A movement ecology paradigm for unifying organismal movement research

Nathan, Ran; Getz, Wayne M.; Revilla, Eloy; Holyoak, Marcel; Kadmon, Ronen; Saltz, David; Smouse, Peter E.

doi:10.1073/pnas.0800375105

Cited by 2,269 publications

(2,533 citation statements)

References 44 publications

Supporting

Mentioning

2,423

Contrasting

Unclassified

Order By: Relevance

“…Specific questions in this context include how dispersal is affected by morphological, physiological, and behavioral traits, to what extent, the variation in dispersal rate is governed by genetic versus environmental factors (and genotype × environment interactions), and does natural selection affect relevant traits under changing environmental conditions (Nathan et al. 2008; Clobert et al. 2012).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Thermal biology of flight in a butterfly: genotype, flight metabolism, and environmental conditions

Mattila

2015

Ecology and Evolution

View full text Add to dashboard Cite

Knowledge of the effects of thermal conditions on animal movement and dispersal is necessary for a mechanistic understanding of the consequences of climate change and habitat fragmentation. In particular, the flight of ectothermic insects such as small butterflies is greatly influenced by ambient temperature. Here, variation in body temperature during flight is investigated in an ecological model species, the Glanville fritillary butterfly (Melitaea cinxia). Attention is paid on the effects of flight metabolism, genotypes at candidate loci, and environmental conditions. Measurements were made under a natural range of conditions using infrared thermal imaging. Heating of flight muscles by flight metabolism has been presumed to be negligible in small butterflies. However, the results demonstrate that Glanville fritillary males with high flight metabolic rate maintain elevated body temperature better during flight than males with a low rate of flight metabolism. This effect is likely to have a significant influence on the dispersal performance and fitness of butterflies and demonstrates the possible importance of intraspecific physiological variation on dispersal in other similar ectothermic insects. The results also suggest that individuals having an advantage in low ambient temperatures can be susceptible to overheating at high temperatures. Further, tolerance of high temperatures may be important for flight performance, as indicated by an association of heat‐shock protein (Hsp70) genotype with flight metabolic rate and body temperature at takeoff. The dynamics of body temperature at flight and factors affecting it also differed significantly between female and male butterflies, indicating that thermal dynamics are governed by different mechanisms in the two sexes. This study contributes to knowledge about factors affecting intraspecific variation in dispersal‐related thermal performance in butterflies and other insects. Such information is needed for predictive models of the evolution of dispersal in the face of habitat fragmentation and climate change.

show abstract

Section: Introductionmentioning

confidence: 99%

“…To address these questions, dissecting dispersal into its components and investigating movements at different spatial scales is a helpful approach (Nathan et al. 2008). …”

Section: Introductionmentioning

confidence: 99%

Thermal biology of flight in a butterfly: genotype, flight metabolism, and environmental conditions

Mattila

2015

Ecology and Evolution

View full text Add to dashboard Cite

show abstract

“…Individual behavior (see Glossary) underlies almost all aspects of ecology [1][2][3][4][5]. Accurate and highly resolved behavioral data are therefore critical for obtaining a mechanistic and predictive understanding of ecological systems [5].…”

Section: Measuring Behaviormentioning

confidence: 99%

“…Accurate and highly resolved behavioral data are therefore critical for obtaining a mechanistic and predictive understanding of ecological systems [5]. Historically, direct observation by trained biologists was used to quantify behavior [6,7].…”

Section: Measuring Behaviormentioning

confidence: 99%

“…For many ecological questions it is necessary to obtain quantitative information about the environmental landscape, which can be integrated with tracking data to understand how the environment influences behavior [1,5,17,57] (Box 1). Remote quantification of the environment is a key advantage of imaging over bio-logging, which only provides environmental information in the immediate vicinity of the individual to which the logger is attached.…”

