A practical guide to selecting models for exploration, inference, and prediction in ecology

Effects of perceptions of forest change and intergroup competition on community-based conservation behaviors

Approximately one quarter of the earth's population directly harvests natural resources to meet their daily needs. These individuals are disproportionately required to alter their behaviors in response to increasing climatic variability and global biodiversity loss. Much of the ever-ambitious global conservation agenda relies on the voluntary uptake of conservation behaviors in such populations. Thus, it is critical to understand how such individuals perceive environmental change and use conservation practices as a tool to protect their well-being. We developed a participatory mapping activity to elicit spatially explicit perceptions of forest change and its drivers across 43 mangrove-dependent communities in Pemba, Tanzania. We administered this activity along with a questionnaire regarding conservation preferences and behaviors to 423 individuals across those 43 communities. We analyzed these data with a set of Bayesian hierarchical statistical models. Perceived cover loss in 50% of a community's mangrove area drove individuals to decrease proposed limits on fuelwood bundles from 2.74 (forest perceived as intact) to 2.37 if participants believed resultant gains in mangrove cover would not be stolen by outsiders. Conversely, individuals who believed their community mangrove forests were at high risk of theft loosened their proposed harvest limits from 1.26 to 2.75 bundles of fuelwood in response to the same perceived forest decline. High rates of intergroup competition and mangrove loss were thus driving a self-reinforcing increase in unsustainable harvesting preferences in community forests in this system. This finding demonstrates a mechanism by which increasing environmental decline may cause communities to forgo conservation practices, rather than adopt them, as is often assumed in much community-based conservation planning. However, we also found that when effective boundaries were present, individuals were willing to limit their own harvests to stem such perceived decline.

Seed dispersal by Martu peoples promotes the distribution of native plants in arid Australia

Commensal relationships between wild plants and their dispersers play a key ecological and evolutionary role in community structure and function. While non-human dispersers are often considered critical to plant recruitment, human dispersers have received much less attention, especially when it comes to non-domesticated plants. Australia, as a continent historically characterized by economies reliant on non-domesticated plants, is thus a key system for exploring the ecological role of people as seed dispersers in the absence of agriculture. Here, we utilize a controlled observation research design, employing ecological surveys and ethnographic observations to examine how seed dispersal and landscape burning by Martu Aboriginal people affects the distribution of three preferred plants and one (edible, but non-preferred) control species. Using an information theoretic approach, we find that the three preferred plants show evidence of human dispersal, with the strongest evidence supporting anthropogenic dispersal for the wild bush tomato, Solanum diversiflorum.

The heat is on: Thermoregulatory and evaporative cooling patterns of desert-dwelling bats

For small endotherms inhabiting desert ecosystems, defending body temperatures (Tb) is challenging as they contend with extremely high ambient temperatures (Ta) and limited standing water. In the arid zone, bats may thermoconform whereby Tb varies with Ta, or may evaporatively cool themselves to maintain Tb < Ta. We used an integrative approach that combined both temperature telemetry and flow through respirometry to investigate the ecological and physiological strategies of lesser long-eared bats (Nyctophilus geoffroyi) in Australia's arid zone. We predicted individuals would exhibit desert-adapted thermoregulatory patterns (i.e., thermoconform to prioritise water conservation), and that females would be more conservative with their water reserves for evaporative cooling compared to males. Temperature telemetry data indicated that free-ranging N. geoffroyi were heterothermic (Tskin = 18.9-44.9 °C) during summer and thermoconformed over a wide range of temperatures, likely to conserve water and energy during the day. Experimentally, at high Tas, females maintained significantly lower Tb and resting metabolic rates, despite lower evaporative water loss (EWL) rates compared to males. Females only increased EWL at experimental Ta = 42.5 °C, significantly higher than males (40.7 °C), and higher than any bat species yet recorded. During the hottest day of this study, our estimates suggest the water required for evaporative cooling ranged from 18.3% (females) and 25.5% (males) of body mass. However, if we extrapolate these results to a recent heatwave these values increase to 36.5% and 47.3%, which are likely beyond lethal limits. It appears this population is under selective pressures to conserve water reserves and that these pressures are more pronounced in females than males. Bats in arid ecosystems are threatened by both current and future heatwaves and we recommend future conservation efforts focus on protecting current roost trees and creating artificial standing water sites near vulnerable populations.

Comparison of WAIC and posterior predictive approaches for N-mixture models

Hierarchical models are common for ecological analysis, but determining appropriate model selection methods remains an ongoing challenge. To confront this challenge, a suitable method is needed to evaluate and compare available candidate models. We compared performance of conditional WAIC, a joint-likelihood approach to WAIC (WAICj), and posterior-predictive loss for selecting between candidate N-mixture models. We tested these model selection criteria on simulated single-season N-mixture models, simulated multi-season N-mixture models with temporal auto-correlation, and three case studies of single-season N-mixture models based on eBird data. WAICj proved more accurate than the standard conditional formulation or posterior-predictive loss, even when models were temporally correlated, suggesting WAICj is a robust alternative to model selection for N-mixture models.

Strong and consistent effects of waterbird composition on HPAI H5 occurrences across Europe

Since 2014, highly pathogenic avian influenza (HPAI) H5 viruses of clade 2.3.4.4 have been dominating the outbreaks across Europe, causing massive deaths among poultry and wild birds. However, the factors shaping these broad-scale outbreak patterns, especially those related to waterbird community composition, remain unclear. In particular, we do not know whether these risk factors differ from those of other H5 clades. Addressing this knowledge gap is important for predicting and preventing future HPAI outbreaks. Using extensive waterbird survey datasets from about 6883 sites, we here explored the effect of waterbird community composition on HPAI H5Nx (clade 2.3.4.4) spatial patterns in the 2016/2017 and 2020/2021 epidemics in Europe, and compared it with the 2005/2006 HPAI H5N1 (clade 2.2) epidemic. We showed that HPAI H5 occurrences in wild birds in the three epidemics were strongly associated with very similar waterbird community attributes, which suggested that, in nature, similar interspecific transmission processes operate between the HPAI H5 subtypes or clades. Importantly, community phylogenetic diversity consistently showed a negative association with H5 occurrence in all three epidemics, suggesting a dilution effect of phylogenetic diversity. In contrast, waterbird community variables showed much weaker associations with HPAI H5Nx occurrence in poultry. Our results demonstrate that models based on previous epidemics can predict future HPAI H5 patterns in wild birds, implying that it is important to include waterbird community factors in future HPAI studies to predict outbreaks and improve surveillance activities.

Bat thermoregulation in the heat: seasonal variation in evaporative cooling capacities in four species of European bats

Phenotypic flexibility is an important source of physiological variation in endotherms and plays an integral role in species' response to rapid environmental changes. Studies of phenotypic flexibility have focused on winter acclimatization and cold endurance, and there are fewer data on summer acclimatization and adjustments in heat dissipation capacity, especially in Temperate-Zone species. We used indirect calorimetry and thermometry to test if thermoregulation at high air temperatures (Ta) varies between spring and summer in four species of European vespertilionid bats: Nyctalus noctula, Pipistrellus nathusii, P. pygmaeus, and P. pipistrellus. We measured subcutaneous body temperature (Tsub), evaporative water loss, and resting metabolic rate while exposing bats to a stepped profile of increasing Ta, from 28 °C-48 °C. We predicted that during summer, bats increase heat tolerance and evaporative cooling capacity, to better tolerate hotter Tas. In contrast, we found lower maximum ratios of evaporative heat loss (EHL) to metabolic heat production (MHP) during summer, but no seasonal differences in maximum Ta tolerated or Tsub. The main cause of this seasonal difference in maximum EHL/MHP seems to be from bats increasing EWL more gradually with increasing Ta in summer than spring, particularly in the smaller Pipistrellus species. Therefore, this seasonal variation in heat-dissipation strategies may reflect enhanced water conservation during summer to avoid dehydration, as bats are confined to roosts for longer and hotter days compared to spring.

