Dataset Information

Conservation of pattern as a tool for inference on spatial snapshots in ecological data.

ABSTRACT: As climate change and other anthropogenic factors increase the uncertainty of vegetation ecosystem persistence, the ability to rapidly assess their dynamics is paramount. Vegetation and sessile communities form a variety of striking regular spatial patterns such as stripes, spots and labyrinths, that have been used as indicators of ecosystem current state, through qualitative analysis of simple models. Here we describe a new method for rigorous quantitative estimation of biological parameters from a single spatial snapshot. We formulate a synthetic likelihood through consideration of the expected change in the correlation structure of the spatial pattern. This then allows Bayesian inference to be performed on the model parameters, which includes providing parameter uncertainty. The method was validated against simulated data and then applied to real data in the form of aerial photographs of seagrass banding. The inferred parameters were found to be able to reproduce similar patterns to those observed and able to detect strength of spatial competition, competition-induced mortality and the local range of reproduction. This technique points to a way of performing rapid inference of spatial competition and ecological stability from a single spatial snapshots of sessile communities.

SUBMITTER: Irvine MA

PROVIDER: S-EPMC5760736 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature

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Conservation of pattern as a tool for inference on spatial snapshots in ecological data.

Irvine Michael A MA Bull James C JC Keeling Matt J MJ

Scientific reports 20180109 1

As climate change and other anthropogenic factors increase the uncertainty of vegetation ecosystem persistence, the ability to rapidly assess their dynamics is paramount. Vegetation and sessile communities form a variety of striking regular spatial patterns such as stripes, spots and labyrinths, that have been used as indicators of ecosystem current state, through qualitative analysis of simple models. Here we describe a new method for rigorous quantitative estimation of biological parameters fr ...[more]

PMID: 29317656

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Project description:Abstract Accelerometers in animal‐attached tags are powerful tools in behavioural ecology, they can be used to determine behaviour and provide proxies for movement‐based energy expenditure. Researchers are collecting and archiving data across systems, seasons and device types. However, using data repositories to draw ecological inference requires a good understanding of the error introduced according to sensor type and position on the study animal and protocols for error assessment and minimisation. Using laboratory trials, we examine the absolute accuracy of tri‐axial accelerometers and determine how inaccuracies impact measurements of dynamic body acceleration (DBA), a proxy for energy expenditure, in human participants. We then examine how tag type and placement affect the acceleration signal in birds, using pigeons Columba livia flying in a wind tunnel, with tags mounted simultaneously in two positions, and back‐ and tail‐mounted tags deployed on wild kittiwakes Rissa tridactyla. Finally, we present a case study where two generations of tag were deployed using different attachment procedures on red‐tailed tropicbirds Phaethon rubricauda foraging in different seasons. Bench tests showed that individual acceleration axes required a two‐level correction to eliminate measurement error. This resulted in DBA differences of up to 5% between calibrated and uncalibrated tags for humans walking at a range of speeds. Device position was associated with greater variation in DBA, with upper and lower back‐mounted tags varying by 9% in pigeons, and tail‐ and back‐mounted tags varying by 13% in kittiwakes. The tropicbird study highlighted the difficulties of attributing changes in signal amplitude to a single factor when confounding influences tend to covary, as DBA varied by 25% between seasons. Accelerometer accuracy, tag placement and attachment critically affect the signal amplitude and thereby the ability of the system to detect biologically meaningful phenomena. We propose a simple method to calibrate accelerometers that can be executed under field conditions. This should be used prior to deployments and archived with resulting data. We also suggest a way that researchers can assess accuracy in previously collected data, and caution that variable tag placement and attachment can increase sensor noise and even generate trends that have no biological meaning.

Dataset Information

Conservation of pattern as a tool for inference on spatial snapshots in ecological data.

Publications

Conservation of pattern as a tool for inference on spatial snapshots in ecological data.

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