The demographic causes of population change vary across four decades in a long-lived shorebird

Understanding which factors cause populations to decline begins with identifying which parts of the life cycle, and which vital rates, have changed over time. However, in a world where humans are altering the environment both rapidly and in different ways, the demographic causes of decline likely vary over time. Identifying temporal variation in demographic causes of decline is crucial to assure that conservation actions target current and not past threats. However, this has rarely been studied as it requires long time series. Here we investigate how the demography of a long-lived shorebird (the Eurasian Oystercatcher Haematopus ostralegus) has changed in the past four decades, resulting in a shift from stable dynamics to strong declines (−9% per year), and recently back to a modest decline. Since individuals of this species are likely to respond differently to environmental change, we captured individual heterogeneity through three state variables: age, breeding status, and lay date (using integral projection models). Timing of egg-laying explained significant levels of variation in reproduction, with a parabolic relationship of maximal productivity near the average lay date. Reproduction explained most variation in population growth rates, largely due to poor nest success and hatchling survival. However, the demographic causes of decline have also been in flux over the last three decades: hatchling survival was low in the 2000s but improved in the 2010s, while adult survival declined in the 2000s and remains low today. Overall, the joint action of several key demographic variables explain the decline of the oystercatcher, and improvements in a single vital rate cannot halt the decline. Conservations actions will thus need to address threats occurring at different stages of the oystercatcher's life cycle. The dynamic nature of the threat landscape is further supported by the finding that the average individual no longer has the highest performance in the population, and emphasizes how individual heterogeneity in vital rates can play an important role in modulating population growth rates. Our results indicate that understanding population decline in the current era requires disentangling demographic mechanisms, individual variability, and their changes over time

Development of a Prediction Model to Identify Children at Risk of Future Developmental Delay at Age 4 in a Population-Based Setting

Our aim was to develop a prediction model for infants from the general population, with easily obtainable predictors, that accurately predicts risk of future developmental delay at age 4 and then assess its performance. Longitudinal cohort data were used (N = 1983), including full-term and preterm children. Development at age 4 was assessed using the Ages and Stages Questionnaire. Candidate predictors included perinatal and parental factors as well as growth and developmental milestones during the first two years. We applied multiple logistic regression with backwards selection and internal validation, and we assessed calibration and discriminative performance (i.e., area under the curve (AUC)). The model was evaluated in terms of sensitivity and specificity at several cut-off values. The final model included sex, maternal educational level, pre-existing maternal obesity, several milestones (smiling, speaking 2–3 word sentences, standing) and weight for height z score at age 1. The fit was good, and the discriminative performance was high (AUC: 0.837). Sensitivity and specificity were 73% and 80% at a cut-off probability of 10%. Our model is promising for use as a prediction tool in community-based settings. It could aid to identify infants in early life (age 2) with increased risk of future developmental problems at age 4 that may benefit from early interventions

State-dependent environmental sensitivity of reproductive success and survival in a shorebird

To understand the consequences of anthropogenic and environmental changes for wildlife populations, it is important to study how individuals differ in their sensitivity to environmental change, and whether this depends on individual characteristics. An individual’s reproductive performance may provide an integrative, unidimensional proxy of an individual’s characteristics. In this study, we define an individual’s characteristics by three such reproductive states, namely successful-, failed-, and non-breeders in the previous year. We used a 16-year dataset of individually marked breeding Eurasian Oystercatchers Haematopus ostralegus to examine the inter-annual fluctuations in reproductive success and survival among breeding states, and their state-dependent sensitivity to environmental conditions. Environmental conditions included available biomass of the main prey species of breeding Oystercatchers (Ragworm, Baltic Tellin and Lugworm), tidal height which reflects one of the main causes of nest loss (flooding) and conditions that may impact the energetic requirements during incubation, such as temperature. We also included environmental variables measured in winter, including available biomass of the main winter prey species (Blue Mussel and Common Cockle) along with factors that may affect food availability and energetic requirements for homeostasis, namely bivalve weight loss, windchill, winter severity, and precipitation. Breeding birds that were successful the previous year had higher survival, and were more likely to remain successful, than failed- and non-breeders. The effects of environmental conditions acted in the same direction on reproductive success but had opposite effects on survival among the three breeding states, especially for windchill and Blue Mussel biomass. The contrasting state-dependent effects of the environment on survival thus averaged out when examining consequences for lifetime reproductive nest success (LRnS); instead LRnS was largely influenced by environmental conditions acting upon reproduction. Our study indicates that an individual’s previous breeding state provides an integrative measure of heterogeneity in individuals’ sensitivity of reproduction and survival to environmental change. Incorporating previous breeding state as a source of individual heterogeneity in population modelling may improve predictions of future population dynamics in a rapidly changing world

Conceptualizing and quantifying body condition using structural equation modelling:A user guide