Insufficient pollinator visitation often limits yield in crop systems worldwide

Declining pollinator populations could threaten global food production, especially if current crop yields are limited by insufficient pollinator visitation to flowers, in a phenomenon referred to as 'pollinator limitation'. Here, we assess the global prevalence of pollinator limitation, explore the risk factors, such as crop type or geographic region, that predict where pollinator limitation is more likely and ask by how much increases in pollinator visitation could improve crop yields. We address these questions using 198,360 plant-pollinator interactions and 2,083 yield measurements from 32 crop species grown in 120 study systems. We find that 28-61% of global crop systems are pollinator limited and that this limitation most frequently occurs in blueberry, coffee and apple crops. For a few datasets, we note that the probability of pollinator limitation decreases with greater forest land cover surrounding a crop field at 1 km, although average effect sizes are small. Finally, we estimate that for those crops we identify as pollinator limited, increasing pollinator visitation at all farms to existing levels observed in the 90th percentile of each study system would close 63% of yield gaps between high- and low-yielding fields. Our findings show variations in sensitivity to pollinator limitation across diverse crop systems and indicate that realistic increases in pollinator visitation could mitigate crop yield shortfalls attributable to pollinator limitation.

A century of statistical Ecology

As data and computing power have surged in recent decades, statistical modeling has become an important tool for understanding ecological patterns and processes. Statistical modeling in ecology faces two major challenges. First, ecological data may not conform to traditional methods, and second, professional ecologists often do not receive extensive statistical training. In response to these challenges, the journal Ecology has published many innovative statistical ecology papers that introduced novel modeling methods and provided accessible guides to statistical best practices. In this paper, we reflect on Ecology's history and its role in the emergence of the subdiscipline of statistical ecology, which we define as the study of ecological systems using mathematical equations, probability, and empirical data. We showcase 36 influential statistical ecology papers that have been published in Ecology over the last century and, in so doing, comment on the evolution of the field. As data and computing power continue to increase, we anticipate continued growth in statistical ecology to tackle complex analyses and an expanding role for Ecology to publish innovative and influential papers, advancing the discipline and guiding practicing ecologists.

Global field collection data confirm an affinity of brown rot fungi for coniferous habitats and substrates

Unlike 'white rot' (WR) wood-decomposing fungi that remove lignin to access cellulosic sugars, 'brown rot' (BR) fungi selectively extract sugars and leave lignin behind. The relative frequency and distribution of these fungal types (decay modes) have not been thoroughly assessed at a global scale; thus, the fate of one-third of Earth's aboveground carbon, wood lignin, remains unclear. Using c. 1.5 million fungal sporocarp and c. 30 million tree records from publicly accessible databases, we mapped and compared decay mode and tree type (conifer vs angiosperm) distributions. Additionally, we mined fungal record metadata to assess substrate specificity per decay mode. The global average for BR fungi proportion (BR/(BR + WR records)) was 13% and geographic variation was positively correlated (R2 = 0.45) with conifer trees proportion (conifer/(conifer + angiosperm records)). Most BR species (61%) were conifer, rather than angiosperm (22%), specialists. The reverse was true for WR (conifer: 19%; angiosperm: 62%). Global BR proportion patterns were predicted with greater accuracy using the relative distributions of individual tree species (R2 = 0.82), rather than tree type. Fungal decay mode distributions can be explained by tree type and, more importantly, tree species distributions, which our data suggest is due to strong substrate specificities.

Cognitive dysfunction in diabetes-related foot complications: A cohort study

Objective

Mild-moderate cognitive impairment has been identified in general diabetes, and early evidence indicates cognitive reductions may be more pronounced in those with diabetes-related foot complications (DRFC). Cognitive difficulties may impede treatment engagement and self-management. This requires further explication to optimise patient care and outcomes. The current study aimed to characterise cognitive function in people with DRFC using comprehensive cognitive measures.

Method

This cross-sectional cohort study recruited 80 adult participants (M age  = 63.38, SD = 11.40, range = 30 - 89) from the Royal Melbourne Hospital Diabetic Foot Unit in Victoria, Australia, all with DRFC. Each completed a comprehensive cognitive battery (memory, attention, executive functions) and scores were calculated using age-matched population norms, where available.

Results

On the majority of tasks, DRFC participants performed significantly worse than age-matched norms, with the largest decrements seen in inhibition control, verbal memory, verbal abstract reasoning and working memory. Small to moderate reductions were also seen in visual learning, verbal fluency, processing speed and premorbid functioning. Demographic (lower education, male gender) and clinical factors (higher HbA1c, macrovascular and microvascular disease, longer diabetes duration) were associated with poorer cognitive functioning.

Conclusions

Marked reductions in cognitive functioning were found in individuals with DRFC, predominantly in the domains of verbal memory and executive functioning. Lower education, male gender and indicators of diabetes severity, such as vascular disease, are associated with heightened risk for poorer cognitive functioning. As DRFCs are a serious complication with devastating outcomes if not successfully managed, cognitive barriers to self-management must be addressed to optimise treatment.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40200-023-01381-4.

Biochemical and molecular biomarkers and their association with anthropogenic chemicals in wintering Manx shearwaters (Puffinus puffinus)

Anthropogenic pollution poses a threat to marine conservation by causing chronic toxic effects. Seabirds have contact throughout their lives with pollutants like plastic, metals, polychlorinated biphenyls (PCBs), and organochlorine pesticides such as hexachlorocyclohexanes (HCHs). We assessed 155 Manx shearwaters (Puffinus puffinus) stranded along the Brazilian coast, analyzing associations between organic pollutants, plastic ingestion, biomarkers (transcript levels of aryl hydrocarbon receptor, cytochrome P450-1A-5 [CYP1A5], UDP-glucuronosyl-transferase [UGT1], estrogen receptor alpha-1 [ESR1], and heat shock protein-70 genes) and enzymes activity (ethoxy-resorufin O-deethylase and glutathione S-transferase [GST]). Plastic debris was found in 29 % of the birds. The transcription of UGT1 and CYP1A5 was significantly associated with hexachlorobenzene (HCB) and PCBs levels. ESR1 was associated with HCB and Mirex, and GST was associated with Drins and Mirex. While organic pollutants affected shearwaters more than plastic ingestion, reducing plastic availability remains relevant as xenobiotics are also potentially adsorbed onto plastics.

Aggressive interactions influence cognitive performance in Western Australian magpies

Extensive research has investigated the relationship between the social environment and cognition, suggesting that social complexity may drive cognitive evolution and development. However, evidence for this relationship remains equivocal. Group size is often used as a measure of social complexity, but this may not capture intraspecific variation in social interactions. Social network analysis can provide insight into the cognitively demanding challenges associated with group living at the individual level. Here, we use social networks to investigate whether the cognitive performance of wild Western Australian magpies (Gymnorhina tibicen dorsalis) is related to group size and individual social connectedness. We quantified social connectedness using four interaction types: proximity, affiliative, agonistic and vocal. Consistent with previous research on this species, individuals in larger groups performed better on an associative learning task. However, social network position was also related to cognitive performance. Individuals receiving aggressive interactions performed better, while those involved in aggressive interactions with more group members performed worse. Overall, this suggests that cognitive performance is related to specific types of social interaction. The findings from this study highlight the value of considering fine-grained metrics of sociality that capture the challenges associated with social life when testing the relationship between the social environment and cognition.

Building pondscapes for amphibian metapopulations

The success of ponds constructed to restore ecological infrastructure for pond-breeding amphibians and benefit aquatic biodiversity depends on where and how they are built. We studied effects of pond and landscape characteristics, including connectivity, on metapopulation dynamics of 12 amphibian species in Switzerland. To understand the determinants of long-term occupancy (here summarized as incidence), environmental effects on both colonization and persistence should be considered. We fitted dynamic occupancy models to 20 years of monitoring data on a pond construction program to quantify effects of pond and landscape characteristics and different connectivity metrics on colonization and persistence probabilities in constructed ponds. Connectivity to existing populations explained dynamics better than structural connectivity metrics, and simple metrics (distance to the nearest neighbor population, population density) were useful surrogates for dispersal kernel-weighted metrics commonly used in metapopulation theory. Population connectivity mediated the persistence of conservation target species in new ponds, suggesting source-sink dynamics in newly established populations. Population density captured this effect well and could be used by practitioners for site selection. Ponds created where there were 2-4 occupied ponds within a radius of ∼0.5 km had >3.5 times higher incidence of target species (median) than isolated ponds. Species had individual preferences regarding pond characteristics, but breeding sites with larger (≥100 m2) total water surface area, that temporarily dried, and that were in surroundings with maximally 50% forest benefitted multiple target species. Pond diversity will foster amphibian diversity at the landscape scale.

