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Highlights • A framework to guide forest management to deal more effectively with plant invasions. • Models based on age/size classes represent a significant improvement. • Height classes are influenced by distinct factors operating at different scales. • Step forward for establishing management actions at appropriate spatial scales. • Way to enhance the efficiency of control actions on major forest invaders. Abstract Identifying the key factors driving invasion processes is crucial for designing and implementing appropriate management strategies. In fact, the importance of (model-based) prevention and early detection was highlighted in the recent European Union regulation on Invasive Alien Species. Models based on abundance estimates for different age/size classes would represent a significant improvement relative to the more usual models based only on species' occurrence data. Here, we evaluate the relative contribution of different environmental drivers to the spatial patterns of abundance of several height classes (or life-stages) of invasive tree populations at the regional scale, using a data-driven hierarchical modelling approach. A framework for modelling life-stages to obtain spatial projections of their potential occurrence or abundance has not been formalized before. We used Acacia dealbata (Silver-wattle) as a test species in northwest of Portugal, a heavily invaded region, and applied a multimodel inference to test the importance of various environmental drivers in explaining the abundance patterns of five plant height classes in local landscape mosaics. The ensemble of height classes is considered here as a proxy for population dynamics, life-stages and age of adult trees. In this test with A. dealbata , we used detailed field data on population height structure and calibrated an independent model for each height class. We found evidence to support our hypothesis that the distribution of height classes is mostly influenced by distinct factors operating at different scales. The spatial projections which resulted from several height class models provide an overview of population structure and invasion dynamics considering various life-stages, that is widely used in biodiversity and invasion research. The approach proposed here provides a framework to guide forest management to deal more effectively with plant invasions. It allows to test the effects of key invasion factors (depending on the focal species and on data availability) and supports the spatial identification of suitable areas for invasive species' occurrence while also accounting for the structural complexity of invasive species populations, thereby anticipating future invasion dynamics. The approach thus constitutes a step forward for establishing management actions at appropriate spatial scales and for focusing on earlier stages of invasion and their respective driving factors (regeneration niche), thereby enhancing the efficiency of control actions on major forest invaders. [ABSTRACT FROM AUTHOR]

Copyright of Avian Conservation & Ecology is the property of Resilience Alliance and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract The assessment of fetal size and the accurate estimation of gestational age are of crucial importance for proper pregnancy management. The information is almost exclusively based on ultrasound measurements of fetal biometric parameters and the means for evaluating these measurements are age-related reference charts (centile charts) allowing interpretation of obtained fetal measurement in comparison with the expected average measurement in the reference population. The construction of such reference charts requires an appropriate statistical methodology. The most frequent method for the construction of fetal reference charts from cross-sectional data is the parametric approach with fractional polynomials regression functions for the mean and standard deviation of each fetal measurement. This article suggests how this method can be extended to longitudinal data using fractional polynomials in linear mixed effect regression. The presented approach includes maximum likelihood estimation for fitting first- and second-order fractional polynomial models, and multimodel inference using Akaike's information criterion and related tools as a suitable strategy for model selection. Finally, an example of the suggested approach is presented. [ABSTRACT FROM AUTHOR]

Although spatial variability in the distribution of soil organisms is often regarded as random, recent attempts have found significant spatial structure at several scales. Understanding the drivers of this variability at large scale could help to evaluate the ecological and socio-economic impacts of soil organisms in the ecosystems. In the present study we aim to (i) understand the relative role of environmental factors and human influence on the distribution of one of the World’s most destructive and invasive plant pathogens, Phytophthora cinnamomi Rands, and (ii) to estimate its potential geographic distribution in the Iberian Peninsula as a proxy of its potential impact. For this purpose, we used a total of 277 records of P. cinnamomi (presence: n  = 157; absences: n  = 120) in the Iberian Peninsula and three sets of explanatory variables reflecting abiotic conditions (climate and soil), biotic conditions (main susceptible host tress distribution and vegetation cover based on NDVI) and human influence. The current distribution of P. cinnamomi in the Iberian Peninsula seems to be influenced principally by fine texture soil and climate, following the land use and lastly the presence of its main host forest species. Its potential distribution across the Iberian Peninsula suggests potential for further expansion along the northeast and southeast of Spain and central Portugal. Given the significant impact of this pathogen on forest ecosystems, the modelling of its distribution in the Iberian Peninsula offers an important decision tool for the monitoring and restoration of declining Mediterranean oak forests. [ABSTRACT FROM AUTHOR]

The effect of multiple stressors on marine ecosystems is poorly understood. To partially bridge this knowledge gap we investigated the relative contribution of environmental variables to density and diversity dynamics of the zooplankton community in the Belgian part of the North Sea. We applied multimodel inference on generalized additive models to quantify the relative contribution of chlorophyll a , temperature, nutrients, salinity and anthropogenic chemicals (i.e. polychlorinated biphenyls and polycyclic aromatic hydrocarbons) to the dynamics of calanoid copepod species in the Belgian part of the North Sea. Temperature was the only predictor consistently showing a high importance in all models predicting the abundances of the selected copepod species. The relative contribution of other predictors was species-dependent. Anthropogenic chemicals were important predictors for three out of six species indicating that chemical mixtures at low concentrations should not be left unattended when performing risk assessments in a natural environment. [ABSTRACT FROM AUTHOR]

