Negative density dependence characterizes mutualistic interactions between birds and fruiting plants across latitudes.

Negative density dependence (NDD) in biotic interactions of interference such as plant-plant competition, granivory and herbivory are well-documented mechanisms that promote species' coexistence in diverse plant communities worldwide. Here, we investigated the generality of a novel type of NDD mechanism that operates through the mutualistic interactions of frugivory and seed dispersal among fruit-eating birds and plants. By sampling community-wide frugivory interactions at high spatial and temporal resolution in Pennsylvania, Puerto Rico, Peru, Brazil and Argentina, we evaluated whether interaction frequencies between birds and fruit resources occurred more often (selection), as expected, or below expectations (under-utilization) set by the relative fruit abundance of the fruit resources of each plant species. Our models considered the influence of temporal scales of fruit availability and bird phylogeny and diets, revealing that NDD characterizes frugivory across communities. Irrespective of taxa or dietary guild, birds tended to select fruits of plant species that were proportionally rare in their communities, or that became rare following phenological fluctuations, while they mostly under-utilized abundant fruit resources. Our results demonstrate that negative density-dependence in frugivore-plant interactions provides a strong equalizing mechanism for the dispersal processes of fleshy-fruited plant species in temperate and tropical communities, likely contributing to building and sustaining plant diversity. This article is part of the theme issue 'Diversitydependence of dispersal: interspecific interactions determine spatial dynamics'.

Water temperature and microenvironmental factors predict the presence and detection of the snail host of Fasciola hepatica in Andean Patagonia.

The transmission of Fasciola hepatica occurs only where there are -or recently were- aquatic or amphibious snails of the Lymnaeidae family, the intermediate host of this parasite. Direct detection of these snails is time-consuming and imprecise, hindering accurate and detailed mapping of transmission risk. To identify which microenvironmental factors could be used as proxies for the occurrence of the lymnaeid snail Galba viator, a major intermediate host in South America, a total of 183 1-m2 quadrants across diverse water bodies in an endemic area in Andean Patagonia were manually timed-searched for snails and microenvironmental variables were registered. Data was analyzed using a Bayesian hierarchical occupancy model that assessed the effects of the microenvironmental variables on the presence of snails while considering imperfect snail detection. The model estimated that G. viator predominantly inhabits shallow aquatic environments, in the presence of grasses, where snails of the genus Biomphalaria are also detected, and with scarce tree canopy cover. Physical factors affecting occupancy presumably act as proxies for the average water temperature, while the temperature at the time of sampling was found to affect snail detectability. The identified variables are easy, fast, and inexpensive to measure, and can complement management decisions and risk maps based on coarser remote-sensing data, particularly relevant in a context of growing resistance to anthelminthic drugs.

miR-21, miR-221, miR-29 and miR-34 are distinguishable molecular features of a metabolically unhealthy phenotype in young adults.

Discrepancies between the measurement of body mass index (BMI) and metabolic health status have been described for the onset of metabolic diseases. Studying novel biomarkers, some of which are associated with metabolic syndrome, can help us to understand the differences between metabolic health (MetH) and BMI. A group of 1469 young adults with pre-specified anthropometric and blood biochemical parameters were selected. Of these, 80 subjects were included in the downstream analysis that considered their BMI and MetH parameters for selection as follows: norm weight metabolically healthy (MHNW) or metabolically unhealthy (MUNW); overweight/obese metabolically healthy (MHOW) or metabolically unhealthy (MUOW). Our results showed for the first time the differences when the MetH status and the BMI are considered as global MetH statures. First, all the evaluated miRNAs presented a higher expression in the metabolically unhealthy group than the metabolically healthy group. The higher levels of leptin, IL-1b, IL-8, IL-17A, miR-221, miR-21, and miR-29 are directly associated with metabolic unhealthy and OW/OB phenotypes (MUOW group). In contrast, high levels of miR34 were detected only in the MUNW group. We found differences in the SIRT1-PGC1α pathway with increased levels of SIRT1+ cells and diminished mRNA levels of PGCa in the metabolically unhealthy compared to metabolically healthy subjects. Our results demonstrate that even when metabolic diseases are not apparent in young adult populations, MetH and BMI have a distinguishable phenotype print that signals the potential to develop major metabolic diseases.