An exploratory analysis of the structure of tetranychid and phytoseiid assemblages in walnut orchards in California

Spider mites were considered secondary pests of walnut production in California, under the control of phytoseiid predators. Due to increased importance as walnut pests in recent decades there is renewed interest in the structure and function of the associated phytoseiid assemblage. In this study we report the results from a 3 year survey of the tetranychid and phytoseiid assemblages in walnut orchards in the Central Valley of California. The survey was conducted to determine the range and dominance of web-spinning Tetranychus species present, to investigate the species richness and dominance of the phytoseiid species present, and to explore whether patterns of variation in the relative abundance of phytoseiid species could be explained by one or more explanatory variables. Tetranychus urticae was the dominant spider mite in all growing regions and years with T. pacificus and T. turkestani also present in orchards in the southern San Joaquin Valley. Phytoseiid species richness declined with latitude among the three walnut growing regions and of the 13 species found Amblyseius similoides, Euseius stipulatus, Galendromus occidentalis and Typhlodromus caudiglans were the most abundant and widespread species present. Mean proportional abundance significantly increased from early (mid May-July) to late (August-mid October) season and from southern to northern growing regions for Type II and IV predators, but significantly decreased from early to late season and from southern to northern growing regions for Type III predators. The mean proportional abundance of Type II predators, particularly G. occidentalis, significantly increased and that of Type III predators significantly decreased with mean Tetranychus density in individual orchards. The current survey provides a more in-depth analysis of mite assemblages in walnut orchards in California and can be used to better inform adaptive management strategies for integrated mite management in the future.

Applications of nature-inspired metaheuristic algorithms for tackling optimization problems across disciplines

Nature-inspired metaheuristic algorithms are important components of artificial intelligence, and are increasingly used across disciplines to tackle various types of challenging optimization problems. This paper demonstrates the usefulness of such algorithms for solving a variety of challenging optimization problems in statistics using a nature-inspired metaheuristic algorithm called competitive swarm optimizer with mutated agents (CSO-MA). This algorithm was proposed by one of the authors and its superior performance relative to many of its competitors had been demonstrated in earlier work and again in this paper. The main goal of this paper is to show a typical nature-inspired metaheuristic algorithmi, like CSO-MA, is efficient for tackling many different types of optimization problems in statistics. Our applications are new and include finding maximum likelihood estimates of parameters in a single cell generalized trend model to study pseudotime in bioinformatics, estimating parameters in the commonly used Rasch model in education research, finding M-estimates for a Cox regression in a Markov renewal model, performing matrix completion tasks to impute missing data for a two compartment model, and selecting variables optimally in an ecology problem in China. To further demonstrate the flexibility of metaheuristics, we also find an optimal design for a car refueling experiment in the auto industry using a logistic model with multiple interacting factors. In addition, we show that metaheuristics can sometimes outperform optimization algorithms commonly used in statistics.

High resilience of Pacific Island forests to a category- 5 cyclone

Assessing how forests respond to, and recuperate from, cyclones is critical to understanding forest dynamics and planning for the impacts of climate change. Projected increases in the intensity and frequency of severe cyclones can threaten both forests and forest-dependent communities. The Pacific Islands are subject to frequent low-intensity cyclones, but there is little information on the effects of high intensity cyclones, or on how forest stewardship practices may affect outcomes. We assess the resistance and resilience of forests in three community-stewarded sites on the island of Tanna, Vanuatu, to the wind-related effects of 2015 Category-5 Cyclone Pam, one of the most intense cyclones to make landfall globally. Drawing on transect data established pre-and post-cyclone, we (1) test whether windspeed and tree structural traits predict survival and damage intensity, and whether this varies across sites; (2) assess post-cyclone regeneration of canopy, ground cover, seedlings, and saplings, and how community composition shifts over time and across sites. In sites that sustained a direct hit, 88 % of trees were defoliated, 34 % sustained severe damage, and immediate mortality was 13 %. Initial mortality, but not severe damage, was lower in areas that received an indirect hit and had lower windspeed. Larger trees and those with lighter wood had a higher probability of uprooting and snapping, respectively. Canopy and ground cover regenerated within three years and seedling and sapling regeneration was widespread across life histories, from pioneer to mature forest species. Three species of non-native vines recruited post-cyclone but within 5 years had largely declined or disappeared with canopy closure. Tanna's historical cyclone frequency, combined with customary stewardship practices that actively maintain a diversity of species and multiplicity of regeneration pathways, are likely responsible for the island's resistance and resilience to an intense tropical cyclone.

LIES of omission: complex observation processes in ecology

Advances in statistics mean that it is now possible to tackle increasingly sophisticated observation processes. The intricacies and ambitious scale of modern data collection techniques mean that this is now essential. Methodological research to make inference about the biological process while accounting for the observation process has expanded dramatically, but solutions are often presented in field-specific terms, limiting our ability to identify commonalities between methods. We suggest a typology of observation processes that could improve translation between fields and aid methodological synthesis. We propose the LIES framework (defining observation processes in terms of issues of Latency, Identifiability, Effort and Scale) and illustrate its use with both simple examples and more complex case studies.

Application of Multivariate Regression and Artificial Neural Network Modelling for Prediction of Physicochemical Properties of Grape-Skin Compost

The reusability of by-products in the food industry is consistent with sustainable and greener production; therefore, the aim of this paper was to evaluate the applicability of multiple linear regression (MLR), piecewise linear regression (PLR) and artificial neural network models (ANN) to the prediction of grape-skin compost's physicochemical properties (moisture, dry matter, organic matter, ash content, carbon content, nitrogen content, C/N ratio, total colour change of compost samples, pH, conductivity, total dissolved solids and total colour change of compost extract samples) during in-vessel composting based on the initial composting conditions (air-flow rate, moisture content and day of sampling). Based on the coefficient of determination for prediction, the adjusted coefficient of determination for calibration, the root-mean-square error of prediction (RMSEP), the standard error of prediction (SEP), the ratio of prediction to deviation (RPD) and the ratio of the error range (RER), it can be concluded that all developed MLR and PLR models are acceptable for process screening. Furthermore, the ANN model developed for predicting moisture and dry-matter content can be used for quality control (RER >11). The obtained results show the great potential of multivariate modelling for analysis of the physicochemical properties of compost during composting, confirming the high applicability of modelling in greener production processes.

Spatial accumulation of flood-driven riverside litter in two Northern Atlantic Rivers

The escalation of litter accumulation in aquatic environments is recognized as an emerging global concern. Although rivers represent the main conduits for land-based waste into the oceans, the spatial dynamics of litter accumulation in these systems remain poorly investigated, especially after hydro-climatic extreme events. Floods have been identified as major drivers of litter mobilization, including macroplastics, within rivers. However, predicting flood-induced litter accumulation along riverbanks is complex due to the cumulative interplay of multiple environmental (geomorphological and riparian) and anthropogenic factors. Using empirical data collected from 14 stream reaches in two Northern Atlantic rivers in Portugal, our study evaluates which factors, among geomorphological, riparian, and anthropogenic descriptors, best drive riverside litter accumulation after floods. Taking into account the longitudinal gradient and the spatial heterogeneity of the studied reaches, our study enhances how the accumulation and characteristics (type, size) of riverside litter vary across a rural-urban continuum. Our model reveals that the combination of the human population density and the stream slope at river reach showed the highest explanatory power for the accumulation of riverside litter. Our findings indicate that litter tends to be retained close to the source, even under flood conditions. We also found that the structure of riparian vegetation showed low explanatory power for litter accumulation. However, riparian trapping could be influenced by litter input (density and type) which varies with anthropogenic activities. This work highlights the importance of gathering field data to identify critical areas of riverside litter accumulation within river basins. Our findings can further support environmental managers in designing and implementing effective cleanup campaigns and implementing plastic recovery strategies at specific areas. Nevertheless, it is crucial to enhance coordinated efforts across the entire value chain to reduce plastic pollution, promote innovative approaches for plastic litter valorization, and establish effective prevention pathways.