Section: Call To Developers: the Ideal Automated Image-based Trackingmentioning

confidence: 99%

See 1 more Smart Citation

Automated image-based tracking and its application in ecology

Dell

Bender²,

Branson

et al. 2014

Trends in Ecology & Evolution

446

484

View full text Add to dashboard Cite

The behavior of individuals determines the strength and outcome of ecological interactions, which drive population, community, and ecosystem organization. Bio-logging, such as telemetry and animal-borne imaging, provides essential individual viewpoints, tracks, and life histories, but requires capture of individuals and is often impractical to scale. Recent developments in automated image-based tracking offers opportunities to remotely quantify and understand individual behavior at scales and resolutions not previously possible, providing an essential supplement to other tracking methodologies in ecology. Automated image-based tracking should continue to advance the field of ecology by enabling better understanding of the linkages between individual and higher-level ecological processes, via high-throughput quantitative analysis of complex ecological patterns and processes across scales, including analysis of environmental drivers. Measuring behaviorIndividual behavior (see Glossary) underlies almost all aspects of ecology [1][2][3][4][5]. Accurate and highly resolved behavioral data are therefore critical for obtaining a mechanistic and predictive understanding of ecological systems [5]. Historically, direct observation by trained biologists was used to quantify behavior [6,7]. However, the extent and resolution to which direct observations can be made is highly constrained [8] and the number of individuals that can be observed simultaneously is small. In addition, an exact record of events is not preserved, only the biologist's subjective account of them.Recent technological advances in tracking now make it possible to collect large amounts of highly precise and accurate behavioral data. For many organisms equipment can be attached that provide information about the Glossary Background subtraction: a method used by software to compare the current video frame with a stored picture of the background; any pixel of the current frame that is significantly different from the corresponding pixel in the background is likely to be associated with the body of an animal. Useful in situations where the background is unchanging, for example, when the surface of the background is rigid and lighting does not change. Behavior: the actions of individuals, often in response to stimuli. Behavior can involve movement of the individual's body through space, such as walking or chasing, or can occur while the animal is stationary, such as grooming or eating. Bio-logging: attachment or implantation of equipment to organisms to provide information about their identity, location, behavior, or physiology (e.g., global positioning systems, accelerometers, video cameras, telemetry tags). Ecological interaction: any interaction between an organism and its environment, or between two organisms (i.e., including interactions between conspecifics). Fingerprinting: a method used to identify unmarked individuals using natural variation in their physical and/or behavioral appearance. The method works by transforming the images of each individual ...

show abstract

Dispersal kernel of green rice leafhopper estimated from truncated data

Andow

2022

Population Ecology

View full text Add to dashboard Cite

Characterizing dispersal kernels from truncated data is important for managing and predicting population dynamics. We used mark‐recapture data from 10 previously published replicated experiments at three host plant development stages (seedling, tillering, and heading) to estimate parameters of the normal and exponential dispersal kernels for green rice leafhopper, Nephotettix cincticeps (Uhler). We compared classic statistical methods for estimating untruncated distribution parameters from truncated data with maximum likelihood (MLE) and the method of statistical moments for simulated and empirical data. Simulations showed that both methods provided accurate parameter estimates with similar precision. The method of moments is algebraically complex, but simple to calculate, while the MLE methods require numerical solutions of nonlinear equations. Simulations also showed that accurate, precise estimates of the parameters of the untruncated distributions could be attained even under severe truncation with sufficient numbers of recaptures. Both diffusivity and the exponential mean were higher with later plant growth stage, showing that insects moved farther and faster at the heading stage. Precision of the estimates was not strongly related to percent capture, size of the experimental field, or the number of leafhoppers captured. The leptokurtic exponential kernel fit the data better than the normal kernel for all the experiments. These results support an alternative explanation for the strong density‐dependent population regulation of this species at the heading stage. Instead of leafhopper density per se, the increase in movement at this stage could integrate the populations in the separate fields, leveling densities throughout the landscape.

show abstract

A movement ecology paradigm for unifying organismal movement research

Cited by 2,269 publications

References 44 publications

Thermal biology of flight in a butterfly: genotype, flight metabolism, and environmental conditions

Thermal biology of flight in a butterfly: genotype, flight metabolism, and environmental conditions

Automated image-based tracking and its application in ecology

Dispersal kernel of green rice leafhopper estimated from truncated data

Contact Info

Product

Resources

About