Genetic risk score for insulin resistance based on gene variants associated to amino acid metabolism in young adults.

Circulating concentration of arginine, alanine, aspartate, isoleucine, leucine, phenylalanine, proline, tyrosine, taurine and valine are increased in subjects with insulin resistance, which could in part be attributed to the presence of single nucleotide polymorphisms (SNPs) within genes associated with amino acid metabolism. Thus, the aim of this work was to develop a Genetic Risk Score (GRS) for insulin resistance in young adults based on SNPs present in genes related to amino acid metabolism. We performed a cross-sectional study that included 452 subjects over 18 years of age. Anthropometric, clinical, and biochemical parameters were assessed including measurement of serum amino acids by high performance liquid chromatography. Eighteen SNPs were genotyped by allelic discrimination. Of these, ten were found to be in Hardy-Weinberg equilibrium, and only four were used to construct the GRS through multiple linear regression modeling. The GRS was calculated using the number of risk alleles of the SNPs in HGD, PRODH, DLD and SLC7A9 genes. Subjects with high GRS (≥ 0.836) had higher levels of glucose, insulin, homeostatic model assessment- insulin resistance (HOMA-IR), total cholesterol and triglycerides, and lower levels of arginine than subjects with low GRS (p < 0.05). The application of a GRS based on variants within genes associated to amino acid metabolism may be useful for the early identification of subjects at increased risk of insulin resistance.

Using sulfur stable isotope ratios (δ34 S) for animal geolocation: Estimating the delay mechanisms between diet ingestion and isotope incorporation in tail hair.

RATIONALE: Metabolism and diet quality play an important role in determining delay mechanisms between an animal ingesting an element and depositing the associated isotope signal in tissue. While many isotope mixing models assume instantaneous reflection of diet in an animal- tissue, this is rarely the case. Here we use data from wildebeest to measure the lag time between ingestion of 34 S and its detection in tail hair. METHODS: We use time-lagged regression analysis of δ34 S data from GPS-collared blue wildebeest from the Serengeti ecosystem in combination with δ34 S isoscape data to estimate the lag time between an animal ingesting and depositing 34 S in tail hair. RESULTS: The best fitting regression model of δ34 S in tail hair and an individual- position on the δ34 S isoscape is generated assuming an average time delay of 78 days between ingestion and detection in tail hair. This suggests that sulfur may undergo multiple metabolic transitions before being deposited in tissue. CONCLUSION: Our findings help to unravel the underlying complexities associated with sulfur metabolism and are broadly consistent with results from other species. These findings will help to inform research aiming to apply the variation of δ34 S in inert biological material for geolocation or understanding dietary changes, especially for fast moving migratory ungulates such as wildebeest.

Inferring the interaction rules of complex systems with graph neural networks and approximate Bayesian computation.

Inferring the underlying processes that drive collective behaviour in biological and social systems is a significant statistical and computational challenge. While simulation models have been successful in qualitatively capturing many of the phenomena observed in these systems in a variety of domains, formally fitting these models to data remains intractable. Recently, approximate Bayesian computation (ABC) has been shown to be an effective approach to inference if the likelihood function for a model is unavailable. However, a key difficulty in successfully implementing ABC lies with the design, selection and weighting of appropriate summary statistics, a challenge that is especially acute when modelling high dimensional complex systems. In this work, we combine a Gaussian process accelerated ABC method with the automatic learning of summary statistics via graph neural networks. Our approach bypasses the need to design a model-specific set of summary statistics for inference. Instead, we encode relational inductive biases into a neural network using a graph embedding and then extract summary statistics automatically from simulation data. To evaluate our framework, we use a model of collective animal movement as a test bed and compare our method to a standard summary statistics approach and a linear regression-based algorithm.