Loss, resilience and recovery of kelp forests in a region of rapid ocean warming

BACKGROUND AND AIMS

Changes in kelp abundances on regional scales have been highly variable over the past half-century owing to strong effects of local and regional drivers. Here, we assess patterns and dominant environmental variables causing spatial and interspecific variability in kelp persistence and resilience to change in Nova Scotia over the past 40 years.

METHODS

We conducted a survey of macrophyte abundance at 251 sites spanning the Atlantic coast of Nova Scotia from 2019 to 2022. We use this dataset to describe spatial variability in kelp species abundances, compare species occurrences to surveys conducted in 1982 and assess changes in kelp abundance over the past 22 years. We then relate spatial and temporal patterns in abundance and resilience to environmental metrics.

KEY RESULTS

Our results show losses of sea urchins and the cold-tolerant kelp species Alaria esculenta, Saccorhiza dermatodea and Agarum clathratum in Nova Scotia since 1982 in favour of the more warm-tolerant kelps Saccharina latissima and Laminaria digitata. Kelp abundances have increased slightly since 2000, and Saccharina latissima and L. digitata are widely abundant in the region today. The highest kelp cover occurs on wave-exposed shores and at sites where temperatures have remained below thresholds for growth (21 °C) and mortality (23 °C). Moreover, kelp has recovered from turf dominance following losses at some sites during a warm period from 2010 to 2012.

CONCLUSIONS

Our results indicate that dramatic changes in kelp community composition and a loss of sea urchin herbivory as a dominant driver of change in the system have occurred in Nova Scotia over the past 40 years. However, a broad-scale shift to turf-dominance has not occurred, as predicted, and our results suggest that resilience and persistence are still a feature of kelp forests in the region despite rapid warming over the past several decades.

Diurnal temperature variation impacts energetics but not reproductive effort across seasons in a temperate dung beetle

Temperature varies on multiple timescales and ectotherms must adjust to these changes to survive. These adjustments may lead to energetic trade-offs between self-maintenance and reproductive investment. However, we know little about how diurnal and seasonal temperature changes impact energy allocation. Here we used a combination of empirical data and modeling of both thermoregulatory behaviors and body temperature to examine potential energetic trade-offs in the dung beetle Onthophagus taurus. Beginning in March 2020, universities and laboratories were officially closed due to the COVID-19 pandemic. We thus performed experiments at a private residence near Knoxville, Tennessee, USA, leveraging the heating, ventilation and air conditioning of the home to manipulate temperature and compare beetle responses to stable indoor temperatures versus variable outdoor temperatures. We collected O. taurus beetles in the early-, mid-, and late-breeding seasons to examine energetics and reproductive output in relation to diurnal and seasonal temperature fluctuations. We recorded the mass of field fresh beetles before and after a 24-h fast and used the resulting change in mass as a proxy for energetic costs of self-maintenance across seasons. To understand the impacts of diurnal fluctuations on energy allocation, we held beetles either indoors or outdoors for 14-day acclimation trials, fed them cow dung, and recorded mass change and reproductive output. Utilizing biophysical models, we integrated individual-level biophysical characteristics, microhabitat-specific performance, respirometry data, and thermoregulatory behaviors to predict temperature-induced changes to the allocation of energy toward survival and reproduction. During 24 h of outdoor fasting, we found that beetles experiencing reduced temperature variation lost more mass than those experiencing greater temperature variation, and this was not affected by season. By contrast, during the 14-day acclimation trials, we found that beetles experiencing reduced temperature variation (i.e., indoors) gained more mass than those experiencing greater temperature variation (i.e., outdoors). This effect may have been driven by shifts in the metabolism of the beetles during acclimation to increased temperature variation. Despite the negative relationship between temperature variation and energetic reserves, the only significant predictor of reproductive output was mean temperature. Taken together, we find that diurnal temperature fluctuations are important for driving energetics, but not reproductive output.

Predicting the spatial distribution of wintering golden eagles to inform full annual cycle conservation in western North America

Wildlife conservation strategies focused on one season or population segment may fail to adequately protect populations, especially when a species' habitat preferences vary among seasons, age-classes, geographic regions, or other factors. Conservation of golden eagles (Aquila chrysaetos) is an example of such a complex scenario, in which the distribution, habitat use, and migratory strategies of this species of conservation concern vary by age-class, reproductive status, region, and season. Nonetheless, research aimed at mapping priority use areas to inform management of golden eagles in western North America has typically focused on territory-holding adults during the breeding period, largely to the exclusion of other seasons and life-history groups. To support population-wide conservation planning across the full annual cycle for golden eagles, we developed a distribution model for individuals in a season not typically evaluated-winter-and in an area of the interior western U.S. that is a high priority for conservation of the species. We used a large GPS-telemetry dataset and library of environmental variables to develop a machine-learning model to predict spatial variation in the relative intensity of use by golden eagles during winter in Wyoming, USA, and surrounding ecoregions. Based on a rigorous series of evaluations including cross-validation, withheld and independent data, our winter-season model accurately predicted spatial variation in intensity of use by multiple age- and life-history groups of eagles not associated with nesting territories (i.e., all age classes of long-distance migrants, and resident non-adults and adult "floaters", and movements of adult territory holders and their offspring outside their breeding territories). Important predictors in the model were wind and uplift (40.2% contribution), vegetation and landcover (27.9%), topography (14%), climate and weather (9.4%), and ecoregion (8.7%). Predicted areas of high-use winter habitat had relatively low spatial overlap with nesting habitat, suggesting a conservation strategy targeting high-use areas for one season would capture as much as half and as little as one quarter of high-use areas for the other season. The majority of predicted high-use habitat (top 10% quantile) occurred on private lands (55%); lands managed by states and the Bureau of Land Management (BLM) had a lower amount (33%), but higher concentration of high-use habitat than expected for their area (1.5-1.6x). These results will enable those involved in conservation and management of golden eagles in our study region to incorporate spatial prioritization of wintering habitat into their existing regulatory processes, land-use planning tasks, and conservation actions.

Biotic homogenization, lower soil fungal diversity and fewer rare taxa in arable soils across Europe

Soil fungi are a key constituent of global biodiversity and play a pivotal role in agroecosystems. How arable farming affects soil fungal biogeography and whether it has a disproportional impact on rare taxa is poorly understood. Here, we used the high-resolution PacBio Sequel targeting the entire ITS region to investigate the distribution of soil fungi in 217 sites across a 3000 km gradient in Europe. We found a consistently lower diversity of fungi in arable lands than grasslands, with geographic locations significantly impacting fungal community structures. Prevalent fungal groups became even more abundant, whereas rare groups became fewer or absent in arable lands, suggesting a biotic homogenization due to arable farming. The rare fungal groups were narrowly distributed and more common in grasslands. Our findings suggest that rare soil fungi are disproportionally affected by arable farming, and sustainable farming practices should protect rare taxa and the ecosystem services they support.

Urban Magpies Frequently Feed on Coyote Scats and May Spread an Emerging Zoonotic Tapeworm

Allocoprophagy, in which animals feed on the feces of other individuals or species, has been little studied in vertebrates, despite its relevance to parasite transmission. These relationships may be especially important in cities, where animal density, disease incidence, and spatial overlap of humans and wildlife increase. Our goal was to document the incidence and predictors of coprophagy by black-billed magpies (Pica hudsonia) at coyote (Canis latrans) scats in Edmonton, Canada. We detected scats by following coyote trails and recorded whether coprophagy had occurred. We used multiple logistic regression to determine the top contextual and environmental predictors of coprophagy. Of 668 coyote scats, 37.3% had apparently been fed on. Coprophagy was more likely in winter and when scats were not fresh and did not contain vegetation or garbage. Environmental predictors of coprophagy included proximity to other coyote scats and playgrounds, distance from water and maintained trails, abundant natural land cover, and proximity to encampments of people experiencing homelessness. Our results reveal that magpies frequently access coyote scat and often do so near human-use areas. In Edmonton, where > 50% of coyotes are infected with a zoonotic tapeworm, coprophagy likely causes magpies to transport parasites with implications for zoonotic disease risk.

