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The temporal scale of diet and dietary proxies - PubMed

  • ️Fri Jan 01 2016

Review

. 2016 Mar 2;6(6):1883-97.

doi: 10.1002/ece3.2054. eCollection 2016 Mar.

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Review

The temporal scale of diet and dietary proxies

Matt Davis et al. Ecol Evol. 2016.

Abstract

Diets estimated from different proxies such as stable isotopes, stomach contents, and dental microwear often disagree, leading to nominally well-supported but greatly differing estimates of diet for both extinct and extant species that complicate our understanding of ecology. We show that these perceived incongruences can be caused by proxies recording diet over vastly different timescales. Field observations reveal a diet averaged over minutes or hours, whereas dental morphology may reflect the diet of a lineage over millions of years of evolution. Failing to explicitly consider the scale of proxies and the potentially large temporal variability in diet can cause erroneous predictions in any downstream analyses such as conservation planning or paleohabitat reconstructions. We propose a cross-scale framework for conceptualizing diet suitable for both modern ecologists and paleontologists and provide recommendations for any studies involving dietary data. Treating diet in this temporally explicit framework and matching the scale of our questions with the scale of our data will lead to a much richer and clearer understanding of ecological and evolutionary processes.

Keywords: Diet; dietary proxies; isotopes; microwear; temporal scale; time averaging.

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Figures

Figure 1
Figure 1

The different temporal scales over which proxies record diet. (A) A proxy's resolution is given by its temporal grain. Proxy extent is the range of time over which a proxy can be used. Proxies marked by a pointed end have ranges that extend past the graph limits. Double‐pointed ends indicate that proxies can be used in exceptional fossil cases. (B) The perceived diet of the African elephant (Loxodonta africana) depends on the time span over which it is measured. Blue‐shaded area represents dietary limits of elephants at different scales estimated from observational and fossil evidence. Gray lines show actual diets at different scales computed from a 6‐year isotopic record (Cerling et al. 2009). Four lines are highlighted in color to show how perceived diet changes as it is averaged for longer periods of time. Both graphs share the same logged x‐axis given in years above and common calendar units below.

Figure 2
Figure 2

Hypothetical example illustrating the effects of temporal averaging on diet and how it can cause the same diet to appear different in two species. (B) The “true” percentage of C4 plants consumed taken from a 6‐year isotopic record (Cerling et al. 2009) of African elephants (Loxodonta africana) is shown by the gray line. If elephants and striped mice (Lemniscomys striatus) each consumed the same diet shown by the gray line, different mass‐related turnover times would cause stable isotopes in their muscle tissue to produce the different “measured” diets shown by the red and blue lines, respectively. If an ecologist sampled elephant and mouse muscle tissue on 15 random days in the late spring in two separate years (circles), she would reconstruct elephants and mice as having very different dietary distributions, (A) even though they were consuming the exact same percentages of C4 plants at the same time.

Figure 3
Figure 3

Diagrammatic example based off of O'Reilly et al. (2002) showing a simple food chain in Lake Tanganyika, East Africa that is flipped upside down with the measured isotopic values of phytoplankton and zooplankton making them appear more trophically enriched than their predators, fish. The anomaly was explained by lower trophic levels recording a recent upwelling of nitrate that had not yet been integrated into fish muscle, a tissue that records dietary changes much more slowly than the full body isotopes of phytoplankton and zooplankton.

Figure 4
Figure 4

Diagrammatic example based off of Reynolds‐Hogland and Mitchell (2007) showing how changes in the temporal grain of an investigation can completely alter the conclusions of a study. (A) Logging seems to have no long‐term effect on American black bear (Ursus americanus) foraging patterns but a better understanding of the study system shows that yearly averages are not the correct scale to capture the biological and behavioral changes that logging brings. (B) Seasonal or biannual averages reveal that logging greatly alters bears' foraging patterns.

Figure 5
Figure 5

Still life of grizzly bear (Ursus arctos) with diet inferred from multiple dietary proxies like gross morphology and isotopes of hair, teeth, and blood. Division of Vertebrate Zoology,

YPM MAM

9751. Courtesy of the Peabody Museum of Natural History, Yale University, New Haven,

CT

,

USA

.

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