Nutritional, biochemical, and clinical determinants of hyperuricemia in systemic lupus erythematosus patients: Relationship with clinical and renal disease activity.

Systemic lupus erythematosus (SLE) is the prototypical autoimmune disease considered as an independent risk factor for mortality by cardiovascular disease. Currently, uric acid is described as a novel biomarker associated with cardiometabolic risk. However, nutritional and serum determinants that influence hyperuricemia development in autoimmune diseases have not been fully elucidated. This study aimed to assess the nutritional, biochemical, and cardiometabolic determinants of hyperuricemia and its relationship with clinical variables in SLE patients. A cross-sectional study was conducted in 167 SLE patients and 195 control subjects (CS). Nutrient intake, anthropometry, biochemical, and cardiometabolic indexes were evaluated. In SLE patients, adequate protein (OR = 0.4; p = 0.04) and carbohydrate (OR = 0.2; p = 0.01) intakes were associated with a lower risk of hyperuricemia. SLE patients with hyperuricemia presented a higher risk of clinical (OR = 2.2; p = 0.03) and renal activity (OR = 3.4; p < 0.01), as well as triglycerides ≥150 mg/dL (OR = 3.6; p < 0.01), hs-CRP ≥1 mg/L (OR = 3.1; p < 0.01), Kannel score ≥3 (OR = 2.5; p = 0.02), and BMI ≥25 kg/m2 (OR = 2.2; p = 0.02). Oppositely, serum levels of HDL-C ≥40 mg/dL (OR = 0.2; p < 0.01) were associated with a lower risk of hyperuricemia. According to the pharmacotherapy administered, prednisone treatment was associated with a high risk of hyperuricemia (OR = 4.7; p < 0.001). In contrast, the hydroxychloroquine treatment was associated with a lower risk of hyperuricemia (OR = 0.4; p = 0.02). In conclusion, SLE patients with hyperuricemia presented a high risk of clinical and renal activity as well as worse cardiometabolic status. Notably, an adequate intake of protein, carbohydrates, healthy HDL-C serum levels, and hydroxychloroquine treatment could be determinants of lower risk of hyperuricemia.

Integration of mark-recapture and acoustic detections for unbiased population estimation in animal communities.

Abundance estimation methods that combine several types of data are becoming increasingly common because they yield more accurate and precise parameter estimates and predictions than are possible from a single data source. These beneficial effects result from increasing sample size (through data pooling) and complementarity between different data types. Here, we test whether integrating mark-recapture data with passive acoustic detections into a joint likelihood improves estimates of population size in a multi-guild community. We compared the integrated model to a mark-recapture-only model using simulated data first and then using a data set of mist-net captures and acoustic recordings from an Afrotropical agroforest bird community. The integrated model with simulated data improved accuracy and precision of estimated population size and detection parameters. When applied to field data, the integrated model was able to produce, for each bird guild, ecologically plausible estimates of population size and detection parameters, with more precision compared with the mark-recapture model. Overall, our results show that adding acoustic data to mark-recapture analyses improves estimates of population size. With the increasing availability of acoustic recording devices, this data collection technique could readily be added to routine field protocols, leading to a cost-efficient improvement of traditional mark-recapture population estimation.

A preliminary study on health impacts of Mexican mercury mining workers in a context of precarious employment.

Mercury mining is one of the main sources of mercury (Hg) release into the environment, causing serious impacts on human health and the environment. Workers in these mines are employed informally and precariously and therefore lack labor rights such as social security. The objective of the study is to make visible the exposure to environmental contaminants and the health of workers in mercury mines. An environmental assessment was conducted to determine workers' exposure to contaminants; urine samples were obtained to measure exposure to mercury and arsenic, and blood samples were obtained for lead and cadmium. Clinical parameters were also evaluated. Concentrations of Hg, As and Pb were determined in soil, 279.4 mg/kg (24.4-788.5), 14.7 mg/kg (9.5-20.3) and 1.4 mg/kg (1-2.8), respectively. The exposure results for mercury were 551 µg/g creatinine, for arsenic 50 µg/L and for lead 4.7 µg/dL. Cd-B was not found. In addition, 17.6 % of the workers had diabetes and 17.6 % had renal disorders. Principal Component Regression was performed obtaining an r2 of 0.86 for glomerular filtration rate and 0.54 for albumin creatinine ratio using clinical, occupational, and metal exposure variables. Exposure to Hg in this type of mine is not exclusive, so there is a cumulative risk of chronic exposure to different environmental pollutants directly impacting the health of workers. It is necessary to implement health strategies and different work opportunities for these workers.

