Explore the vast range of titles available.

Numerous grassland experiments have found evidence for a complementarity effect, an increase in productivity with higher plant species richness due to niche partitioning. However, empirical tests of complementarity in natural forests are rare. We conducted a spatially explicit analysis of 518 433 growth records for 274 species from a 50‐ha tropical forest plot to test neighborhood complementarity, the idea that a tree grows faster when it is surrounded by more dissimilar neighbors. We found evidence for complementarity: focal tree growth rates increased by 39.8% and 34.2% with a doubling of neighborhood multi‐trait dissimilarity and phylogenetic dissimilarity, respectively. Dissimilarity from neighbors in maximum height had the most important effect on tree growth among the six traits examined, and indeed, its effect trended much larger than that of the multi‐trait dissimilarity index. Neighborhood complementarity effects were strongest for light‐demanding species, and decreased in importance with increasing shade tolerance of the focal individuals. Simulations demonstrated that the observed neighborhood complementarities were sufficient to produce positive stand‐level biodiversity–productivity relationships. We conclude that neighborhood complementarity is important for productivity in this tropical forest, and that scaling down to individual‐level processes can advance our understanding of the mechanisms underlying stand‐level biodiversity–productivity relationships.

Over the past few years, research on remote monitoring of animal behaviour by means of accelerometers integrated in GPS collars considerably increased. Use of accelerometers allows for long-term fine-scale behavioural measurements, which are extremely useful to study activity patterns. As the values generated by accelerometers are strongly affected by individual factors, season-related environmental effects, and the position of the collar on the animal, comparisons of accelerometer data among different individuals and time-periods may yield misleading results. Researchers have to find an easy-to-use method in order to turn accelerometer data into behavioural data, one which enables them to take into consideration inter- and intra-individual variations. We propose an easy individual-based method, which generates threshold values to distinguish between active and inactive behaviours with no need of direct observation. By treating each animal independently and adopting ad hoc temporal scales, this method is able to take into consideration the influence of individual factor modifications (e.g., body size, collar tightness) on the data recorded by the accelerometer. We validated this approach and showed its potential by testing it with an activity dataset from 26 free-ranging Alpine ibex (Capra ibex). We managed to distinguish between active and inactive behaviours with a high percentage (93%) of correctly classified binary behavioural state. We showed that, when the threshold values are calculated at a large temporal scale, the accuracy decreases and activity pattern predictions may yield misleading results. By adopting the method proposed and by transforming the accelerometer data provided by the collars into time spent active, it may be possible to analyse how the activity levels of the monitored individuals change over the seasons, to appreciate fine variations of individual characteristics, and to compare the activity patterns of different populations as well as of different species.

Modelling physical–biological interactions in the early life of fish is becoming an integral part of theoretical and applied marine ecology. A workshop on ‘Advancements in modelling physical–biological interactions in fish early-life history’ (WKAMF) was held in April 2006 to review recent developments and identify future research directions. Here we provide an overview of the information presented at WKAMF (some of which is published in this Theme Section), discussions that took place at the workshop, and the authors’ perspectives as workshop co-Chairs. The major themes identified at the workshop were the need for enhanced model validation and sensitivity methods and improved understanding of physical and biological processes. Using the appropriate level of model complexity required for each model application is important; developing quantitative consistency of model results with good quality observational data is critical. In addition, improving our prediction of physical processes, such as circulation patterns and turbulence, will advance our knowledge of fish early life, as will a better understanding of biological processes like mortality, behaviour, and energetics. The latter stage-dependent, often species-specific, processes pose particular challenges, although technical advances in field and laboratory observations are likely to result in considerable progress in the near future. Finally, there is a clear requirement for interdisciplinary collaboration between modellers, field scientists and laboratory scientists. Studies receiving input from a wide range of disciplines will increase our understanding of fish early-life ecology and prediction of recruitment to fish populations.

Estimating habitat and spatial associations for wildlife is common across ecological studies and it is well known that individual traits can drive population dynamics and vice versa. Thus, it is commonly assumed that individual- and population-level data should represent the same underlying processes, but few studies have directly compared contemporaneous data representing these different perspectives. We evaluated the circumstances under which data collected from Lagrangian (individual-level) and Eulerian (population-level) perspectives could yield comparable inference to understand how scalable information is from the individual to the population. We used Global Positioning System (GPS) collar (Lagrangian) and camera trap (Eulerian) data for seven species collected simultaneously in eastern Washington (2018–2020) to compare inferences made from different survey perspectives. We fit the respective data streams to resource selection functions (RSFs) and occupancy models and compared estimated habitat- and space-use patterns for each species. Although previous studies have considered whether individual- and population-level data generated comparable information, ours is the first to make this comparison for multiple species simultaneously and to specifically ask whether inferences from the two perspectives differed depending on the focal species. We found general agreement between the predicted spatial distributions for most paired analyses, although specific habitat relationships differed. We hypothesize the discrepancies arose due to differences in statistical power associated with camera and GPS-collar sampling, as well as spatial mismatches in the data. Our research suggests data collected from individual-based sampling methods can capture coarse population-wide patterns for a diversity of species, but results differ when interpreting specific wildlife-habitat relationships.

