ecological indicators 8 (2008) 270–284
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Measures of structural complexity in digital images for monitoring the ecological signature of an old-growth forest ecosystem Raphae¨l Proulx, Lael Parrott * Complex Systems Laboratory, Department of Geography, University of Montreal, C.P. 6128 succursale Centre-ville, Montreal, Que., Canada H3C 3J7
article info
abstract
Article history:
Conducting field samples for monitoring ecological dynamics across multiple spatiotem-
Received 28 September 2006
poral scales is a difficult task using standard protocols. One alternative is to measure a
Received in revised form
restricted set of variables which can serve as an ecological orientor (EO) for quantifying
14 February 2007
habitat change. The objective of this article is to derive from digital images a measure of
Accepted 19 February 2007
structural complexity that may serve as a proximate EO for monitoring forest dynamics in space and time. The mean information gain (MIG) index was used as a measure of structural complexity in photographs taken directly in the field over the entire growing season. At a
Keywords:
small scene extent, the complexity of light intensity variations in digital images was
Ecological orientor
positively related to species richness. At larger scene extents, forest understorey and
Structural complexity
overstorey layers showed predictable ecological signatures in structural complexity. In
Imagery
general, intensity and chroma were the two color space components which yielded the
Forest
greatest sensitivity to habitat change through time. Within the framework of a standardized
Spatiotemporal dynamics
photographic protocol, it seems therefore reasonable to consider MIG as a suitable EO for
Shannon entropy
monitoring forest dynamics in both space and time. Our results support the idea that it is
Mean information gain
possible on one hand to adopt a more holistic view of ecological processes to gain, on the other hand, spatial and temporal degrees of freedom for testing multiple scale hypotheses in the field. # 2007 Elsevier Ltd. All rights reserved.
1.
Introduction
It is well established that although mechanisms driving ecological dynamics are not well understood at the community (intermediate) level, ecosystem theory is nonetheless supported by robust principles occurring at either larger (e.g., landscape scaling laws; Lawton, 1999; Gaston, 2000) or smaller (e.g., individual based allometric laws; Turchin, 2001; Marquet et al., 2005; West and Brown, 2005) levels of taxonomic resolution (see also Simberloff, 2004). Notwithstanding this fact, ecologists are now faced with the additional challenge of
uncovering organizing principles governing ecological dynamics across multiple spatial and temporal scales (Sole´ et al., 1999; Brown et al., 2002; Storch and Gaston, 2004). In this context, organizing principles in natural systems may be regarded as an epiphenomenon of heuristic ecological goal functions (Wilhelm and Bru¨ggeman, 2000) which encompass the formation of higher-level structures emerging from lower-level interactions (Margalef, 1963; Christensen, 1995; Ulanowicz, 2004; Mu¨ller, 2005). In the last century the search for goal functions, also more properly termed ecological orientors (EO) in Mu¨ller and Leupelt (1998), has spread so
* Corresponding author. Tel.: +1 514 343 8064; fax: +1 514 343 8008. E-mail addresses:
[email protected] (R. Proulx),
[email protected] (L. Parrott). 1470-160X/$ – see front matter # 2007 Elsevier Ltd. All rights reserved. doi:10.1016/j.ecolind.2007.02.005
ecological indicators 8 (2008) 270–284
widely among ecosystem theories that reviewing all of them would necessitate an article length task. Any proximate EO (as opposed to an absolute or universal one) is defined as the quantity a living system tends to optimize, from a nonteleological view, in the course of its development. Contemporary examples include concepts of: fitness, productivity, stability, resilience, exergy, emergy, ascendancy, network efficiency, metabolic activity, and criticality among others (Odum, 1969; Kauffman, 1995; Mu¨ller and Leupelt, 1998; Jorgensen and Mu¨ller, 2000; Fath et al., 2001). All these concepts are useful descriptors of ecological communities, but share the common drawback that their estimation requires excessively large amounts of input data and variables that are difficult to measure in the field. Consequently a trade-off exists between the number of variables, replicates, and visits that one can include in a field sampling protocol, where each aspect (i.e., descriptive, spatial and temporal) is constrained by extent and resolution limits (Fig. 1). It follows that most ecological field protocols are misbalanced towards maximizing the number of