Association of BCAT2 and BCKDH polymorphisms with clinical, anthropometric and biochemical parameters in young adults.

BACKGROUND AND AIM: Circulating amino acids are modified by sex, body mass index (BMI) and insulin resistance (IR). However, whether the presence of genetic variants in branched-chain amino acid (BCAA) catabolic enzymes modifies circulating amino acids is still unknown. Thus, we determined the frequency of two genetic variants, one in the branched-chain aminotransferase 2 (BCAT2) gene (rs11548193), and one in the branched-chain ketoacid dehydrogenase (BCKDH) gene (rs45500792), and elucidated their impact on circulating amino acid levels together with clinical, anthropometric and biochemical parameters. METHODS AND RESULTS: We performed a cross-sectional comparative study in which we recruited 1612 young adults (749 women and 863 men) aged 19.7 ± 2.1 years and with a BMI of 24.9 ± 4.7 kg/m2. Participants underwent clinical evaluation and provided blood samples for DNA extraction and biochemical analysis. The single nucleotide polymorphisms (SNPs) were determined by allelic discrimination using real-time polymerase chain reaction (PCR). The frequencies of the less common alleles were 15.2 % for BCAT2 and 9.83 % for BCKDH. The subjects with either the BCAT2 or BCKDH SNPs displayed no differences in the evaluated parameters compared with subjects homozygotes for the most common allele at each SNP. However, subjects with both SNPs had higher body weight, BMI, blood pressure, glucose, and circulating levels of aspartate, isoleucine, methionine, and proline than the subjects homozygotes for the most common allele (P < 0.05, One-way ANOVA). CONCLUSION: Our findings suggest that the joint presence of both the BCAT2 rs11548193 and BCKDH rs45500792 SNPs induces metabolic alterations that are not observed in subjects without either SNP.

Frugivory Specialization in Birds and Fruit Chemistry Structure Mutualistic Networks across the Neotropics.

AbstractThe interaction between fruit chemistry and the physiological traits of frugivores is expected to shape the structure of mutualistic seed dispersal networks, but it has been understudied compared with the role of morphological trait matching in structuring interaction patterns. For instance, highly frugivorous birds (i.e., birds that have fruits as the main component of their diets), which characteristically have fast gut passage times, are expected to avoid feeding on lipid-rich fruits because of the long gut retention times associated with lipid digestion. Here, we compiled data from 84 studies conducted in the Neotropics that used focal plant methods to record 35,815 feeding visits made by 317 bird species (155 genera in 28 families) to 165 plant species (82 genera in 48 families). We investigated the relationship between the degree of frugivory of birds (i.e., how much of their diet is composed of fruit) at the genus level and their visits to plant genera that vary in fruit lipid content. We used a hierarchical modeling of species communities approach that accounted for the effects of differences in body size, bird and plant phylogeny, and spatial location of study sites. We found that birds with a low degree of frugivory (e.g., predominantly insectivores) tend to have the highest increase in visitation rates as fruits become more lipid rich, while birds that are more frugivorous tend to increase visits at a lower rate or even decrease visitation rates as lipids increase in fruits. This balance between degree of frugivory and visitation rates to lipid-poor and lipid-rich fruits provides a mechanism to explain specialized dispersal systems and the occurrence of certain physiological nutritional filters, ultimately helping us to understand community-wide interaction patterns between birds and plants.

A hierarchical machine learning framework for the analysis of large scale animal movement data.