Body condition is an important concept in behaviour, evolution and conservation, commonly used as a proxy of an individual's performance, for example in the assessment of environmental impacts. Although body condition potentially encompasses a wide range of health state dimensions (nutritional, immune or hormonal status), in practice most studies operationalize body condition using a single (univariate) measure, such as fat storage. One reason for excluding additional axes of variation may be that multivariate descriptors of body condition impose statistical and analytical challenges. Structural equation modelling (SEM) is used in many fields to study questions relating multidimensional concepts, and we here explain how SEM is a useful analytical tool to describe the multivariate nature of body condition. In this ‘Research Methods Guide’ paper, we show how SEM can be used to resolve different challenges in analysing the multivariate nature of body condition, such as (a) variable reduction and conceptualization, (b) specifying the relationship of condition to performance metrics, (c) comparing competing causal hypothesis and (d) including many pathways in a single model to avoid stepwise modelling approaches. We illustrated the use of SEM on a real-world case study and provided R-code of worked examples as a learning tool. We compared the predictive power of SEM with conventional statistical approaches that integrate multiple variables into one condition variable: multiple regression and principal component analyses. We show that model performance on our dataset is higher when using SEM and led to more accurate and precise estimates compared to conventional approaches. We encourage researchers to consider SEM as a flexible framework to describe the multivariate nature of body condition and thus understand how it affects biological processes, thereby improving the value of body condition proxies for predicting organismal performance. Finally, we highlight that it can be useful for other multidimensional ecological concepts as well, such as immunocompetence, oxidative stress and environmental conditions

Dynamic prediction model to identify young children at high risk of future overweight: Development and internal validation in a cohort study

Background: Primary prevention of overweight is to be preferred above secondary prevention, which has shown moderate effectiveness. Objective: To develop and internally validate a dynamic prediction model to identify young children in the general population, applicable at every age between birth and age 6, at high risk of future overweight (age 8). Methods: Data were used from the Prevention and Incidence of Asthma and Mite Allergy birth cohort, born in 1996 to 1997, in the Netherlands. Participants for whom data on the outcome overweight at age 8 and at least three body mass index SD scores (BMI SDS) at the age of ≥3 months and ≤6 years were available, were included (N = 2265). The outcome of the prediction model is overweight (yes/no) at age 8 (range 7.4-10.5 years), defined according to the sex- and age-specific BMI cut-offs of the International Obesity Task Force. Results: After backward selection in a Generalized Estimating Equations analysis, the prediction model included the baseline predictors maternal BMI, paternal BMI, paternal education, birthweight, sex, ethnicity and indoor smoke exposure; and the longitudinal predictors BMI SDS, and the linear and quadratic terms of the growth curve describing a child's BMI SDS development over time, as well as the longitudinal predictors' interactions with age. The area under the curve of the model after internal validation was 0.845 and Nagelkerke R2 was 0.351. Conclusions: A dynamic prediction model for overweight was developed with a good predictive ability using easily obtainable predictor information. External validation is needed to confirm that the model has potential for use in practice

Love thy neighbour?-Spatial variation in density dependence of nest survival in relation to predator community

Aim: In many species, density-dependent effects on reproduction are an important driver of population dynamics. However, it is rarely considered that the direction of density dependence is expected to vary over space and time depending on anti-predator behaviour and predator community. Aggregation may allow for effective group mobbing against avian nest predators while aggregation may also attract mammalian predators, causing negative density dependence. We aim to quantify spatial variation in the effect of conspecific breeding density on nest survival in a mobbing bird species (Eurasian oystercatcher; Haematopus ostralegus) and identify whether this variation in density dependence can be explained by the predator community. Location: Country-wide (The Netherlands). Methods: We integrated reproductive data with breeding territory maps of Eurasian oystercatchers and occupancy maps of avian and mammalian predator species across the Netherlands for a 10-year period. Results: Spatial variation in the composition of the predator community explained the effects of neighbour density, showing decreasing nest survival when both conspecific density and mammalian dominance increased. Also, heterospecific density (from breeding godwits and lapwing) has an additional effect on the oystercatcher nest survival. Strikingly, this pattern did not extend to mammal-free island populations. Main conclusions: Our study provides evidence that both the strength and sign of density dependence can vary spatially within species, implying that it is dangerous to generalize results from a single local population to large-scale management implications and modelling exercises. The study also suggests that conservation actions that aim to attract breeding birds should be prioritized in areas with fewer mammalian predators, but this idea requires further testing on island populations

