We utilized a long-term data base collected over a broad geographic range to examine predator size -prey size relationships for 18 species of marine fish predators from continental shelf waters off the northeast US coast. Regression analysis was used to illustrate interspecific variation in ontogenetic patterns of prey size use, gape allometries, and ratio-based trophic niche breadths. Sizebased feeding strategies were assessed through comparison of frequency distributions of relative prey sizes eaten and were related to general predator feeding tactics and gape morphology. The results demonstrated that the range of prey sizes eaten expanded with increasing predator body size for each of the marine predators examined, leading to asymmetric predator size -prey size distributions. Absolute maximum prey size and slopes of maximum prey size versus predator size varied widely among predator taxa. Distinct size-based feeding strategies were evident, as diets of some predators were dominated by prey that were 10 to 20% of predator size, whereas other predators frequently consumed prey > 50% of predator size. Gape sizes and allometric relationships with body size were also diverse among predators and often were closely associated with maximum prey sizes. Ratio-based trophic-niche breadths generally did not expand with predator ontogeny and tended to narrow for the largest predators, which may be common for animal taxa.
Scatter diagrams have historically proved useful in the study of associative relationships in ecology. Several important ecological questions involve correlations between variables resulting in polygonal shapes. Two examples that have received considerable attention are patterns between prey size and predator size in animal populations and the relationship between animal abundance and body size. Each is typically illustrated using scatter diagrams with upper and lower boundaries of response variables often changing at different rates with changes in the independent variables. Despite recent statistical contributions that have stimulated an interest in characterizing the limits of a variable, a consensus on an appropriate methodology to quantify the boundaries of scatter diagrams has not yet been achieved. We tested regression techniques based on least squares and least absolute values models using several independent data sets on prey length and predator length for piscivorous fishes and compared estimated slopes for consistency. Our results indicated that least squares regression techniques were particularly sensitive to outlying y values and irregularities in the distribution of observations, and that they frequently produced inconsistent estimates of slope for upper and lower bounds. In contrast, quantile regression techniques based on least absolute values models appeared robust to outlying y values and sparseness within data sets, while providing consistent estimates of upper and lower bound slopes. Moreover, the use of quantile regression eliminated the need for an excess of arbitrary decision-making on the part of the investigator. We recommend quantile regression as an improvement to currently available techniques used to examine potential ecological relationships dependent upon quantitative information on the boundaries of polygonal relationships.
Scatter diagrams have historically proved useful in the study of associative relationships in ecology. Several important ecological questions involve correlations between variables resulting in polygonal shapes. Two examples that have received considerable attention are patterns between prey size and predator size in animal populations and the relationship between animal abundance and body size. Each is typically illustrated using scatter diagrams with upper and lower boundaries of response variables often changing at different rates with changes in the independent variables. Despite recent statistical contributions that have stimulated an interest in characterizing the limits of a variable, a consensus on an appropriate methodology to quantify the boundaries of scatter diagrams has not yet been achieved. We tested regression techniques based on least squares and least absolute values models using several independent data sets on prey length and predator length for piscivorous fishes and compared estimated slopes for consistency. Our results indicated that least squares regression techniques were particularly sensitive to outlying y values and irregularities in the distribution of observations, and that they frequently produced inconsistent estimates of slope for upper and lower bounds. In contrast, quantile regression techniques based on least absolute values models appeared robust to outlying y values and sparseness within data sets, while providing consistent estimates of upper and lower bound slopes. Moreover, the use of quantile regression eliminated the need for an excess of arbitrary decision‐making on the part of the investigator. We recommend quantile regression as an improvement to currently available techniques used to examine potential ecological relationships dependent upon quantitative information on the boundaries of polygonal relationships.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.