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24 results for “performance landscape”
Data from: Nutrigonometry I: using right-angle triangles to quantify nutritional trade-offs in performance landscapes
<p>Animals regulate their food intake to maximise the expression of fitness traits but are forced to trade-off optimal expression of some fitness traits due to differences in nutrient requirements of each trait ('nutritional trade-offs'). Nutritional trade-offs have been experimentally uncovered using the Geometric Framework for Nutrition (GF). However, current analytical methods to measure such responses rely on either visual inspection or complex models of vector calculations applied to multidimensional performance landscapes, making these approaches subjective, or conceptually difficult, computationally expensive, and in some cases inaccurate. Here, we present a simple trigonometric model to measure nutritional trade-offs in multidimensional landscapes (Nutrigonometry), which relies on the trigonometric relationships of right-angle triangles and thus, is both conceptually and computationally easier to understand and use than previous quantitative approaches. We apply Nutrigonometry to a landmark GF dataset for the comparison of several standard statistical models to assess model performance in finding regions in the performance landscapes. This revealed that polynomial (Bayesian) regressions can be used for precise and accurate predictions of peaks and valleys in performance landscapes, irrespective of the underlying structure of the data (i.e., individual food intakes vs fixed diet ratios). We then identified the known nutritional trade-off between lifespan and reproductive rate both in terms of nutrient balance and concentration for validation of the model. This shows Nutrigonometry enables a fast, reliable, and reproducible quantification of nutritional trade-offs in multidimensional performance landscapes, thereby broadening the potential for future developments in comparative research on the evolution of animal nutrition.</p>
Data from: Nutrigonometry I: using right-angle triangles to quantify nutritional trade-offs in performance landscapes
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Linking Problem Landscape Features with the Performance of Individual CMA-ES Modules - Data
<p>This repository contains the performance data used in the paper "Linking Problem Landscape Features with the Performance of Individual CMA-ES Modules".</p> <p>The configurations run are in 'dt_run_confs.csv', and each other csv-file corresponds the the AUC values of one of these configurations.</p> <p>The script used to generate the full performance data is included in 'generate.py', and the processing using IOHanalyzer is shown in 'script.R' </p>
A new theoretical performance landscape for suction feeding reveals adaptive kinematics in a natural population of reef damselfish
<p><span>Understanding how organismal traits determine performance and</span><span>, </span><span>ultimately</span><span>, </span><span>fitness is a fundamental goal of evolutionary ecomorphology. However, multiple traits can interact in non-linear and context-dependent ways to affect performance, hindering efforts to place natural populations with respect to performance peaks or valleys. Here, we used an established mechanistic model of suction-feeding performance (SIFF) derived from hydrodynamic principles to estimate a theoretical performance landscape for zooplankton prey capture. This performance space can be used to predict prey capture performance for any combination of six morphological and kinematic trait values. We then mapped in situ high-speed video observations of suction feeding in a natural population of a coral reef zooplanktivore, Chromis viridis, onto the performance space to estimate the population's location with respect to the topography of the performance landscape. Although the kinematics of the natural population closely matched regions of high performance in the landscape, the population was not located on a performance peak. Individuals were furthest from performance peaks on the peak gape, ram speed and mouth opening speed trait axes. Moreover, we found that the trait combination</span><span>s </span><span>in the observed population were associated with higher performance than expected by chance, suggesting that these combinations are under selection. Our results provide a framework for assessing whether natural populations occupy performance optima.</span></p>
Data from: Nutrient landscape of a cricket nymph: How dietary protein and carbohydrate shape intake, performance, and body composition in the two-spotted cricket, <em>Gryllus bimaculatus</em>
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A new theoretical performance landscape for suction feeding reveals adaptive kinematics in a natural population of reef damselfish
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Estimating the influence of field inventory sampling intensity on forest landscape model performance for determining high-severity wildfire risk
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Data from: Nectar resource limitation affects butterfly flight performance and metabolism differently in intensive and extensive agricultural landscapes
Flight is an essential biological ability of many insects, but is energetically costly. Environments under rapid human-induced change are characterized by habitat fragmentation and may impose constraints on the energy income budget of organisms. This may, in turn, affect locomotor performance and willingness to fly. We tested flight performance and metabolic rates in Meadow brown butterflies (Maniola jurtina) of two contrasted agricultural landscapes: intensively managed, nectar-poor (IL) versus extensively managed, nectar-rich landscapes (EL). Young female adults were submitted to four nectar treatments (i.e. nectar quality and quantity) in outdoor flight cages. IL-individuals had better flight capacities in a flight mill and had lower resting metabolic rates (RMR) than EL-individuals, except under the severest treatment. Under this treatment, RMR increased in IL-individuals, but decreased in EL-individuals; flight performance was maintained by IL-individuals, but dropped by a factor 2.5 in EL-individuals. IL-individuals had more canalized (i.e. less plastic) responses relative to the nectar treatments than EL-individuals. Our results show significant intraspecific variation in the locomotor and metabolic response of a butterfly to different energy income regimes relative to the landscape of origin. Ecophysiological studies help improving our mechanistic understanding of the eco-evolutionary impact of anthropogenic environments on rare and widespread species.
