. 2. We show for the kittiwake that dynamic heterogeneity is a sufficient explanation of observed variation of life histories. 3. The total heterogeneity in life histories has a small contribution from reproductive stage dynamics and a large contribution from survival differences. We quantify the diversity in life histories by metrics computed from the generating stochastic process. 4. We show how dynamic heterogeneity can be used as a null model and also how it can lead to positive associations between reproduction and survival across the life span. 5. We believe our approach to identifying the nature of among-individual heterogeneity yields important insights into the forces that generate within-population variation of life-history traits. It provides an alternative to claims that fixed individual differences are a major determinant of heterogeneity in life histories.

Boltonia decurrens is an endangered plant restricted to the Illinois River Valley. Its complex life cycle has evolved in response to the dynamics of the historic flood regime, which has changed dramatically in the last century due to the construction of navigation dams and agricultural levees. To explore the effects of these changes, we developed deterministic and stochastic matrix population models of the demography of Boltonia. We used periodic matrix models to incorporate intra-annual seasonal variation. We estimated parameters as a function of the timing of spring flood recession (early or late) and of growing season precipitation (high or low). Late floods and/or low precipitation reduce population growth (λ). Early floods and high precipitation lead to explosive population growth. Elasticity analysis shows that changes in floods and precipitation alter the life history pathways responsible for population growth, from annual to biennial and eventually clonal pathways. We constructed and analyzed a stochastic model in which flood timing and precipitation vary independently, and we computed the stochastic growth rate (log λs) and the variance growth rate (σ2) as functions of the frequency of late floods and low precipitation. Using historical data on floods and rainfall over the last 100 years, we found that log λshas declined as a result of hydrological changes accompanying the regulation of the river. Stochastic elasticity analysis showed that over that time the contribution of annual life history pathways to log λshas declined as the contributions of biennial and clonal pathways have increased. Over the same time period, σ2has increased, in agreement with observations of large fluctuations in local B. decurrens populations. Undoubtedly, many plant and animal species evolved in concert with dynamic habitats and are now threatened by anthropogenic changes in those dynamics. The data and analyses used in this study can be applied to management and development strategies to preserve other dynamic systems.

We carried out statistical evaluations of two probability density functions for the microboudin palaeopiezometer using the Akaike information criterion (AIC) and the cross-validation (CV) technique. In terms of the relevant stress-transfer model, these functions are defined as the elastic matrix model and the Newtonian viscous matrix model, respectively. The AIC and CV techniques enable us to evaluate the relative quality of both models when applied to nine data sets collected from metachert samples containing tourmaline grains in a quartz matrix, collected from the East Pilbara Terrane, Western Australia. The results show that the elastic matrix model is the more appropriate probability density function for analysis of fracturing of tourmaline grains in a quartz matrix. This statistical evaluation shows the validity of the elastic matrix model for the microboudin palaeopiezometer when analysing such data sets. Graphical abstract .

1. Plant demographers using matrix tools have paid special attention to vital rates of reproduction, growth and survival. The demographic implications of plants regressing in size, or shrinking, have been overlooked. Shrinkage has either been ignored during demographic censuses or lumped with other demographic processes such as stasis or growth under the assumption that they have similar demographic effects. 2. We carried out a comparative prospective analysis using classical vital rate elasticities in size-based projection matrices of 80 herbaceous perennial species. We analysed the correlations of the elasticities of each demographic vital rate with the demographic life-history traits (life span, population growth rate, etc.). 3. We also conducted a comparative loop analysis to understand the effects of shrinkage on demographic parameters linked to size plasticity. We classified loops into 'recruitment' (growth that contributes to reproduction), 'size plasticity' (where individuals fluctuate in size) and 'size rigidity' (no change in size class), and used them as the basis to explain ecological characteristics of the species. 4. Our results with classical vital rates demonstrate that considering shrinkage as a separate vital rate increases our understanding of factors that contribute to demographic equilibrium (e.g. minimized departure from population growth rate at equilibrium) and buffering (e.g. higher speed of recovery after disturbance), and to reproductive strategies (e.g. mean age of parents of offspring). 5. The loop analysis results support the findings with vital rate analyses and also reveal new patterns: high growth rates are not exclusively dominated by high elasticities of recruitment, but also by size-plastic loops, and long-lived species experience a marginal increase in the demographic importance of size plasticity. 6. Synthesis. This study illustrates the necessity for exploring individual demographic vital rates, as opposed to grouping them, to advance our understanding of how different biological processes affect population dynamics. Shrinkage is demographically important because it aids in demographic buffering, increases survival and is related to maintenance–reproduction trade-offs. However, shrinkage cannot be fully explored only with traditional elasticity approaches; because shrinkage for some species is a fundamental plastic trait, its importance is more appropriately captured with loop analyses.