BACKGROUND: In recent years the field of movement ecology has been revolutionized by our ability to collect high-accuracy, fine scale telemetry data from individual animals and groups. This growth in our data collection capacity has led to the development of statistical techniques that integrate telemetry data with random walk models to infer key parameters of the movement dynamics. While much progress has been made in the use of these models, several challenges remain. Notably robust and scalable methods are required for quantifying parameter uncertainty, coping with intermittent location fixes, and analysing the very large volumes of data being generated. METHODS: In this work we implement a novel approach to movement modelling through the use of multilevel Gaussian processes. The hierarchical structure of the method enables the inference of continuous latent behavioural states underlying movement processes. For efficient inference on large data sets, we approximate the full likelihood using trajectory segmentation and sample from posterior distributions using gradient-based Markov chain Monte Carlo methods. RESULTS: While formally equivalent to many continuous-time movement models, our Gaussian process approach provides flexible, powerful models that can detect multiscale patterns and trends in movement trajectory data. We illustrate a further advantage to our approach in that inference can be performed using highly efficient, GPU-accelerated machine learning libraries. CONCLUSIONS: Multilevel Gaussian process models offer efficient inference for large-volume movement data sets, along with the fitting of complex flexible models. Applications of this approach include inferring the mean location of a migration route and quantifying significant changes, detecting diurnal activity patterns, or identifying the onset of directed persistent movements.

Tracking animal movements using biomarkers in tail hairs: a novel approach for animal geolocating from sulfur isoscapes.

BACKGROUND: Current animal tracking studies are most often based on the application of external geolocators such as GPS and radio transmitters. While these technologies provide detailed movement data, they are costly to acquire and maintain, which often restricts sample sizes. Furthermore, deploying external geolocators requires physically capturing and recapturing of animals, which poses an additional welfare concern. Natural biomarkers provide an alternative, non-invasive approach for addressing a range of geolocation questions and can, because of relatively low cost, be collected from many individuals thereby broadening the scope for population-wide inference. METHODS: We developed a low-cost, minimally invasive method for distinguishing between local versus non-local movements of cattle using sulfur isotope ratios (δ34S) in cattle tail hair collected in the Greater Serengeti Ecosystem, Tanzania. RESULTS: We used a Generalized Additive Model to generate a predicted δ34S isoscape across the study area. This isoscape was constructed using spatial smoothers and underpinned by the positive relationship between δ34S values and lithology. We then established a strong relationship between δ34S from recent sections of cattle tail hair and the δ34S from grasses sampled in the immediate vicinity of an individual's location, suggesting δ34S in the hair reflects the δ34S in the environment. By combining uncertainty in estimation of the isoscape, with predictions of tail hair δ34S given an animal's position in the isoscape we estimated the anisotropic distribution of travel distances across the Serengeti ecosystem sufficient to detect movement using sulfur stable isotopes. CONCLUSIONS: While the focus of our study was on cattle, this approach can be modified to understand movements in other mobile organisms where the sulfur isoscape is sufficiently heterogeneous relative to the spatial scale of animal movements and where tracking with traditional methods is difficult.

Can network metrics predict vulnerability and species roles in bird-dispersed plant communities? Not without behaviour.

Network metrics are widely used to infer the roles of mutualistic animals in plant communities and to predict the effect of species' loss. However, their empirical validation is scarce. Here we parameterized a joint species model of frugivory and seed dispersal with bird movement and foraging data from tropical and temperate communities. With this model, we investigate the effect of frugivore loss on seed rain, and compare our predictions to those of standard coextinction models and network metrics. Topological coextinction models underestimated species loss after the removal of highly linked frugivores with unique foraging behaviours. Network metrics informed about changes in seed rain quantity after frugivore loss. However, changes in seed rain composition were only predicted by partner diversity. Nestedness, closeness, and d' specialisation could not anticipate the effects of rearrangements in plant-frugivore communities following species loss. Accounting for behavioural differences among mutualists is critical to improve predictions from network models.

Bioremoval of Cobalt(II) from Aqueous Solution by Three Different and Resistant Fungal Biomasses.

