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Animal ecologists frequently quantify variance in hierarchically structured traits in wild populations. Importantly, phenotypic plasticity within the period of measurement can modify the trait of interest in response to various unmeasured, temporally or spatially changeable, environmental conditions. Non‐random sampling among units of the random effect (e.g. individuals) regarding the environment at issue may lead to estimates of the variance among (σ̂I2) or within (σ̂W2) such units that conflate several types of processes. This mixing of underlying biology can affect interpretations of the random effect variance. Here, we explore the conditions leading to this situation and assess potential solutions when relevant information is missing. We simulated a trait's phenotypic values that depended on the environmental variable, and individuals that differed in their deviation to the mean population phenotype (random intercepts). We also simulated different types of variation in an environmental variable that was either shared or specific to each individual. We then varied the repeatability in the timing of sampling (RIS2) and analysed simulated datasets using linear mixed‐effect models with different fixed‐ and random‐effect structures. In the presence of unmeasured environmental factors, the estimated among‐individual variance (σ̂I2) contained a larger signature of the current environment as the strength of the temporal autocorrelation and the repeatability in the timing of sampling (RIS2) increased. For low to moderate values of RIS2 (e.g. <60% of the total variance in our simulations) the risk of pre‐study and within‐study effects conflating estimates of variance components was low and could easily be corrected with a model including period or individual‐period combination as random effects. Higher RIS2 led to an increase in conflating effects that were difficult to correct. Our study shows the importance of limiting the variance among individuals in the timing structure of sampling (RIS2). We recommend researchers estimate RIS2 and report it in papers. Finally, RIS2 can be limited by sampling all individuals in the same period, or sensitivity analyses could be conducted by removing extreme sampling dates at the analysis stage to reduce RIS2.

Aim: There is global consensus that marine protected areas offer a plethora of benefits to the biodiversity within and around them. Nevertheless, many organisms threatened by human impacts also find shelter in unexpected or informally protected places. For coral reef organisms, refuges can be tourist resorts implementing local environment-friendly bottom-up management strategies. We used the coral reef ecosystem as a model to test whether such practices have positive effects on the biodiversity associated with de facto protected areas. Location: North Ari Atoll, Maldives. Methods: We modelled the effects of the environment and three human management regimes (tourist resorts, uninhabited and local community islands) on the abundance and diversity of echinoderms and commercially important fish species, the per cent cover of reef benthic organisms (corals, calcareous coralline algae, turf and macroalgae) and the proportion of coral disease. We used multivariate techniques to assess the differences between reef components among the management regimes. Results: Reefs varied between the management regimes. A positive \"resort effect\" was found on sessile benthic organisms, with good coral cover and significantly less algae at resort islands. Corals were larger and had fewer diseases in uninhabited islands. Minor \"resort effect\" was detected on motile species represented by commercial fish and echinoderms. Main conclusions: In countries where natural biodiversity strongly sustains the tourist sector and where local populations rely on natural resources, a balance between tourism development, local extraction practices and biodiversity conservation is necessary. The presence of eco-friendly managed resorts, which practices would need to be certified on the long term, is beneficial to protect certain organisms. House reefs around resorts could therefore provide areas adding to existing marine protected areas, while marine protection efforts in local community islands should focus on improving fishing management.

Highly active antiretroviral therapy (HAART) has proved efficient in increasing CD4 counts in many randomized clinical trials. Because randomized trials have some limitations (e.g., short duration, highly selected subjects), it is interesting to assess the effect of treatments using observational studies. This is challenging because treatment is started preferentially in subjects with severe conditions. This general problem had been treated using Marginal Structural Models (MSM) relying on the counterfactual formulation. Another approach to causality is based on dynamical models. We present three discrete-time dynamic models based on linear increments models (LIM): the first one based on one difference equation for CD4 counts, the second with an equilibrium point, and the third based on a system of two difference equations, which allows jointly modeling CD4 counts and viral load. We also consider continuous-time models based on ordinary differential equations with non-linear mixed effects (ODE-NLME). These mechanistic models allow incorporating biological knowledge when available, which leads to increased statistical evidence for detecting treatment effect. Because inference in ODE-NLME is numerically challenging and requires specific methods and softwares, LIM are a valuable intermediary option in terms of consistency, precision, and complexity. We compare the different approaches in simulation and in illustration on the ANRS CO3 Aquitaine Cohort and the Swiss HIV Cohort Study.

