Locating Discontinuities along Ecological Gradients

Ludwig, John A.; Cornelius, Joe M.

doi:10.2307/1939277

Cited by 132 publications

(84 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Because n in our three data sets varied from 35 (mammals) to 106 (birds), and because we were most interested in determining community structure, we maintained a constant statistical power of approximately 0.50 when setting alpha for detecting discontinuities. Although we believe that the application of a null model is the best method for determining body-mass aggregations, we confirmed our results with a form of the split moving-window boundary analysis [SMW; Webster (1978) and Ludwig and Cornelius (1987)] and hierarchical cluster analysis (SAS Institute 1985).…”

Section: Discussionsupporting

confidence: 77%

Body Mass Patterns Predict Invasions and Extinctions in Transforming Landscapes

1999

View full text Add to dashboard Cite

Scale-specific patterns of resource distribution on landscapes entrain attributes of resident animal communities such that species body-mass distributions are organized into distinct aggregations. Species within each aggregation respond to resources over the same range of scale. This discontinuous pattern has predictive power: invasive species and extinct or declining species in landscapes subject to human transformation tend to be located at the edge of body-mass aggregations (P Ͻ 0.01), which may be transition zones between distinct ranges of scale. Location at scale breaks affords species great opportunity, but also potential crisis.

show abstract

Section: Discussionsupporting

confidence: 77%

Body Mass Patterns Predict Invasions and Extinctions in Transforming Landscapes

1999

View full text Add to dashboard Cite

show abstract

“…To detect discontinuities in the unevenly distributed time series of individual isotope ratio values, data were arranged chronologically and a split moving-window analysis was performed. The analysis is a modification of the method used by Webster (1973) to detect boundaries in soil analysis and by Ludwig & Cornelius (1987) to detect discontinuities along ecological gradients. At the beginning of the time series, a window consisting of the first 16 records was introduced.…”

Section: In Harbour Porpoisesmentioning

confidence: 99%

Stable isotope evidence of long-term changes in the North Sea food web structure

Christensen¹,

Richardson²

2008

Mar. Ecol. Prog. Ser.

View full text Add to dashboard Cite

“…This temporal sectioning was undertaken by first making divisions each day at dawn and dusk, estimated using a split-moving-window analysis (Ludwig and Cornelius 1987) on proportion of time at depth. This approach, which estimates points of significant change in a one-dimensional signal, has been used elsewhere to divide the vertical behavior of animals into sections over longer timescales (Humphries et al 2010).…”

Section: Data Pre-processingmentioning

confidence: 99%

Objective classification of latent behavioral states in bio‐logging data using multivariate‐normal hidden Markov models

Phillips

Patterson

Leroy

et al. 2015

Ecological Applications

View full text Add to dashboard Cite

Abstract. Analysis of complex time-series data from ecological system study requires quantitative tools for objective description and classification. These tools must take into account largely ignored problems of bias in manual classification, autocorrelation, and noise. Here we describe a method using existing estimation techniques for multivariate-normal hidden Markov models (HMMs) to develop such a classification. We use high-resolution behavioral data from bio-loggers attached to free-roaming pelagic tuna as an example. Observed patterns are assumed to be generated by an unseen Markov process that switches between several multivariate-normal distributions.Our approach is assessed in two parts. The first uses simulation experiments, from which the ability of the HMM to estimate known parameter values is examined using artificial time series of data consistent with hypotheses about pelagic predator foraging ecology. The second is the application to time series of continuous vertical movement data from yellowfin and bigeye tuna taken from tuna tagging experiments. These data were compressed into summary metrics capturing the variation of patterns in diving behavior and formed into a multivariate time series used to estimate a HMM. Each observation was associated with covariate information incorporating the effect of day and night on behavioral switching.Known parameter values were well recovered by the HMMs in our simulation experiments, resulting in mean correct classification rates of 90-97%, although some variance-covariance parameters were estimated less accurately. HMMs with two distinct behavioral states were selected for every time series of real tuna data, predicting a shallow warm state, which was similar across all individuals, and a deep colder state, which was more variable. Marked diurnal behavioral switching was predicted, consistent with many previous empirical studies on tuna.HMMs provide easily interpretable models for the objective classification of many different types of noisy autocorrelated data, as typically found across a range of ecological systems. Summarizing time-series data into a multivariate assemblage of dimensions relevant to the desired classification provides a means to examine these data in an appropriate behavioral space. We discuss how outputs of these models can be applied to bio-logging and other imperfect behavioral data, providing easily interpretable models for hypothesis testing.

show abstract

Locating Discontinuities along Ecological Gradients

Cited by 132 publications

References 9 publications

Body Mass Patterns Predict Invasions and Extinctions in Transforming Landscapes

Body Mass Patterns Predict Invasions and Extinctions in Transforming Landscapes

Stable isotope evidence of long-term changes in the North Sea food web structure

Objective classification of latent behavioral states in bio‐logging data using multivariate‐normal hidden Markov models

Contact Info

Product

Resources

About