A field-validated ensemble species distribution model of Eriogonum pelinophilum, an endangered subshrub in Colorado, USA

Understanding the suitable habitat of endangered species is crucial for agencies such as the Bureau of Land Management to plan management and conservation. However, few species distribution models are directly validated, potentially limiting their application in management. In preparation for a Species Status Assessment of clay-loving wild buckwheat (Eriogonum pelinophilum), an endangered subshrub found in southwest Colorado, we ran a series of species distribution models to estimate the species' potential occupied habitat and validated these models in the field. A 1-meter resolution digital elevation model derived from LiDAR and a high-resolution geology mapping helped identify biologically relevant characteristics of the species' habitat. We employed a weighted ensemble model based on two Random Forest and one Boosted Regression Tree model, and discrimination performance of the ensemble model was high (AUC-PR = 0.793). We then conducted a systematic field survey of model habitat suitability predictions, during which we discovered 55 new subpopulations of the species and demonstrated that new species observations were strongly associated with model predictions (p < .0001, Cliff's delta = 0.575). We further refined our original models by incorporating the additional species occurrences collected in the field survey, a new explanatory variable, and a more diverse set of models. These iterative changes marginally improved performance of the ensemble model (AUC-PR = 0.825). Direct validation of species distribution models is extremely rare, and our field survey provides strong validation of our model results. This helps increase confidence to utilize predictions in planning. The final model predictions greatly improve the Bureau of Land Management's understanding of the species' habitat and increase our ability to consider potential habitat in planning land use activities such as road development and travel management.

Bayesian model selection for generalized linear mixed models

We propose a Bayesian model selection approach for generalized linear mixed models (GLMMs). We consider covariance structures for the random effects that are widely used in areas such as longitudinal studies, genome-wide association studies, and spatial statistics. Since the random effects cannot be integrated out of GLMMs analytically, we approximate the integrated likelihood function using a pseudo-likelihood approach. Our Bayesian approach assumes a flat prior for the fixed effects and includes both approximate reference prior and half-Cauchy prior choices for the variances of random effects. Since the flat prior on the fixed effects is improper, we develop a fractional Bayes factor approach to obtain posterior probabilities of the several competing models. Simulation studies with Poisson GLMMs with spatial random effects and overdispersion random effects show that our approach performs favorably when compared to widely used competing Bayesian methods including deviance information criterion and Watanabe-Akaike information criterion. We illustrate the usefulness and flexibility of our approach with three case studies including a Poisson longitudinal model, a Poisson spatial model, and a logistic mixed model. Our proposed approach is implemented in the R package GLMMselect that is available on CRAN.

The effectiveness of interventions to reduce cardio-metabolic risk factors among regular street food consumers in Dar es Salaam, Tanzania: The pre-post findings from a cluster randomized trial (Registered by Pan African clinical trial registry with trial # PACTR202208642850935)

INTRODUCTION

The healthy plate model (HPM) is a practical guide to modulate the portion of staple food in main meals, subsequently affecting the risks associated with Non-communicable Diseases include type2 diabetes mellitus (T2DM).

OBJECTIVE

This study investigated the effectiveness of health information and the healthy plate model on cardio-metabolic risk factors, knowledge and attitude towards T2DM prevention measures.

METHODS

A pre-post analysis, as part of a cluster randomized trial with street food vendors and their customers, was implemented in three randomly selected districts in Dar es Salaam, Tanzania. Two vendor-customer clusters each with 15 and more vendors from each district were randomly assigned to receive either T2DM health information only (Intervention package1 [IP1]) or IP1 plus a subsidized meal with vegetables and fruits, following the principles of the HPM (Intervention package2 [IP2]). Within the clusters the participants were informed on the importance of the intervention they received. An intervention period lasted for three months from 1st April to 31st June 2019. We applied Generalized Linear Mixed Models and Bayesian Modelling (for sensitivity analysis) to assess the effectiveness of the interventions.

RESULTS

Overall, 336 (IP2 = 175 and IP1 = 161) out of 560 (280/arm) previous study participants participated in evaluation. Diastolic BP was lower among IP2 participants in the evaluation than baseline AβC = -4.1mmHg (95%CI:-5.42 to -2.76). After adjusting for the interaction between IP2 and age of the consumers, the BMI was significantly lower among IP2 in the evaluation than baseline AβC = -0.7kg/m2 (95%CI: -1.17 to -0.23). With interaction between IP2 and income, BMI was higher in the IP2 in the evaluation than baseline AβC = 0.73kg/m2 (95%CI: 0.08 to 1.38). Systolic and diastolic BP were significantly lower among IP1 in the evaluation than baseline AβC = -3.5mmHg (95%CI:-5.78 to -1.24) and AβC = -5.9mmHg (95%CI:-7.34 to -4.44) respectively. Both the knowledge scores and positive attitudes towards T2DM prevention measures were higher in the evaluation than baseline in both interventions arms.

CONCLUSION

The positive effects on cardio-metabolic risk factors, knowledge and attitude were observed in both intervention arms. Due to interactions between IP2, age and income; designing interventions relating to food and cardio-metabolic risk factors, should consider combining socio-economic factors.

Translation of Machine Learning-Based Prediction Algorithms to Personalised Empiric Antibiotic Selection: A Population-Based Cohort Study

BACKGROUND

Prediction of antibiotic non-susceptibility based on patient characteristics and clinical status may support selection of empiric antibiotics for suspected hospital-acquired urinary tract infections (HA-UTIs).

METHODS

Prediction models were developed to predict non-susceptible results of eight antibiotic susceptibility tests ordered for suspected HA-UTI. Eligible patients were those with urine culture and susceptibility test results after 48 hours of admission between 2010-2021. Patient demographics, diagnosis, prescriptions, exposure to multidrug-resistant organisms, transfer history, and a daily calculated antibiogram were used as predictors. Lasso logistic regression (LLR), extreme gradient boosting (XGB), random forest, and stacked ensemble methods were used for development. Parsimonious models were also developed for clinical utility. Discrimination was assessed using the area under the receiver operating characteristic curve (AUROC).

RESULTS

In 10 474 suspected HA-UTI cases, the mean age was 62.1 ± 16.2 years and 48.1% were male. Non-susceptibility prediction for ampicillin/sulbactam, cefepime, ciprofloxacin, imipenem, piperacillin/tazobactam, and trimethoprim/sulfamethoxazole performed best using the stacked ensemble (AUROC 76.9, 76.1, 77.0, 80.6, 76.1, and 76.5, respectively). The model for ampicillin performed best with LLR (AUROC 73.4). Extreme gradient boosting only performed best for gentamicin (AUROC 66.9). In the parsimonious models, the LLR yielded the highest AUROC for ampicillin, ampicillin/sulbactam, cefepime, gentamicin, and trimethoprim/sulfamethoxazole (AUROC 70.6, 71.8, 73.0, 65.9, and 73.0, respectively). The model for ciprofloxacin performed best with XGB (AUROC 70.3), while the model for imipenem performed best in the stacked ensemble (AUROC 71.3). A personalised application using the parsimonious models was publicly released.

CONCLUSIONS

Prediction models for antibiotic non-susceptibility were developed to support empiric antibiotic selection for HA-UTI.

Hedges, mottes, and baileys: Causally ambiguous statistical language can increase perceived study quality and policy relevance

There is a norm in psychology to use causally ambiguous statistical language, rather than straightforward causal language, when describing methods and results of nonexperimental studies. However, causally ambiguous language may inhibit a critical examination of the study's causal assumptions and lead to a greater acceptance of policy recommendations that rely on causal interpretations of nonexperimental findings. In a preregistered experiment, 142 psychology faculty, postdocs, and doctoral students (54% female), ages 22-67 (M = 33.20, SD = 8.96), rated the design and analysis from hypothetical studies with causally ambiguous statistical language as of higher quality (by .34-.80 SD) and as similarly or more supportive (by .16-.27 SD) of policy recommendations than studies described in straightforward causal language. Thus, using statistical rather than causal language to describe nonexperimental findings did not decrease, and may have increased, perceived support for implicitly causal conclusions.