Conservation policies often take for granted the importance of protected areas for supplying ecosystem services. The first edition of the State of Protected and Conserved Areas in Eastern and Southern Africa report contained limited information on ecosystem services, so for the 2nd edition we statistically compared 561 standardized economic values of various types of ecosystem services inside and outside of protected areas. We found that data from local and sub‐national case studies in the Ecosystem Service Valuation Database were biased geographically, highlighting major evidence gaps for most of the region. For well‐studied countries (Botswana, Ethiopia, Kenya, South Africa, Tanzania, Uganda), the value of ecosystem services varied considerably across different types of services but were—on average—three to six times higher outside protected areas. This trend was not universal, however, given that opportunities for recreation and tourism tended to be higher within protected areas. Combined, these findings suggest that conservation authorities across Eastern and Southern Africa (1) prioritize ecosystem service valuation studies; (2) expand the focus of ecosystem service policies to include wider landscapes beyond protected area boundaries; and (3) avoid generic assumptions about ecosystem services by identifying the services that are most compatible with the broader goals of protected areas. This study reports on an analysis of the value of ecosystem services in protected areas in Eastern and Southern Africa. Although data were biased geographically, ecosystem services in countries with sufficient data were generally more valuable outside of protected areas. However, certain types of ecosystem services were more valuable in protected areas, suggesting that more nuanced approaches are needed to identify types of services most compatible with the broader goals of protection.

We developed predictive models of coral reef fish species richness and abundance that account for both broad-scale environmental gradients and fine-scale biotic processes, such as dispersal, and we compared the importance of absolute geographical location (i.e. geographical coordinates) versus relative geographical location (i.e. distance to domain boundaries). Great Barrier Reef, Australia. Four annual surveys of coral reef fishes were combined with a 0.01°-resolution grid of environmental variables including depth, sea surface temperature, salinity and nutrient concentrations. A principal component-based method was developed to select candidate predictors from a large number of correlated variables. Generalized linear mixed-effects models (GLMMs) were used to gauge the respective importance of the different spatial and environmental predictors. An error covariance matrix was included in the models to account for spatial autocorrelation. (1) Relative geographical descriptors, represented by distances to the coast and to the barrier reef, provided the highest-ranked single model of species richness and explained up to 36.8% of its deviance. (2) Accounting for spatial autocorrelation doubled the deviance in abundance explained to 71.9%. Sea surface temperature, salinity and nitrate concentrations were also important predictors of abundance. Spatially explicit predictions of species richness and abundance were robust to variation in the spatial scale considered during model calibration. This study demonstrates that distance-to-domain boundaries (i.e. relative geographical location) can offer an ecologically relevant alternative to geographical coordinates (i.e. absolute geographical location) when predicting biodiversity patterns, providing a proxy for multivariate and complex environmental processes that are often difficult or expensive to estimate.

Ecological restoration aims at recovering biodiversity in degraded ecosystems, and it is commonly assessed via species richness. However, it is unclear whether increasing species richness in a site also recovers its functional diversity (FD), which has been shown to be a better representation of ecosystem functioning. We conducted a quantitative synthesis of 30 restoration projects and tested whether restoration improves FD. We compared actively and passively restored sites with degraded and reference sites with respect to four key measures of FD (functional richness, evenness, dispersion, and turnover) and two measures of species diversity (richness and evenness). We separately analyzed longitudinal studies (which monitor degraded, reference, and restored sites through time) and space-for-time substitutions (which compare at one point in time degraded and reference sites with restored sites of different ages). Space-for-time studies suggested that species diversity and FD improved over time. However, replicated longitudinal data showed no sustained benefits of active or passive restoration for FD measures, relative to degraded sites. This could suggest that the positive results in space-for-time designs may have been unreliable, but the relatively short duration of longitudinal studies suggests a need for longer-term longitudinal research to robustly demonstrate the absence of any effect. These differences across study designs may explain the variable results found in recent studies directly measuring the response of FD to restoration. We recommend that future assessments of ecological community dynamics include control sites in monitoring, to ensure that the consequences of treatments, including but not limited to restoration, are correctly partitioned from unassisted temporal changes.

Individual trees, not forests, respond to climate. Such an individual‐scale approach has seldom been used to retrospectively track the radial growth responses of trees to climate in dendrochronology. The aim of this study was to adopt this individual view to retrospectively assess tree sensitivity to climate warming, and to evaluate and compare the potential drivers of tree growth responses to climate acting at species, site and individual scales. Following a dendroecological framework, we sampled a network of 29 Pinus uncinata forests in NE Spain and obtained tree‐ring widths series from 642 trees. Individual features as northness, elevation, slope, basal area, sapwood area, tree height and tree age were used to evaluate the potential drivers of tree growth responses to climate. The analysed data set includes diverse ecological and biogeographical conditions. The tree growth responses to climate were assessed by relating growth indices to climatic variables using linear‐mixed effects models. Maximum November temperatures during the year prior to tree‐ring formation enhanced P. uncinata growth mainly in mid‐elevation sites, whereas at higher elevations growth was more dependent on the positive effect of warmer minimum May temperatures during the year of tree‐ring formation. Current June precipitation was the positive main climatic driver of growth in sites prone to water deficit such as the southernmost limit of the species distribution area or very steep sites. Elevation was the main factor controlling how much growth variability is explained by climate at the site and tree scales. Climate warming was more intense during the early 20th century, when the importance of elevation as an indirect modulator of growth declined as compared with the late 20th century. Synthesis. The individual‐scale approach taken in this study allowed detecting that trees growing at southern and low‐elevation sites were the most negatively affected by warm and dry summer conditions. Our results emphasize that both (i) an individual‐scale approach to quantify tree growth responses to climate and (ii) a detailed evaluation of the potential biotic and abiotic drivers of those individual responses are necessary to understand climate sensitivity of trees.