Model choice is usually an inevitable source of uncertainty in model-based statistical analyses. While the focus of model choice was traditionally on methods for choosing a single model, methods to formally account for multiple models within a single analysis are now accessible to many researchers. The specific technique of model averaging was developed to improve predictive ability by combining predictions from a set of models. However, it is now often used to average regression coefficients across multiple models with the ultimate goal of capturing a variable's overall effect. This use of model averaging implicitly assumes the same parameter exists across models so that averaging is sensible. While this assumption may initially seem tenable, regression coefficients associated with particular explanatory variables may not hold equivalent interpretations across all of the models in which they appear, making explanatory inference about covariates challenging. Accessibility to easily implementable software, concerns about being criticized for ignoring model uncertainty, and the chance to avoid having to justify choice of a final model have all led to the increasing popularity of model averaging in practice. We see a gap between the theoretical development of model averaging and its current use in practice, potentially leaving well-intentioned researchers with unclear inferences or difficulties justifying reasons for using (or not using) model averaging. We attempt to narrow this gap by revisiting some relevant foundations of regression modeling, suggesting more explicit notation and graphical tools, and discussing how individual model results are combined to obtain a model averaged result. Our goal is to help researchers make informed decisions about model averaging and to encourage question-focused modeling over method-focused modeling. [ABSTRACT FROM AUTHOR]

The Alagoas Curassow ( Pauxi mitu ) became extinct in the wild in the 1980's through a combination of loss/degradation of its Atlantic Forest habitat and over-hunting. Ambitious plans are now underway to reintroduce captive-reared Curassows using a patchwork of protected forest fragments on private lands. Although the planned reintroduction sites are broadly ecologically suitable, it is not clear that the threats from hunting and habitat disturbance have been removed. In other words, the cultural (as opposed to biological) suitability and viability of these sites is unknown. We used a semi-structured social survey of 402 residents who live near three proposed reintroduction sites to evaluate the cultural suitability of the reintroduction. Specifically, we adopted a multimodel inference approach to identify socio-demographic characteristics (e.g., age, gender, education level, duration of residence), knowledge (e.g., of local biodiversity and hunting practices) and behaviors (e.g., bushmeat consumption and firewood collection) that influence support for the reintroduction. Respondents were generally positive to bringing the Curassow back, though our data indicates that hunting is still a major part of the local culture. Support for the reintroduction was most strongly associated with older and, especially, better educated residents. Residents from different reintroduction sites also differed in their professed levels of support. Our results highlight the importance of focused environmental educational programmes to improve the cultural feasibility of this flagship reintroduction for Northeast Brazil. [ABSTRACT FROM AUTHOR]

Knowledge of wood fiber attributes (WFA) is important for evaluating forest resources and optimizing efficiency in the forest industry. To improve our ability to estimate WFA in the forest, we analyzed the relationships between structural metrics derived from terrestrial laser scanner (TLS) data and four key attributes of industrial significance: wood density, fiber length, microfibril angle, and coarseness. We developed a suite of structural metrics that relate to four aspects of the forest: canopy structure, competition, vegetation density, and local topography. We modeled WFA for sites dominated by black spruce ( Picea mariana ) and balsam fir ( Abies balsamea ) trees. For black spruce sites, R 2 values ranged from 63% to 72%. Structural metrics that relate to competition were the strongest explanatory variables. For balsam fir sites, R 2 ranged from 37% to 63% using structural metrics that relate mostly to canopy structure. Our results demonstrate that local structural variables are useful explanatory variables for predicting WFA of the dominant coniferous species in Newfoundland. [ABSTRACT FROM AUTHOR]

Copyright of Basic & Applied Ecology is the property of Urban & Fischer Verlag and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Tropical Conservation Science is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Highlights: [•] Models of fiber attributes were developed using an extensive database of measurements taken from tree cores. [•] Akaike’s information criterion was used to select predictive models. [•] The most influential variables were dependent on the species and fiber attributes being modeled. [•] Results demonstrate potential to map fiber attributes for boreal forests in Newfoundland, Canada. [ABSTRACT FROM AUTHOR]

Copyright of Brazilian Journal of Biology is the property of Instituto Internacional de Ecologia and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Understanding the factors associated with declines of at‐risk species is an important first step in setting management and recovery targets. This step can be challenging when multiple aspects of climate and land use are changing simultaneously, and any or all could be contributing to population declines. We analyzed population trends of monarch butterflies in western North America in relation to likely environmental drivers. Unlike the larger eastern monarch population, past analyses of western monarchs have only evaluated the importance of climate (i.e., not land use) factors as drivers of abundance. We used partial least squares regression (PLSR) to evaluate the potential importance of changes in land use and climate variables. Trends in western monarch abundance were more strongly associated with land use variables than climate variables. Conclusions about importance of climate and land use variables were robust to changes in PLSR model structure. However, individual variables were too collinear to unambiguously separate their effects. We compared these conclusions to the more widely used technique of multiple regression, followed by multi‐model inference (MRMI). Naïve interpretation of MRMI results could be misleading, if collinearity were not taken into account. MRMI was also highly sensitive to variation in model construction. Our results suggest a two‐pronged approach to monarch conservation, specifically, starting efforts now to restore habitat, while also using experiments to more clearly delineate separate effects of climate and land use factors. They also demonstrate the utility of PLSR, a technique that is growing in use but is still relatively under‐appreciated in conservation biology. [ABSTRACT FROM AUTHOR]