Data from: Tree performance in a biodiversity enrichment experiment in an oil palm landscape
1. Large-scale conversion of tropical forests into oil palm monocultures has led to dramatic losses of biodiversity and ecosystem functions. While ecological restoration is urgently needed in many oil palm landscapes, there is a lack of scientific knowledge of sustainable management strategies. 2. We established experimental tree islands of varying sizes (25 m2 to 1600 m2) and diversity levels (1, 2, 3 and 6 species) in an oil palm plantation in Sumatra, Indonesia. Six native multi-purpose tree species including Jengkol (Archidendron pauciflorum), Durian (Durio zibethinus), Petai (Parkia speciosa), Meranti (Shorea leprosula), Sungkai (Peronema canescens), and Jelutung (Dyera polyphylla) were planted between living and felled oil palms. Here, we analyze the controlling factors of tree growth and survival during the first four years at the level of local neighborhood and tree island. 3. We found a significant effect of diversity levels on tree productivity, i.e. basal area was higher in mixed-species than in single-species tree islands. This overyielding was attributed to enhanced tree growth, while mortality had no effect. In the local neighborhood, tree species richness had a positive effect on tree growth during the first year only, indicating that selection and dominance of well-performing species at high level of diversity are most likely driving overyielding. 4. Trees grew better away from living oil palms, suggesting tree-palm competition. Proximity to felled oil palms increased growth especially during the first years, during which the planted trees might have benefited from the additional available space and resources. Despite positive edge effects from the conventional oil palm management in the surrounding, tree island size had an overall positive effect on tree growth. 5. Synthesis and applications. We planted multiple native trees in an oil palm plantation following a tree island pattern. The establishment success differed widely among species. The selection of particular species is a decisive factor to foster a positive relationship between diversity andtree growth. Planting larger tree islands (e.g. 1'600 square meters) is a better option to enhance tree growth, but tree-palm competition implies a trade-off between tree growth and palm oil production locally.
Data from: Performance of forest bryophytes with different geographical distributions transplanted across a topographically heterogeneous landscape
Most species distribution models assume a close link between climatic conditions and species distributions. Yet, we know little about the link between species' geographical distributions and the sensitivity of performance to local environmental factors. We studied the performance of three bryophyte species transplanted at south- and north-facing slopes in a boreal forest landscape in Sweden. At the same sites, we measured both air and ground temperature. We hypothesized that the two southerly distributed species Eurhynchium angustirete andHerzogiella seligeri perform better on south-facing slopes and in warm conditions, and that the northerly distributed species Barbilophozia lycopodioides perform better on north-facing slopes and in relatively cool conditions. The northern, but not the two southern species, showed the predicted relationship with slope aspect. However, the performance of one of the two southern species was still enhanced by warm temperatures. An important reason for the inconsistent results can be that microclimatic gradients across landscapes are complex and influenced by many climate-forcing factors. Therefore, comparing only north- and south-facing slopes might not capture the complexity of microclimatic gradients. Population growth rates and potential distributions are the integrated results of all vital rates. Still, the study of selected vital rates constitutes an important first step to understand the relationship between population growth rates and geographical distributions and is essential to better predict how climate change influences species distributions.