Aphids represent a significant challenge to food production. The Rothamsted Insect Survey (RIS) runs a network of 12·2‐m suction‐traps throughout the year to collect migrating aphids. In 2014, the RIS celebrated its 50th anniversary. This paper marks that achievement with an extensive spatiotemporal analysis and the provision of the first British annotated checklist of aphids since 1964. Our main aim was to elucidate mechanisms that advance aphid phenology under climate change and explain these using life‐history traits. We then highlight emerging pests using accumulation patterns. Linear and nonlinear mixed‐effect models estimated the average rate of change per annum and effects of climate on annual counts, first and last flights and length of flight season since 1965. Two climate drivers were used: the accumulated day degrees above 16 °C (ADD16) indicated the potential for migration during the aphid season; the North Atlantic Oscillation (NAO) signalled the severity of the winter before migration took place. All 55 species studied had earlier first flight trends at rate of β = −0·611 ± SE 0·015 days year⁻¹. Of these species, 49% had earlier last flights, but the average species effect appeared relatively stationary (β = −0·010 ± SE 0·022 days year⁻¹). Most species (85%) showed increasing duration of their flight season (β = 0·336 ± SE 0·026 days year⁻¹), even though only 54% increased their log annual count (β = 0·002 ± SE <0·001 year⁻¹). The ADD16 and NAO were shown to drive patterns in aphid phenology in a spatiotemporal context. Early in the year when the first aphids were migrating, the effect of the winter NAO was highly significant. Further into the year, ADD16 was a strong predictor. Latitude had a near linear effect on first flights, whereas longitude produced a generally less‐clear effect on all responses. Aphids that are anholocyclic (permanently parthenogenetic) or are monoecious (non‐host‐alternating) were advancing their phenology faster than those that were not. Climate drives phenology and traits help explain how this takes place biologically. Phenology and trait ecology are critical to understanding the threat posed by emerging pests such as Myzus persicae nicotianae and Aphis fabae cirsiiacanthoidis, as revealed by the species accumulation analysis.

We consider inference for misaligned multivariate functional data that represents the same underlying curve, but where the functional samples have systematic differences in shape. We introduce a class of generally applicable models where warping effects are modelled through non-linear transformation of latent Gaussian variables and systematic shape differences are modelled by Gaussian processes. To model cross-covariance between sample co-ordinates we propose a class of low dimensional cross-covariance structures that are suitable for modelling multivariate functional data. We present a method for doing maximum likelihood estimation in the models and apply the method to three data sets. The first data set is from a motion tracking system where the spatial positions of a large number of body markers are tracked in three dimensions over time. The second data set consists of longitudinal height and weight measurements for Danish boys. The third data set consists of three-dimensional spatial hand paths from a controlled obstacle avoidance experiment. We use the method to estimate the cross-covariance structure and use a classification set-up to demonstrate that the method outperforms state of the art methods for handling misaligned curve data.

1. There is growing recognition as to the importance of extreme climatic events (ECEs) in determining changes in species populations. In fact, it is often the extent of climate variability that determines a population's ability to persist at a given site. 2. This study examined the impact of ECEs on the resident UK butterfly species (n = 41) over a 37-year period. The study investigated the sensitivity of butterflies to four extremes (drought, extreme precipitation, extreme heat and extreme cold), identified at the site level, across each species' life stages. Variations in the vulnerability of butterflies at the site level were also compared based on three life-history traits (voltinism, habitat requirement and range). 3. This is the first study to examine the effects of ECEs at the site level across all life stages of a butterfly, identifying sensitive life stages and unravelling the role life-history traits play in species sensitivity to ECEs. 4. Butterfly population changes were found to be primarily driven by temperature extremes. Extreme heat was detrimental during overwintering periods and beneficial during adult periods and extreme cold had opposite impacts on both of these life stages. Previously undocumented detrimental effects were identified for extreme precipitation during the pupal life stage for univoltine species. Generalists were found to have significantly more negative associations with ECEs than specialists. 5. With future projections of warmer, wetter winters and more severe weather events, UK butterflies could come under severe pressure given the findings of this study.