Long-term assessment of relationships between changing environmental conditions and the physiology of southern Beaufort Sea polar bears (Ursus maritimus)

Climate change is influencing polar bear (Ursus maritimus) habitat, diet, and behavior but the effects of these changes on their physiology is not well understood. Blood-based biomarkers are used to assess the physiologic health of individuals but their usefulness for evaluating population health, especially as it relates to changing environmental conditions, has rarely been explored. We describe links between environmental conditions and physiologic functions of southern Beaufort Sea polar bears using data from blood samples collected from 1984 to 2018, a period marked by extensive environmental change. We evaluated associations between 13 physiologic biomarkers and circumpolar (Arctic oscillation index) and regional (wind patterns and ice-free days) environmental metrics and seasonal and demographic co-variates (age, sex, season, and year) known to affect polar bear ecology. We observed signs of dysregulation of water balance in polar bears following years with a lower annual Arctic oscillation index. In addition, liver enzyme values increased over time, which is suggestive of potential hepatocyte damage as the Arctic has warmed. Biomarkers of immune function increased with regional-scale wind patterns and the number of ice-free days over the Beaufort Sea continental shelf and were lower in years with a lower winter Arctic oscillation index, suggesting an increased allocation of energetic resources for immune processes under these conditions. We propose that the variation in polar bear immune and metabolic function is likely indicative of physiologic plasticity, a response that allows polar bears to remain in homeostasis even as they experience changes in nutrition and habitat in response to changing environments.

Structural asymmetry in biotic interactions as a tool to understand and predict ecological persistence

A universal feature of ecological systems is that species do not interact with others with the same sign and strength. Yet, the consequences of this asymmetry in biotic interactions for the short- and long-term persistence of individual species and entire communities remains unclear. Here, we develop a set of metrics to evaluate how asymmetric interactions among species translate to asymmetries in their individual vulnerability to extinction under changing environmental conditions. These metrics, which solve previous limitations of how to independently quantify the size from the shape of the so-called feasibility domain, provide rigorous advances to understand simultaneously why some species and communities present more opportunities to persist than others. We further demonstrate that our shape-related metrics are useful to predict short-term changes in species' relative abundances during 7 years in a Mediterranean grassland. Our approach is designed to be applied to any ecological system regardless of the number of species and type of interactions. With it, we show that is possible to obtain both mechanistic and predictive information on ecological persistence for individual species and entire communities, paving the way for a stronger integration of theoretical and empirical research.

Practical advice on variable selection and reporting using Akaike information criterion

The various debates around model selection paradigms are important, but in lieu of a consensus, there is a demonstrable need for a deeper appreciation of existing approaches, at least among the end-users of statistics and model selection tools. In the ecological literature, the Akaike information criterion (AIC) dominates model selection practices, and while it is a relatively straightforward concept, there exists what we perceive to be some common misunderstandings around its application. Two specific questions arise with surprising regularity among colleagues and students when interpreting and reporting AIC model tables. The first is related to the issue of 'pretending' variables, and specifically a muddled understanding of what this means. The second is related to p-values and what constitutes statistical support when using AIC. There exists a wealth of technical literature describing AIC and the relationship between p-values and AIC differences. Here, we complement this technical treatment and use simulation to develop some intuition around these important concepts. In doing so we aim to promote better statistical practices when it comes to using, interpreting and reporting models selected when using AIC.

Models of underlying autotrophic biomass dynamics fit to daily river ecosystem productivity estimates improve understanding of ecosystem disturbance and resilience

Directly observing autotrophic biomass at ecologically relevant frequencies is difficult in many ecosystems, hampering our ability to predict productivity through time. Since disturbances can impart distinct reductions in river productivity through time by modifying underlying standing stocks of biomass, mechanistic models fit to productivity time series can infer underlying biomass dynamics. We incorporated biomass dynamics into a river ecosystem productivity model for six rivers to identify disturbance flow thresholds and understand the resilience of primary producers. The magnitude of flood necessary to disturb biomass and thereby reduce ecosystem productivity was consistently lower than the more commonly used disturbance flow threshold of the flood magnitude necessary to mobilize river bed sediment. The estimated daily maximum percent increase in biomass (a proxy for resilience) ranged from 5% to 42% across rivers. Our latent biomass model improves understanding of disturbance thresholds and recovery patterns of autotrophic biomass within river ecosystems.

Competition and generalization impede cultural formation in wild jackdaws

Animal cultures have now been demonstrated experimentally in diverse taxa from flies to great apes. However, experiments commonly use tasks with unrestricted access to equal pay-offs and innovations seeded by demonstrators who are trained to exhibit strong preferences. Such conditions may not reflect those typically found in nature. For example, the learned preferences of natural innovators may be weaker, while competition for depleting resources can favour switching between strategies and generalizing from past experience. Here we show that in experiments where wild jackdaws (Corvus monedula) can freely discover depleting supplies of novel foods, generalization has a powerful effect on learning, allowing individuals to exploit multiple new opportunities through both social and individual learning. Further, in contrast to studies with trained demonstrators, individuals that were first to innovate showed weak preferences. As a consequence, many individuals ate all available novel foods, displaying no strong preference and no group-level culture emerged. Individuals followed a 'learn from adults' strategy, but other demographic factors played a minimal role in shaping social transmission. These results demonstrate the importance of generalization in allowing animals to exploit new opportunities and highlight how natural competitive dynamics may impede the formation of culture.

Individual variation in the habitat selection of upstream migrating fish near a barrier

BACKGROUND

Migration is a vital element of the life cycle of many freshwater fish species but is increasingly hampered globally by riverine barriers. Fish passes are a common approach to enable migration past barriers but are often ineffective. More knowledge is required on fish behaviour as they approach barriers such as habitat preferences.

METHODS

We evaluate the habitat selection of two upstream migrating fish species, barbel Barbus barbus and grayling Thymallus thymallus, at a hydropower plant in southern Germany, considering individual variation and population trends. Fish were tracked via fine-scale 2D acoustic telemetry in 2018 during their spawning migration. Step selection functions were used to evaluate selection of hydraulic parameters by the fish for a time step of 20 s. Exploratory models were built via model selection for each individual fish, to evaluate the extent of individual variation in model structure. A population model was developed for each species by averaging coefficients from individual models to describe general trends. The extent of individual variation was determined and confidence intervals for the population model coefficients were calculated.

RESULTS

Fish varied greatly in individual model structure though common terms were apparent in both species, such as depth, flow velocity, the angular difference between fish and velocity, and the logarithm of the step length. Final population models for barbel included several parameters describing habitat selection and displacement. Barbel selected for faster flows, deeper water, and higher spatial velocity gradients. In addition, they selected to move more with the flow than against. Interactions were also present between habitat parameters, suggesting selection is context dependent. Barbel movement speed also changed with depth, flow velocity and spatial velocity gradient. With grayling, terms often had contrasting effects among individuals and thus general trends could not be distinguished for most terms.

CONCLUSION

Our findings demonstrate habitat selection by upstream migrating fish approaching a fish pass and differences in individual selection which may have an impact on barrier management. Step selection functions are a promising approach and can provide useful insight into habitat selection and movement by migrating freshwater fish in an altered river system.

Ecosystem bioelement variability is associated with freshwater animal aggregations at the aquatic-terrestrial interface

The impacts of animals on the biogeochemical cycles of major bioelements like C, N, and P are well-studied across ecosystem types. However, more than 20 elements are necessary for life. The feedbacks between animals and the biogeochemical cycles of the other bioelements are an emerging research priority. We explored how much freshwater mussels (Bivalvia: Unionoida) were related to variability in ecosystem pools of 10 bioelements (Ca, Cu, Fe, K, Mn, Na, Mg, P, S and Zn) in streams containing a natural mussel density gradient in the US Interior Highlands. We studied the concentrations of these bioelements across the aquatic-terrestrial interface-in the porewater of riverine gravel bars, and the emergent macrophyte Justicia americana. Higher mussel density was associated with increased calcium in gravel bars and macrophytes. Mussel density also correlated with variability in iron and other redox-sensitive trace elements in gravel bars and macrophytes, although this relationship was mediated by sediment grain size. We found that two explanations for the patterns we observed are worthy of further research: (1) increased calcium availability in gravel bars near denser mussel aggregations may be a product of the buildup and dissolution of shells in the gravel bar, and (2) mussels may alter redox conditions, and thus elemental availability in gravel bars with fine sediments, either behaviorally or through physical structure provided by shell material. A better understanding of the physical and biogeochemical impacts of animals on a wide range of elemental cycles is thus necessary to conserve the societal value of freshwater ecosystems.