Forecasting the dynamics of influenza outbreaks could be useful for decision‐making regarding the allocation of public health resources. Reliable forecasts could also aid in the selection and implementation of interventions to reduce morbidity and mortality due to influenza illness. This paper reviews methods for influenza forecasting proposed during previous influenza outbreaks and those evaluated in hindsight. We discuss the various approaches, in addition to the variability in measures of accuracy and precision of predicted measures. PubMed and Google Scholar searches for articles on influenza forecasting retrieved sixteen studies that matched the study criteria. We focused on studies that aimed at forecasting influenza outbreaks at the local, regional, national, or global level. The selected studies spanned a wide range of regions including USA, Sweden, Hong Kong, Japan, Singapore, United Kingdom, Canada, France, and Cuba. The methods were also applied to forecast a single measure or multiple measures. Typical measures predicted included peak timing, peak height, daily/weekly case counts, and outbreak magnitude. Due to differences in measures used to assess accuracy, a single estimate of predictive error for each of the measures was difficult to obtain. However, collectively, the results suggest that these diverse approaches to influenza forecasting are capable of capturing specific outbreak measures with some degree of accuracy given reliable data and correct disease assumptions. Nonetheless, several of these approaches need to be evaluated and their performance quantified in real‐time predictions.

Oral baiting is used to deliver vaccines to wildlife to prevent, control, and eliminate infectious diseases. A central challenge is how to spatially distribute baits to maximize encounters by target animal populations, particularly in urban and suburban areas where wildlife such as raccoons (Procyon lotor) are abundant and baits are delivered along roads. Methods from movement ecology that quantify movement and habitat selection could help to optimize baiting strategies by more effectively targeting wildlife populations across space. We developed a spatially explicit, individual-based model of raccoon movement and oral rabies vaccine seroconversion to examine whether and when baiting strategies that match raccoon movement patterns perform better than currently used baiting strategies in an oral rabies vaccination zone in greater Burlington, Vermont, USA. Habitat selection patterns estimated from locally radio-collared raccoons were used to parameterize movement simulations. We then used our simulations to estimate raccoon population rabies seroprevalence under currently used baiting strategies (actual baiting) relative to habitat selection-based baiting strategies (habitat baiting). We conducted simulations on the Burlington landscape and artificial landscapes that varied in heterogeneity relative to Burlington in the proportion and patch size of preferred habitats. We found that the benefits of habitat baiting strongly depended on the magnitude and variability of raccoon habitat selection and the degree of landscape heterogeneity within the baiting area. Habitat baiting improved seroprevalence over actual baiting for raccoons characterized as habitat specialists but not for raccoons that displayed weak habitat selection similar to radiocollared individuals, except when baits were delivered off roads where preferred habitat coverage and complexity was more pronounced. In contrast, in artificial landscapes with either more strongly juxtaposed favored habitats and/or higher proportions of favored habitats, habitat baiting performed better than actual baiting, even when raccoons displayed weak habitat preferences and where baiting was constrained to roads. Our results suggest that habitat selection-based baiting could increase raccoon population seroprevalence in urban–suburban areas, where practical, given the heterogeneity and availability of preferred habitat types in those areas. Our novel simulation approach provides a flexible framework to test alternative baiting strategies in multiclass landscapes to optimize bait-distribution strategies.

Aim Mounting ecological impacts of invasive species on freshwater ecosystems are among the greatest challenges confronting ecologists and decision‐makers in conserving biodiversity and ecosystem function. Tools to slow the proliferation of aquatic invasive species are still needed to guide the allocation of limited resources more effectively and efficiently once a species is already established. Here we develop mechanistic models to recreate the invasion history of the rusty crayfish Faxonius rusticus in the John Day River (JDR) basin, forecast its future distribution, and evaluate the management efficiency of, and trade‐offs among, population control actions. Location John Day River Basin, Oregon. Methods The spread and control of rusty crayfish in the JDR was simulated with a spatially explicit individual‐based model (SEIBM) whereby the life history of each crayfish in the population is modelled in response to environmental conditions that vary across space and time. The model was calibrated by comparing modelled rusty crayfish spread throughout the JDR to known occurrences according to three comprehensive surveys. Results Our model accurately reproduced historical rusty crayfish distribution data for 2005, 2010, and 2016 with a specificity and sensitivity of ~80%. Leveraging this realistic model of the spread of rusty crayfish, we show that rapid management actions to the initial invasion would have resulted in an opportunity to slow the spread of rusty crayfish. We instead predict that rusty crayfish will reach the mainstem of the Columbia River by 2025, at which our model predicts that the crayfish population will number on the order of 108 individuals over more than 1100 km of river throughout the watershed and progress into critical salmon spawning habitat. Main conclusion This study demonstrates that SEIBMs can provide unique insight into the future distribution of aquatic invasive species and concretely support decision‐makers in choosing an optimal control strategy.