Impact of landscape ruggedness on performance modeling
<p>Previous efforts in our community has been focusing on developing new performance models, whereas little efforts are devoted to interrogate the bottlenecks of existing approaches. Here we demonstrate the accuracy of predictive models in fitting configuration performance can heavily rely on the ruggedness of the underlying.</p> <p>The set of plots presented here depicts the R2 score of a XGBoost regressor fitting on the configuration data of an entire landscape against the number of local optima/autocorrelation of each landscape. Each point in each subplot represents a workload, with the x and y values showing the R2 score and the number of local optima/autocorrelation of the corresponding landscape. A linear regression fit linear, along with 95% confidence interval of the fit, as well as Spearman's p, are also shown in the plots. </p> <p>From the results, it is very clear to see that the accuracy of the fit is significantly correlated with the ruggedness of the landscape, despite we have employed the same model. Notably, on LLVM, the R2 score can drop from nearly 0.9 all the way down to around 0.2 with the increase in landscape ruggedness.</p> <p>Therefore, local optima and landscape ruggedness can be play a critical role in performance modeling, which have not been previously reported. We intend to add this finding to the main text, since it can potentially inspire new performance modeling methods that can tackle rugged landscapes.</p>
Instances and results of the paper "Local Optima Networks, Landscape Autocorrelation and Heuristic Search Performance"
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Data from: Effects of gene action, marker density, and time since selection on the performance of landscape genomic scans of local adaptation
Genomic "scans" to identify loci that contribute to local adaptation are becoming increasingly common. Many methods used for such studies have assumed that local adaptation is created by loci experiencing antagonistic pleiotropy and that the selected locus itself is assayed, and few consider how signals of selection change through time. However, most empirical data sets have marker density too low to assume that a selected locus itself is assayed, researchers seldom know when selection was first imposed, and many locally adapted loci likely experience not antagonistic pleiotropy but conditional neutrality. We simulated data to evaluate how these factors affect the performance of tests for genotype-environment association. We found that three types of regression-based analyses (linear models, mixed linear models, and latent factor mixed models) and an implementation of BayEnv all performed well, with high rates of true positives and low rates of false positives, when the selected locus experienced antagonistic pleiotropy, and when the selected locus was assayed directly. However, all tests had reduced power to detect loci experiencing conditional neutrality, and the probability of detecting associations was sharply reduced when physically linked rather than causative loci were sampled. Antagonistic pleiotropy also maintained detectable genotype-environment associations much longer than conditional neutrality. Our analyses suggest that if local adaptation is often driven by loci experiencing conditional neutrality, genome-scan methods will have limited capacity to find loci responsible for local adaptation.
Data from: Tree performance in a biodiversity enrichment experiment in an oil palm landscape
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Data from: Performance of forest bryophytes with different geographical distributions transplanted across a topographically heterogeneous landscape
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Data from: Nectar resource limitation affects butterfly flight performance and metabolism differently in intensive and extensive agricultural landscapes
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Data from: Effects of gene action, marker density, and time since selection on the performance of landscape genomic scans of local adaptation
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Data from: Performance of partial statistics in individual-based landscape genetics
Individual-based landscape genetic methods have become increasingly popular for quantifying fine-scale landscape influences on gene flow. One complication for individual-based methods is that gene flow and landscape variables are often correlated with geography. Partial statistics, particularly Mantel tests, are often employed to control for these inherent correlations by removing the effects of geography while simultaneously correlating measures of genetic differentiation and landscape variables of interest. Concerns about the reliability of Mantel tests prompted this study, in which we use simulated landscapes to evaluate the performance of partial Mantel tests and two ordination methods, distance-based redundancy analysis (dbRDA) and redundancy analysis (RDA), for detecting isolation by distance (IBD) and isolation by landscape resistance (IBR). Specifically, we described the effects of suitable habitat amount, fragmentation and resistance strength on metrics of accuracy (frequency of correct results, type I/II errors and strength of IBR according to underlying landscape and resistance strength) for each test using realistic individual-based gene flow simulations. Mantel tests were very effective for detecting IBD, but exhibited higher error rates when detecting IBR. Ordination methods were overall more accurate in detecting IBR, but had high type I errors compared to partial Mantel tests. Thus, no one test outperformed another completely. A combination of statistical tests, for example partial Mantel tests to detect IBD paired with appropriate ordination techniques for IBR detection, provides the best characterization of fine-scale landscape genetic structure. Realistic simulations of empirical data sets will further increase power to distinguish among putative mechanisms of differentiation.
Data from: Quantifying nutritional trade-offs across multidimensional performance landscapes
Animals make feeding decisions to simultaneously maximise fitness traits that often require different nutrients. Recent quantitative methods have been developed to characterise these nutritional trade-offs from performance landscapes on which traits are mapped on a nutrient space defined by two nutrients. This limitation constrains the broad applications of previous methods to more complex data, and a generalised framework is needed. Here, we build upon previous methods and introduce a generalised vector-based approach – the Vector of Position approach – to study nutritional trade-offs in complex multi-dimensional spaces. The Vector of Position Approach allows the estimate of performance variations across entire landscapes (peaks and valleys), and compare these variations between animals. Using landmark published datasets on lifespan and reproduction landscapes, we illustrate how our approach gives accurate quantifications of nutritional trade-offs in two- and three-dimensional spaces, and can bring new insights into the underlying nutritional differences in trait expression between species. The Vector of Position Approach provides a generalised framework for investigating nutritional differences in life-history traits expression within and between species, an essential step for the development of comparative research on the evolution of animal nutritional strategies.
Data from: Functional performance of turtle humerus shape across an ecological adaptive landscape
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.