Unified tumor growth mechanisms from multimodel inference and dataset integration

Mechanistic models of biological processes can explain observed phenomena and predict responses to a perturbation. A mathematical model is typically constructed using expert knowledge and informal reasoning to generate a mechanistic explanation for a given observation. Although this approach works well for simple systems with abundant data and well-established principles, quantitative biology is often faced with a dearth of both data and knowledge about a process, thus making it challenging to identify and validate all possible mechanistic hypothesis underlying a system behavior. To overcome these limitations, we introduce a Bayesian multimodel inference (Bayes-MMI) methodology, which quantifies how mechanistic hypotheses can explain a given experimental datasets, and concurrently, how each dataset informs a given model hypothesis, thus enabling hypothesis space exploration in the context of available data. We demonstrate this approach to probe standing questions about heterogeneity, lineage plasticity, and cell-cell interactions in tumor growth mechanisms of small cell lung cancer (SCLC). We integrate three datasets that each formulated different explanations for tumor growth mechanisms in SCLC, apply Bayes-MMI and find that the data supports model predictions for tumor evolution promoted by high lineage plasticity, rather than through expanding rare stem-like populations. In addition, the models predict that in the presence of cells associated with the SCLC-N or SCLC-A2 subtypes, the transition from the SCLC-A subtype to the SCLC-Y subtype through an intermediate is decelerated. Together, these predictions provide a testable hypothesis for observed juxtaposed results in SCLC growth and a mechanistic interpretation for tumor treatment resistance.

Behavioral evidence of olfactory imprinting during embryonic and larval stages in lake sturgeon

Many migratory fishes are thought to navigate to natal streams using olfactory cues learned during early life stages. However, direct evidence for early-life olfactory imprinting is largely limited to Pacific salmon, and other species suspected to imprint show life history traits and reproductive strategies that raise uncertainty about the generality of the salmonid-based conceptual model of olfactory imprinting in fishes. Here, we studied early-life olfactory imprinting in lake sturgeon (Acipenser fulvescens), which have a life cycle notably different from Pacific salmon, but are nonetheless hypothesized to home via similar mechanisms. We tested one critical prediction of the hypothesis that early-life olfactory imprinting guides natal homing in lake sturgeon: that exposure to odorants during early-life stages results in increased activity when exposed to those odorants later in life. Lake sturgeon were exposed to artificial odorants (phenethyl alcohol and morpholine) during specific developmental windows and durations (limited to the egg, free-embryo, exogenous feeding larvae and juvenile stages), and later tested as juveniles for behavioral responses to the odorants that were demonstrative of olfactory memory. Experiments revealed that lake sturgeon reared in stream water mixed with artificial odorants for as little as 7 days responded to the odorants in behavioral assays over 50 days after the initial exposure, specifically implicating the free-embryo and larval stages as critical imprinting periods. Our study provides evidence for olfactory imprinting in a non-salmonid fish species, and supports further consideration of conservation tactics such as stream-side rearing facilities that are designed to encourage olfactory imprinting to targeted streams during early life stages. Continued research on lake sturgeon can contribute to a model of olfactory imprinting that is more generalizable across diverse fish species and will inform conservation actions for one of the world's most imperiled fish taxonomic groups.

Soundscape analysis using eco-acoustic indices for the birds biodiversity assessment in urban parks (case study: Isfahan City, Iran)

Biophony and anthrophony analysis as part of the urban soundscape is an efficient approach to bird biodiversity monitoring and to studying the impact of noise pollution in urban parks. Here, we analyzed the soundscape composition to monitor the diversity of birds using acoustic indices and machine learning in 21 urban parks of Isfahan, Iran, in spring 2019. To achieve this purpose four-step method was considered: (i) choosing parks and sampling of sound and bird species; (ii) calculated the six acoustic indices; (iii) calculated the six biodiversity indices; and (iv) statistical analysis for predicting biodiversity index from acoustic indices. Three regression models including support vector machine (SVM), random forest (RF), and elastic net regularization (GLMNET) applied the acoustic indices with minimum and maximum recorded thresholds to feature extraction to measure biodiversity indicators. The optimization model was applied to reduce the independent variables. Generally, more than 18,000 samples were modeled for the dependent variables in each model. The regression results demonstrated that the highest R square was related to the songbird (0.93), evenness (0.92), and richness (0.9) indecies in the SVM model and the Shannon index (0.86) in the RF model. The results of acoustics analysis demonstrated that the Acoustic Entropy Index (H), Normalized Difference Soundscape Index (NDSI), Bioacoustics Index (BI), and Acoustic Complexity Index (ACI) indices were suitable because they could serve as proxies for bird richness and activity that reflect differences in habitat quality. Our findings offer using acoustic indicators as an efficient approach for monitoring bird biodiversity in urban parks.

Social network inheritance and differentiation in wild baboons

Immatures' social development may be fundamental to understand important biological processes, such as social information transmission through groups, that can vary with age and sex. Our aim was to determine how social networks change with age and differ between sexes in wild immature baboons, group-living primates that readily learn socially. Our results show that immature baboons inherited their mothers' networks and differentiated from them as they aged, increasing their association with partners of similar age and the same sex. Males were less bonded to their matriline and became more peripheral with age compared to females. Our results may pave the way to further studies testing a new hypothetical framework: in female-philopatric societies, social information transmission may be constrained at the matrilineal level by age- and sex-driven social clustering.

Nutrient and stoichiometry dynamics of decomposing litter in stream ecosystems: A global synthesis

Decomposing organic matter forms a substantial resource base fueling the biogeochemical function and secondary production of most aquatic ecosystems. However, detrital N (nitrogen) and P (phosphorus) dynamics remain relatively unexplored in aquatic relative to terrestrial ecosystems, despite fundamentally linking microbial processes to ecosystem function across broad spatial scales. We synthesized 217 published time series of detrital carbon (C), N, P, and their stoichiometric ratios (C:N, C:P, N:P) from stream ecosystems to analyze the temporal nutrient dynamics of decomposing litter using generalized additive models. Model results indicated that detritus was a net source of N (irrespective of inorganic or organic form) to the environment regardless of initial N content. In contrast, P sink/source dynamics were more strongly influenced by initial P content, where P-poor litters were sinks of nutrients until shifting to net P mineralization after ~40% mass loss. However, large variation surrounded both N and P predictions, suggesting the importance of non-microbial factors such as fragmentation by invertebrates. Detrital C:N ratios converged and became more similar toward the end of decomposition, suggesting predictable microbial functional effects throughout detrital ontogeny. C:P and N:P ratios also converged to some degree, but these model predictions were less robust than for C:N, due in part to the lower number of published detrital C:P time series. Explorations of environmental covariate effects were frequently limited by few coincident covariate measurements across studies, but temperature, N availability, and P tended to accelerate existing ontogenetic patterns in C:N. Our analysis helps unite organic matter decomposition across aquatic-terrestrial boundaries by describing basic patterns of elemental flows catalyzed by decomposition in streams, and points to a research agenda to continue addressing gaps in our knowledge of detrital nutrient dynamics across ecosystems. This article is protected by copyright. All rights reserved.

Data integration reveals dynamic and systematic patterns of breeding habitat use by a threatened shorebird

Incorporating species distributions into conservation planning has traditionally involved long-term representations of habitat use where temporal variation is averaged to reveal habitats that are most suitable across time. Advances in remote sensing and analytical tools have allowed for the integration of dynamic processes into species distribution modeling. Our objective was to develop a spatiotemporal model of breeding habitat use for a federally threatened shorebird (piping plover, Charadrius melodus). Piping plovers are an ideal candidate species for dynamic habitat models because they depend on habitat created and maintained by variable hydrological processes and disturbance. We integrated a 20-year (2000-2019) nesting dataset with volunteer-collected sightings (eBird) using point process modeling. Our analysis incorporated spatiotemporal autocorrelation, differential observation processes within data streams, and dynamic environmental covariates. We evaluated the transferability of this model in space and time and the contribution of the eBird dataset. eBird data provided more complete spatial coverage in our study system than nest monitoring data. Patterns of observed breeding density depended on both dynamic (e.g., surface water levels) and long-term (e.g., proximity to permanent wetland basins) environmental processes. Our study provides a framework for quantifying dynamic spatiotemporal patterns of breeding density. This assessment can be iteratively updated with additional data to improve conservation and management efforts, because reducing temporal variability to average patterns of use may cause a loss in precision for such actions.