Childhood prediction models for hypertension later in life:A systematic review

BACKGROUND: Hypertension, even during childhood, increases the risk of developing atherosclerosis and cardiovascular disease. Therefore, starting prevention of hypertension early in the life course could be beneficial. Prediction models might be useful for identifying children at increased risk of developing hypertension, which may enable targeted primordial prevention of cardiovascular disease. OBJECTIVE: To provide an overview of childhood prediction models for future hypertension. METHODS: Embase and Medline were systematically searched. Studies were included that were performed in the general population, and that reported on development or validation of a multivariable model for children to predict future high blood pressure, prehypertension or hypertension. Data were extracted using the CHARMS checklist for prediction modelling studies. RESULTS: Out of 12 780 reviewed records, six studies were included in which 18 models were presented. Five studies predicted adulthood hypertension, and one predicted adolescent prehypertension/hypertension. BMI and current blood pressure were most commonly included as predictors in the final models. Considerable heterogeneity existed in timing of prediction (from early childhood to late adolescence) and outcome measurement. Important methodological information was often missing, and in four studies information to apply the model in new individuals was insufficient. Reported area under the ROC curves ranged from 0.51 to 0.74. As none of the models were validated, generalizability could not be confirmed. CONCLUSION: Several childhood prediction models for future hypertension were identified, but their value for practice remains unclear because of suboptimal methods, limited information on performance, or the lack of external validation. Further validation studies are indicated.This is an open access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal. http://creativecommons.org/licenses/by-nc-nd/4.0

Childhood prediction models for hypertension later in life: a systematic review.

Abstract BACKGROUND: Hypertension, even during childhood, increases the risk of developing atherosclerosis and cardiovascular disease. Therefore, starting prevention of hypertension early in the life course could be beneficial. Prediction models might be useful for identifying children at increased risk of developing hypertension, which may enable targeted primordial prevention of cardiovascular disease. OBJECTIVE: To provide an overview of childhood prediction models for future hypertension. METHODS: Embase and Medline were systematically searched. Studies were included that were performed in the general population, and that reported on development or validation of a multivariable model for children to predict future high blood pressure, prehypertension or hypertension. Data were extracted using the CHARMS checklist for prediction modelling studies. RESULTS: Out of 12 780 reviewed records, six studies were included in which 18 models were presented. Five studies predicted adulthood hypertension, and one predicted adolescent prehypertension/hypertension. BMI and current blood pressure were most commonly included as predictors in the final models. Considerable heterogeneity existed in timing of prediction (from early childhood to late adolescence) and outcome measurement. Important methodological information was often missing, and in four studies information to apply the model in new individuals was insufficient. Reported area under the ROC curves ranged from 0.51 to 0.74. As none of the models were validated, generalizability could not be confirmed. CONCLUSION: Several childhood prediction models for future hypertension were identified, but their value for practice remains unclear because of suboptimal methods, limited information on performance, or the lack of external validation. Further validation studies are indicated

Dynamic prediction of childhood high blood pressure in a population-based birth cohort: a model development study

OBJECTIVES: To develop a dynamic prediction model for high blood pressure at the age of 9-10 years that could be applied at any age between birth and the age of 6 years in community-based child healthcare. DESIGN, SETTING AND PARTICIPANTS: Data were used from 5359 children in a population-based prospective cohort study in Rotterdam, the Netherlands. OUTCOME MEASURE: High blood pressure was defined as systolic and/or diastolic blood pressure ≥95th percentile for gender, age and height. Using multivariable pooled logistic regression, the predictive value of characteristics at birth, and of longitudinal information on the body mass index (BMI) of the child until the age of 6 years, was assessed. Internal validation was performed using bootstrapping. RESULTS: 227 children (4.2%) had high blood pressure at the age of 9-10 years. Final predictors were maternal hypertensive disease during pregnancy, maternal educational level, maternal prepregnancy BMI, child ethnicity, birth weight SD score (SDS) and the most recent BMI SDS. After internal validation, the area under the receiver operating characteristic curve ranged from 0.65 (prediction at age 3 years) to 0.73 (prediction at age 5-6 years). CONCLUSIONS: This prediction model may help to monitor the risk of developing high blood pressure in childhood which may allow for early targeted primordial prevention of cardiovascular disease

GPS tracking data of Eurasian oystercatchers (Haematopus ostralegus) from the Netherlands and Belgium

We describe six datasets that contain GPS and accelerometer data of 202 Eurasian oystercatchers (Haematopus ostralegus) spanning the period 2008–2021. Birds were equipped with GPS trackers in breeding and wintering areas in the Netherlands and Belgium. We used GPS trackers from the University of Amsterdam Bird Tracking System (UvA-BiTS) for several study purposes, including the study of space use during the breeding season, habitat use and foraging behaviour in the winter season, and impacts of human disturbance. To enable broader usage, all data have now been made open access. Combined, the datasets contain 6.0 million GPS positions, 164 million acceleration measurements and 7.0 million classified behaviour events (i.e., flying, walking, foraging, preening, and inactive). The datasets are deposited on the research repository Zenodo, but are also accessible on Movebank and as down-sampled occurrence datasets on the Global Biodiversity Information Facility (GBIF) and Ocean Biodiversity Information System (OBIS)