The biosorption of Co(II) on three fungal biomasses: Paecilomyces sp., Penicillium sp., and Aspergillus niger, was studied in this work. The fungal biomass of Paecilomyces sp. showed the best results, since it removes 93% at 24 h of incubation, while the biomasses of Penicillium sp. and Aspergillus niger are less efficient, since they remove the metal 77.5% and 70%, respectively, in the same time of incubation, with an optimum pH of removal for the three analyzed biomasses of 5.0 ± 0.2 at 28°C. Regarding the temperature of incubation, the most efficient biomass was that of Paecilomyces sp., since it removes 100%, at 50°C, while the biomasses of Penicillium sp. and Aspergillus niger remove 97.1% and 94.1%, at the same temperature, in 24 hours of incubation. On the contrary, if the concentration of the metal is increased, the removal capacity for the three analyzed biomasses decreases; if the concentration of the bioadsorbent is increased, the removal of the metal also increases. It was observed that, after 4 and 7 days of incubation, 100%, 100%, and 96.4% of Co(II) present in naturally contaminated water were removed, respectively.

Continuous dye adsorption and desorption on an invasive macrophyte (Salvinia minima).

The continuous adsorption-desorption of methylene blue (MB) on an invasive macrophyte, Salvinia minima, was investigated in fixed-bed columns. The effects of bed depth (h) (9.30, 18.70, and 28 cm), inlet dye concentration (C0) (51 ± 1.20, 154 ± 2.00, and 250 ± 1.50 mg L-1), and flow rate (Q) (7 and 14 mL min-1) on dye removal and breakthrough curves were assessed. Thomas, modified dose-response (MDR) and bed depth service time (BDST) models were fitted to the experimental data. Desorption and regeneration studies were also performed. The breakthrough time was affected by h, C0, and Q. The dynamic bed capacity at the breakthrough point (qb) increased with increasing h but decreased with increasing C0 and Q. Dynamic bed capacities (qe) from 318 to 322 mg g-1 were achieved at h = 28 cm, C0 = 154 ± 2.0, or 250 ± 1.50 mg L-1, independently of the Q value. High MB removals were also observed (75-78%). FTIR analysis revealed that hydroxyl and carboxyl groups could be involved in dye adsorption. MDR and BDST models were both successfully used to predict the breakthrough curves of MB adsorption onto S. minima. A high regeneration efficiency (> 87%) was obtained after three adsorption-desorption cycles. These results confirm that the use of S. minima biomass could be a very efficient and eco-friendly alternative for MB adsorption in continuous mode.

Running on empty: recharge dynamics from animal movement data.

Vital rates such as survival and recruitment have always been important in the study of population and community ecology. At the individual level, physiological processes such as energetics are critical in understanding biomechanics and movement ecology and also scale up to influence food webs and trophic cascades. Although vital rates and population-level characteristics are tied with individual-level animal movement, most statistical models for telemetry data are not equipped to provide inference about these relationships because they lack the explicit, mechanistic connection to physiological dynamics. We present a framework for modelling telemetry data that explicitly includes an aggregated physiological process associated with decision making and movement in heterogeneous environments. Our framework accommodates a wide range of movement and physiological process specifications. We illustrate a specific model formulation in continuous-time to provide direct inference about gains and losses associated with physiological processes based on movement. Our approach can also be extended to accommodate auxiliary data when available. We demonstrate our model to infer mountain lion (Puma concolor; in Colorado, USA) and African buffalo (Syncerus caffer; in Kruger National Park, South Africa) recharge dynamics.

Multi-dimensional Precision Livestock Farming: a potential toolbox for sustainable rangeland management.