Effects of climate on fall migration phenology of monarch butterflies departing the northeastern breeding grounds in Canada

Monarch butterflies (Danaus plexippus) undergo an iconic multi-generational migration, traveling thousands of kilometers from the summer breeding grounds in southern Canada to overwintering sites in central Mexico. This migration phenomena can be affected by climate change, which may have important implications on fitness and ultimately populations status. We investigated the long-term trends in fall migration phenology of monarchs using a 25-year dataset collected along the coast of Lake Erie in Ontario, Canada. We also investigated local long-term trends in weather covariates that have the potential to influence migration phenology at this site. Patterns in standardized daily counts of monarchs were compared with local weather covariates using two methods (i.e., monthly averages and moving windows) to assess difference in outputs between analytical approaches. Our results suggest that monarch migration timing (migration midpoint, average peak, first peak, and late passage) and weather covariates have been consistent over time, in direct contrast to a similar study in Cape May, New Jersey, which showed a significant increase in both fall temperature and a 16- to 19-day shift in monarch migration timing. Furthermore, our results differed between analytical approaches. With respect to annual variability in air temperature, our monthly average analysis suggested that for each degree increase in September air temperature, late season passage would advance 4.71 days (±1.59 SE, p = .01). However, the moving window analysis suggested that this result is likely spurious and found no significant correlations between migration timing and any weather covariates. Importantly, our results caution against extrapolating the effects of climate change on the migration phenology of the monarch across study regions and the need for more long-term monitoring efforts to better understand regional drivers of variability in migration timing.

Functional substitutability of native herbivores by livestock for soil carbon stock is mediated by microbial decomposers

Grazing by large mammalian herbivores impacts climate as it can favor the size and stability of a large carbon (C) pool in the soils of grazing ecosystems. As native herbivores in the world's grasslands, steppes, and savannas are progressively being displaced by livestock, it is important to ask whether livestock can emulate the functional roles of their native counterparts. While livestock and native herbivores can have remarkable similarity in their traits, they can differ greatly in their impacts on vegetation composition which can affect soil-C. It is uncertain how these similarities and differences impact soil-C via their influence on microbial decomposers. We test competing alternative hypotheses with a replicated, long-term, landscape-level, grazing-exclusion experiment to ask whether livestock in the Trans-Himalayan ecosystem of northern India can match decadal-scale (2005-2016) soil-C stocks under native herbivores. We evaluate multiple lines of evidence from 17 variables that influence soil-C (quantity and quality of C-input from plants, microbial biomass and metabolism, microbial community composition, eDNA, veterinary antibiotics in soil), and assess their inter-relationships. Livestock and native herbivores differed in their effects on several soil microbial processes. Microbial carbon use efficiency (CUE) was 19% lower in soils under livestock. Compared to native herbivores, areas used by livestock contained 1.5 kg C m^-2 less soil-C. Structural equation models showed that alongside the effects arising from plants, livestock alter soil microbial communities which is detrimental for CUE, and ultimately also for soil-C. Supporting evidence pointed toward a link between veterinary antibiotics used on livestock, microbial communities, and soil-C. Overcoming the challenges of sequestering antibiotics to minimize their potential impacts on climate, alongside microbial rewilding under livestock, may reconcile the conflicting demands from food-security and ecosystem services. Conservation of native herbivores and alternative management of livestock is crucial for soil-C stewardship to envision and achieve natural climate solutions.

Predicting effects of multiple interacting global change drivers across trophic levels

Global change encompasses many co-occurring anthropogenic drivers, which can act synergistically or antagonistically on ecological systems. Predicting how different global change drivers simultaneously contribute to observed biodiversity change is a key challenge for ecology and conservation. However, we lack the mechanistic understanding of how multiple global change drivers influence the vital rates of multiple interacting species. We propose that reaction norms, the relationships between a driver and vital rates like growth, mortality, and consumption, provide insights to the underlying mechanisms of community responses to multiple drivers. Understanding how multiple drivers interact to affect demographic rates using a reaction-norm perspective can improve our ability to make predictions of interactions at higher levels of organization-that is, community and food web. Building on the framework of consumer-resource interactions and widely studied thermal performance curves, we illustrate how joint driver impacts can be scaled up from the population to the community level. A simple proof-of-concept model demonstrates how reaction norms of vital rates predict the prevalence of driver interactions at the community level. A literature search suggests that our proposed approach is not yet used in multiple driver research. We outline how realistic response surfaces (i.e., multidimensional reaction norms) can be inferred by parametric and nonparametric approaches. Response surfaces have the potential to strengthen our understanding of how multiple drivers affect communities as well as improve our ability to predict when interactive effects emerge, two of the major challenges of ecology today.

Predicting age and mass at maturity from feeding behavior and diet in Manduca sexta: An empirical test of a life history model

Feeding for most animals involves bouts of active ingestion alternating with bouts of no ingestion. In insects, the temporal patterning of bouts varies widely with resource quality and is known to affect growth, development time, and fitness. However, the precise impacts of resource quality and feeding behavior on insect life history traits are poorly understood. To explore and better understand the connections between feeding behavior, resource quality, and insect life history traits, we combined laboratory experiments with a recently proposed mechanistic model of insect growth and development for a larval herbivore, Manduca sexta. We ran feeding trials for 4th and 5th instar larvae across different diet types (two hostplants and artificial diet) and used these data to parameterize a joint model of age and mass at maturity that incorporates both insect feeding behavior and hormonal activity. We found that the estimated durations of both feeding and nonfeeding bouts were significantly shorter on low-quality than on high-quality diets. We then explored how well the fitted model predicted historical out-of-sample data on age and mass of M. sexta. We found that the model accurately described qualitative outcomes for the out-of-sample data, notably that a low-quality diet results in reduced mass and later age at maturity compared with high-quality diets. Our results clearly demonstrate the importance of diet quality on multiple components of insect feeding behavior (feeding and nonfeeding) and partially validate a joint model of insect life history. We discuss the implications of these findings with respect to insect herbivory and discuss ways in which our model could be improved or extended to other systems.

Scale-sensitivity in the measurement and interpretation of environmental niches

Species environmental niches are central to ecology, evolution, and global change research, but their characterization and interpretation depend on the spatial scale (specifically, the spatial grain) of their measurement. We find that the spatial grain of niche measurement is usually uninformed by ecological processes and varies by orders of magnitude. We illustrate the consequences of this variation for the volume, position, and shape of niche estimates, and discuss how it interacts with geographic range size, habitat specialization, and environmental heterogeneity. Spatial grain significantly affects the study of niche breadth, environmental suitability, niche evolution, niche tracking, and climate change effects. These and other fields will benefit from a more mechanism-informed choice of spatial grain and cross-grain evaluations that integrate different data sources.

Drought reduces invasive grass performance by disrupting plant-microbe interactions that enhance plant nitrogen supply

Non-native invasive plants can promote their dominance in novel ecosystems by accelerating soil nutrient cycling via interactions with decomposer microbes. Changes in abiotic conditions associated with frequent or prolonged drought may disrupt these interactions, but the effects of disruption on invasive plant performance and the underpinning mechanisms are poorly understood. Here, we used rainout shelters in an experimental field setting to test the hypothesis that drought reduces invasive plant performance by reducing microbial metabolic activity, resulting in decreased nitrogen flow to plants. We imposed growing season drought on populations of the exotic grass Microstegium vimineum, a widespread invasive plant in eastern deciduous forests, and quantified effects on aboveground and belowground biomass, and carbon (C) and nitrogen (N) cycling among plants, decomposers, and soil. Drought resulted in a 24% decrease in soil respiration, a 16% decrease in phenol oxidase enzyme activity, a 12% decrease in dissolved organic N concentration, and a decrease in the C:N ratio of particulate organic matter, suggesting reduced microbial metabolic activity and nutrient mining of soil organic matter. Drought also reduced aboveground Microstegium biomass 33% and increased Microstegium leaf C:N ratio, consistent with a decline in plant N uptake. We conclude that drought can reduce the performance of existing invasive species populations by suppressing plant-microbe interactions that increase nitrogen supply to plants, which may have consequences for the persistence of invasive plants under hydrologic change.