The importance of thermal refuges in a rapidly warming world is particularly evident for migratory species, where individuals encounter a wide range of conditions throughout their lives. In this study, we used a spatially explicit, individual‐based simulation model to evaluate the buffering potential of cold‐water thermal refuges for anadromous salmon and trout (Oncorhynchus spp.) migrating upstream through a warm river corridor that can expose individuals to physiologically stressful temperatures. We considered upstream migration in relation to migratory phenotypes that were defined in terms of migration timing, spawn timing, swim speed, and use of cold‐water thermal refuges. Individuals with different migratory phenotypes migrated upstream through riverine corridors with variable availability of cold‐water thermal refuges and mainstem temperatures. Use of cold‐water refuges (CWRs) decreased accumulated sublethal exposures to physiologically stressful temperatures when measured in degree‐days above 20, 21, and 22°C. The availability of CWRs was an order of magnitude more effective in lowering accumulated sublethal exposures under current and future mainstem temperatures for summer steelhead than fall Chinook Salmon. We considered two emergent model outcomes, survival and percent of available energy used, in relation to thermal heterogeneity and migratory phenotype. Mean percent energy loss attributed to future warmer mainstem temperatures was at least two times larger than the difference in energy used in simulations without CWRs for steelhead and salmon. We also found that loss of CWRs reduced the diversity of energy‐conserving migratory phenotypes when we examined the variability in entry timing and travel time outside of CWRs in relation to energy loss. Energy‐conserving phenotypic space contracted by 7%–23% when CWRs were unavailable under the current thermal regime. Our simulations suggest that, while CWRs do not entirely mitigate for stressful thermal exposures in mainstem rivers, these features are important for maintaining a diversity of migration phenotypes. Our study suggests that the maintenance of diverse portfolios of migratory phenotypes and cool‐ and cold‐water refuges might be added to the suite of policies and management actions presently being deployed to improve the likelihood of Pacific salmonid persistence into a future characterized by climate change.

The transition of bacteria from an individualistic to a biofilm lifestyle profoundly alters their biology. During biofilm development, the bacterial cell‐cell adhesions are a major determinant of initial microcolonies, which serve as kernels for the subsequent microscopic and mesoscopic structure of the biofilm, and determine the resulting functionality. In this study, the significance of bacterial cell‐cell adhesion dynamics on bacterial aggregation and biofilm maturation is elucidated. Using photoswitchable adhesins between bacteria, modifying the dynamics of bacterial cell‐cell adhesions with periodic dark‐light cycles is systematic. Dynamic cell‐cell adhesions with liquid‐like behavior improve bacterial aggregation and produce more compact microcolonies than static adhesions with solid‐like behavior in both experiments and individual‐based simulations. Consequently, dynamic cell‐cell adhesions give rise to earlier quorum sensing activation, better intermixing of different bacterial populations, improved biofilm maturation, changes in the growth of cocultures, and higher yields in fermentation. The here presented approach of tuning bacterial cell‐cell adhesion dynamics opens the door for regulating the structure and function of biofilms and cocultures with potential biotechnological applications. In this study, the vital role of dynamic bacterial cell‐cell adhesion in biofilm formation is highlighted. Using photoswitchable adhesins and periodic dark‐light cycles, dynamic adhesions enhance bacterial aggregation, resulting in thicker biofilms, and improved functionality is shown. Manipulating adhesion dynamics with light offers a promising way forward for shaping biofilm structure and function with potential biotechnological applications.

Understanding the complex interplay between contact networks in social host species and individual movement decisions is essential for designing effective disease control strategies in wild animals. We use a spatially explicit eco‐epidemiological individual‐based model to investigate the effect of host movement decisions on disease spread and persistence and reconcile findings with the patterns of a long‐term outbreak dataset. Using alternative mechanistic host movement submodels, in which decisions where to move are affected by either landscape structure or density of conspecifics, we validate simulations of disease spread against the known long‐term patterns of spread of classical swine fever in wild boar in Northern Germany by applying the same sampling scheme as in the field. We compare simulated with observed data using three key metrics: age class distribution of infected hosts, speed of pathogen spread, and spatial distribution patterns of infected individuals. We found two main movement strategies matching the observed pathogen spread and spatial patterns: correlated, habitat‐driven movement and competition‐driven movement. Furthermore, the only movement strategy that was able to recreate the observed trend in the age class distribution of infected host individuals was the implicit movement, purely based on host density. Our results show the significant impact of habitat composition and host population density on disease outbreak dynamics.