descriptive variables, counterbalanced by poor spatial (or temporal) extent and resolution (Proulx, in press). In other words, proximate EO are often constructed from a single-scaled multivariate standpoint (e.g., matrices of environmental or community descriptors), preventing us from collecting sufficiently long spatiotemporal datasets in situ. Thus, to incorporate more spatial and temporal degrees of freedom in field protocols for monitoring ecosystems, a simple, rapid and preferably cost effective EO sampling should be performed. In a recent meta-analysis, Tews et al. (2004) reviewed a proximate EO known as the habitat heterogeneity hypothesis which arises from the empirical positive relationship between habitat and species diversity (e.g., MacArthur and MacArthur, 1961; Roth, 1976). The authors showed how modifying scales can affect our interpretation of this relationship and how sampling constraints have systematically favored the descriptive component of field protocols; i.e., measuring a large number of variables (Fig. 1). Another important idea in Tews et al. (2004) is that habitat heterogeneity is better quantified by its structural complexity rather than its diversity per se. This
idea links with the arguments of Anand and Tucker (2003) who call for a shift of emphasis from diversity (counts of biological objects at a given time and place) to complexity measures (spatiotemporal structure of a set of biological objects at a given scale). For instance, two sites may well contain the same proportion of the same biological objects but nonetheless show very different complexity. Measures of complexity thus require more than just a statistical distribution of parts. Forest ecology has a long tradition of linking overstorey and understorey vegetation structures to historical succession dynamics (Bazzaz, 1975; Denslow, 1987; Whitney and Foster, 1988; reviewed in Millet et al., 1998). Contemporary measures of vegetation structure were devised to quantify plant architecture in a strict geometrical sense (Jennings et al., 1999; Jonckheere et al., 2004; Parker et al., 2004) as opposed to other measures of community composition. Considering that forest light regimes in overstorey and understorey layers is an important determinant of ecological processes at various scales (Endler, 1993; Trichon et al., 1998; The´ry, 2001; Valladares et al., 2002; Montgomery, 2004) and that the dynamics of forest light can be captured by photographs, it appears logical to consider digital images as a starting point for monitoring forest dynamics. In particular, we hypothesize that heterogeneity in forest light can serve as an indicator of the structural complexity of the vegetation. The principal objective of this article is to derive from digital images a measure of structural complexity that may serve as a proximate EO for monitoring forest dynamics in space and time. This objective relies on two key assumptions: (1) mean information gain (MIG) represents a relevant measure of the structural complexity in digital images, and (2) structural complexity is a proximate EO of a forest ecosystem. More specifically we aim to show the existence of a positive relationship between MIG and species richness at small scene extents. Furthermore, we expect to find predictable spatial and seasonal patterns of structural complexity in forest understorey and overstorey vegetation layers at larger extents.
2.
Fig. 1 – The sampling triangle illustrates, on one hand, the necessity of increasing descriptive, spatial and temporal resolution and extent for studying ecosystem changes at multiple scales. On the other hand, for practical reasons a trade-off always exists between the number of variables, the sample size and the visiting frequency anyone can achieve within the framework of a field protocol.
271
Methodology
This study was conducted at the Gault Nature Reserve (Mount St-Hilaire, Que´bec, Canada), an old-growth forest which comprises about 700 of the 1600 regional species of vascular plants in a 10 km2 area, where dominant trees are sugar maple (Acer saccharum) and American beech (Fagus grandifolia). The Reserve shelters a gradient of species assemblages and geographic conditions which have been extensively studied by various research groups (www.mcgill.ca/gault/research/ bibliography). Field experiments were carried out over two growing seasons and involved two types of experimental protocols; hereafter described as snapshot and trajectory experiments. The two experiments were designed to address complementary questions regarding the relevance of our approach at different temporal and scene extents. The snapshot experiment aimed to evaluate the existence of a positive relationship between structural complexity and species richness at small scene extents (