BACKGROUND: Precision Livestock Farming (PLF) is a promising approach to minimize the conflicts between socio-economic activities and landscape conservation. However, its application on extensive systems of livestock production can be challenging. The main difficulties arise because animals graze on large natural pastures where they are exposed to competition with wild herbivores for heterogeneous and scarce resources, predation risk, adverse weather, and complex topography. Considering that the 91% of the world's surface devoted to livestock production is composed of extensive systems (i.e., rangelands), our general aim was to develop a PLF methodology that quantifies: (i) detailed behavioural patterns, (ii) feeding rate, and (iii) costs associated with different behaviours and landscape traits. METHODS: For this, we used Merino sheep in Patagonian rangelands as a case study. We combined data from an animal-attached multi-sensor tag (tri-axial acceleration, tri-axial magnetometry, temperature sensor and Global Positioning System) with landscape layers from a Geographical Information System to acquire data. Then, we used high accuracy decision trees, dead reckoning methods and spatial data processing techniques to show how this combination of tools could be used to assess energy balance, predation risk and competition experienced by livestock through time and space. RESULTS: The combination of methods proposed here are a useful tool to assess livestock behaviour and the different factors that influence extensive livestock production, such as topography, environmental temperature, predation risk and competition for heterogeneous resources. We were able to quantify feeding rate continuously through time and space with high accuracy and show how it could be used to estimate animal production and the intensity of grazing on the landscape. We also assessed the effects of resource heterogeneity (inferred through search times), and the potential costs associated with predation risk, competition, thermoregulation and movement on complex topography. DISCUSSION: The quantification of feeding rate and behavioural costs provided by our approach could be used to estimate energy balance and to predict individual growth, survival and reproduction. Finally, we discussed how the information provided by this combination of methods can be used to develop wildlife-friendly strategies that also maximize animal welfare, quality and environmental sustainability.

The costs and benefits of pollinator dependence: empirically based simulations predict raspberry fruit quality.

Globally, agriculture increasingly depends on pollinators to produce many seed and fruit crops. However, what constitutes optimal pollination service for pollinator-dependent crops remains unanswered. We developed a simulation model to identify the optimal pollination service that maximizes fruit quality in crops. The model depicts the pollination (i.e., autonomous self-fertilization, pollen deposition) and post-pollination (i.e., pollen germination, and time from germination to ovule fertilization) processes leading to fruit and seed set and allows for negative flower-pollinator interactions, specifically pistil damage. We parameterized and validated the model based on empirical observations of commercial raspberry in western Argentina. To assess the effects of pollination intensity for fruit production, we conducted simulations over a range of visit number per flower by the two primary managed pollinators worldwide, Apis mellifera and Bombus terrestris. Simulations identified that ~15-35 visits per flower by A. mellifera or ~10-20 visits by B. terrestris provide adequate pollination and maximize raspberry fruit quality (i.e., estimated as the proportion of ovules that develop into drupelets). Visits in excess of these optima reduce simulated fruit quality, and flowers receiving >670 honey bee visits or >470 bumble bee visits would produce fruits of poorer quality than those receiving no bee visits. The simulations generated consistent, unbiased predictions of fruit quality for 12 raspberry fields. This model could be adapted easily to other animal-pollinated crops and used to guide efficient pollinator management in any agro-ecosystem.

Sexual-size dimorphism modulates the trade-off between exploiting food and wind resources in a large avian scavenger.

Animals are expected to synchronize activity routines with the temporal patterns at which resources appear in nature. Accordingly, species that depend on resources showing temporally mismatched patterns should be expected to schedule routines that balance the chances of exploiting each of them. Large avian scavengers depend on carcasses which are more likely available early in the morning, but they also depend on wind resources (i.e. uplifts) to subside flight which are stronger in afternoon hours. To understand how these birds deal with this potential trade-off, we studied the daily routines of GPS-tagged individuals of the world's largest terrestrial soaring scavenger, the Andean condor (Vultur gryphus). Andean condors vary largely in weight and show a huge sexual dimorphism that allowed us to evaluate the effect of sex and body size on their daily routines. We found that condors use an intermediate solution strategy between the best times to exploit carcasses and uplifts, with this strategy changing over the year. Bigger males scheduled earlier routines that aligned more closely with uplift availability compared to smaller females, resulting in a partial temporal segregation between sexes. Condors' routines reflect a sexual-size dependent trade-off that may underpin ecological and sociobiological traits of the studied population.