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14 results for “nutritional 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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Data from: The role of non-natural foods in the nutritional strategies of monkeys in a human-modified mosaic landscape
<p>Many tropical animals inhabit mosaic landscapes including human-modified habitat. In such landscapes, animals commonly adjust feeding behavior, and may incorporate non-natural foods. These behavioral shifts can influence consumers' nutritional states, with implications for population persistence. However, few studies have addressed the nutritional role of non-natural food. We examined nutritional ecology of wild blue monkeys to understand how dietary habits related to non-natural foods might support population persistence in a mosaic landscape. We documented prevalence and nutritional composition of non-natural foods in monkey diets to assess how habitat use influenced their consumption and their contribution to nutritional strategies. While most energy and macronutrients came from natural foods, subjects focused non-natural feeding activity on five exotic plants and averaged about a third of daily calories from non-natural foods. Most non-natural food calories came from non-structural carbohydrates and least from protein. Consumption of non-natural foods related to time in human-modified habitats, which two groups used non-randomly. Non-natural and natural foods were similar in nutrients, and the amount of non-natural food consumed drove variation in nutritional strategy. When more daily calories came from non-natural foods, females consumed a higher ratio of non-protein energy to protein (NPE:P). Females also prioritized protein while allowing NPE:P to vary, increasing NPE while capitalizing on non-natural foods. Overall, these tropical mammals achieved a similar nutrient balance regardless of their intake of non-natural foods. Forest and forest-adjacent areas with non-natural vegetation may provide adequate nutrient access for consumers, and thus contribute to wildlife conservation in mosaic tropical landscapes.</p>
Data from: The role of non-natural foods in the nutritional strategies of monkeys in a human-modified mosaic landscape
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Data from: Linking landscape-scale differences in forage to ungulate nutritional ecology
Understanding how habitat and nutritional condition affect ungulate populations is necessary for informing management, particularly in areas experiencing carnivore recovery and declining ungulate population trends. Variations in forage species availability, plant phenological stage, and the abundance of forage make it challenging to understand landscape-level effects of nutrition on ungulates. We developed an integrated spatial modeling approach to estimate landscape-level elk (Cervus elaphus) nutritional resources in two adjacent study areas that differed in coarse measures of habitat quality and related the consequences of differences in nutritional resources to elk body condition and pregnancy rates. We found no support for differences in dry matter digestibility between plant samples or in phenological stage based on ground sampling plots in the two study areas. Our index of nutritional resources, measured as digestible forage biomass, varied among landcover types and between study areas. We found that altered plant composition following fires was the biggest driver of differences in nutritional resources, suggesting that maintaining a mosaic of fire history and distribution will likely benefit ungulate populations. Study area, lactation status and year affected fall body fat of adult female elk. Elk in the study area exposed to lower summer range nutritional resources had lower nutritional condition entering winter. These differences in nutritional condition resulted in differences in pregnancy rate, with average pregnancy rates of 89% for elk exposed to higher nutritional resources and 72% for elk exposed to lower nutritional resources. Summer range nutritional resources have the potential to limit elk pregnancy rate and calf production, and these nutritional limitations may predispose elk to be more sensitive to the effects of harvest or predation. Wildlife managers should identify ungulate populations that are nutritionally limited and recognize that these populations may be more impacted by recovering carnivores or harvest than populations inhabiting more productive summer habitats.
Supplementary material 1 from: Zembrzuski D, Woller DA, Jech L, Black LR, Reuter KC, Overson R, Cease A (2021) Establishing the nutritional landscape and macronutrient preferences of a major United States rangeland pest, Melanoplus sanguinipes, in field and lab populations. Journal of Orthoptera Research 30(2): 163-172. https://doi.org/10.3897/jor.30.61605
Supplementary material 1 from: Zembrzuski D, Woller DA, Jech L, Black LR, Reuter KC, Overson R, Cease A (2021) Establishing the nutritional landscape and macronutrient preferences of a major United States rangeland pest, Melanoplus sanguinipes, in field and lab populations. Journal of Orthoptera Research 30(2): 163-172. https://doi.org/10.3897/jor.30.61605
Data from: Linking landscape-scale differences in forage to ungulate nutritional ecology
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Data from: Linking forest management to moose population trends: the role of the nutritional landscape
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Data from: Honeybee nutrition is linked to landscape composition
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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.
Figure 3 from: Zembrzuski D, Woller DA, Jech L, Black LR, Reuter KC, Overson R, Cease A (2021) Establishing the nutritional landscape and macronutrient preferences of a major United States rangeland pest, Melanoplus sanguinipes, in field and lab populations. Journal of Orthoptera Research 30(2): 163-172. https://doi.org/10.3897/jor.30.61605
Figure 3 Performance experiments. Survival and specific growth rates of grasshoppers from the long-term lab colony no-choice diet experiments. A. The specific growth rates for each diet treatment. Diamonds indicate the mean and bolded lines indicate the median. Boxes are +/- 25%, lines represent minimum and maximum values excluding extreme values, and dots indicate data points > 1.5 farther from the box edge than the interquartile range. Lower case letters indicate differences from Mann-Whitney post-hoc analyses. B. The proportion of grasshoppers surviving through time on each diet treatment. Most diet treatments did not have individuals die until the 5th day of the experiment, and most treatments except 7p:35c had minimal deaths (although there were no significant differences among treatments). C. Proportion of grasshoppers molting to adults over time. Most of the diets saw increases in molting from days 5–7, except diet treatment 7p:35c, which was delayed and had the least number of grasshoppers successfully molt (significantly different from all other treatments).
Figure 2 from: Zembrzuski D, Woller DA, Jech L, Black LR, Reuter KC, Overson R, Cease A (2021) Establishing the nutritional landscape and macronutrient preferences of a major United States rangeland pest, Melanoplus sanguinipes, in field and lab populations. Journal of Orthoptera Research 30(2): 163-172. https://doi.org/10.3897/jor.30.61605
Figure 2 Field IT compared to nutritional landscape. A, B. Grasshopper intake targets of the field populations (black solid line) alongside the nutrient contents of grasses (triangles) and forbs (circles) collected from the same fields. The grey solid line represents the intake target from the other field population. The dotted line represents a 1p:1c ratio. C, D. The average Euclidean distance between the plants (triangles and circles in A and B) and either the grasshopper IT from each location or the 1p:1c line. * denotes a significant difference between the Euclidean distances calculated from the IT and the 1p:1c line.
Figure 1 from: Zembrzuski D, Woller DA, Jech L, Black LR, Reuter KC, Overson R, Cease A (2021) Establishing the nutritional landscape and macronutrient preferences of a major United States rangeland pest, Melanoplus sanguinipes, in field and lab populations. Journal of Orthoptera Research 30(2): 163-172. https://doi.org/10.3897/jor.30.61605
Figure 1 Field populations and lab population ITs. Average intake target (+/- SEM) for two field populations, Bliss and Boise, ID, and the lab colony. The dashed line represents a 1:1 ratio of protein and carbohydrates, and the crosses on the data points represent SE.
Data from: Quantifying nutritional trade-offs across multidimensional performance landscapes
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