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Hansen et al. Edge effects across ecosystem types

Ecosystem Biomass as a Framework for Predicting Habitat Edge Effects

Running Head: Edge effects across ecosystem types

Key Words: biomass, conservation principles, edge effects, forest interior species, habitat

fragmentation, microclimate

Word Count: 7150

Andrew James Hansen, Ecology Department, Montana State University, Bozeman, MT 59717-

3460

Lisa Baril, Ecology Department, Montana State University, Bozeman, MT 59717-3460

Jennifer Watts, Land Resources and Environmental Sciences Department, Montana State

University, Bozeman, MT 59717-3460

Fletcher Kasmer, Montana State University, Bozeman, MT 59717-3460

Toni Ipolyi, Montana State University, Bozeman, MT 59717-3460

Ross Winton, Entomology Department, Montana State University, Bozeman, MT 59717-3460

Corresponding Author: Andrew Hansen, Ecology Department, 310 Lewis Hall, Montana State

University, Bozeman, MT 59717-3460. [email protected]

25 February 2008

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Abstract: Past studies of the consequences of human-induced edge effects on native

biodiversity have had mixed results leading to confusion on if and how to manage to avoid

negative edge effects in different forested ecosystem types. The purpose of this paper is to

evaluate if forest ecosystems differ predictably in response to edge effects based on forest

structure and density. The Biomass Accumulation Hypothesis (Hansen & Rotella 2000) asserts

that edge effects have the highest magnitude of influence in ecosystems that accumulate high

levels of standing aboveground vegetation biomass. We test two predictions of this hypothesis:

1) The dense vegetation in mid to late seral habitats within high-biomass ecosystems buffers

microclimate, resulting in large differences in average microclimate between disturbance patch

edges and forest interiors; 2) In high biomass ecosystems the sharp gradient in vegetation and

microclimate results in finer habitat partitioning by organisms and more forest interior specialists

than is the case in lower biomass ecosystems. These predictions were tested by statistical

analysis of the results of published studies in several of the major forest biomes. We found that

magnitude of edge influence of microclimate was significantly related to forest biomass for light

intensity and relative humidity. Trends for air temperature, soil temperature, vapor pressure

deficit, and soil moisture were as predicted, but not statistically significant. A model including

all microclimate samples and controlling for microclimate variable type was significant. The

percent of species that specialized on forest interiors was significantly related to biomass for

mammals and birds, and nearly significant for beetles. The results suggest that forest

fragmentation is most likely to negatively influence forest species in high biomass ecosystems

such as tropical and temperate rainforests, but may have little influence on forest species in low-

biomass ecosystems such as boreal or subalpine forests. These finding provide a basis for

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itcmaint, 10/22/08,

Express as varionation over the biomass gradient.


prioritizing the management of habitat fragmentation appropriately across the world’s forest

ecosystems.

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Introduction

Human expansion has led to fragmentation of many natural ecosystems (Saunders et al.

1991). Habitat fragmentation results in both a reduction in total area of habitat and change in

spatial configuration of remaining habitat patches (Wilcove et al. 1986). As fragmentation

proceeds, changes in habitat patch size and number can lead to increases in abrupt edges between

remaining habitat patches and the expanding matrix. While some native species thrive at habitat

edges, others are adapted to habitat patch interiors and may become extinct in landscapes where

habitat is dominated by edge effects (Noss et al. 2006a).

The vast body of research on the consequences of human-induced habitat edge effects on

native biodiversity, however, has shown conflicting results, (Paton 1994; Murcia 1995; Harper et

al. 2005) leading to confusion concerning management. A major review by Matlack & Litvaitis

(1999) concluded, “The negative repercussions of edge habitats have prompted changes in

forestry practices …. Yet there is considerable variability in edge response among forest sites

and species, which makes it difficult to formulate a consistent edge management policy.” (pg.

222). More recently, Fahrig (2003) concluded that change in spatial configuration of habitat

(including edges), independent of habitat loss, “… has rather weak effects on biodiversity, which

are as likely to be positive as negative.” (pg. 508). She states regarding management, “…

conservation actions that attempt to minimize fragmentation (for a given habitat amount) may

often be ineffectual.” (pg., 508). A caution is offered, however, that that negative edge effects

may be much stronger in tropical than in temperate systems, but, “This prediction remains to be

tested.” (pg 508). Thus, substantial uncertainty remains on which ecosystem types are sensitive

to edge effects and which management strategies are most effective for maintaining native

biodiversity in a given ecosystem.

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Efforts to explain the variable findings of these edge studies have focused primarily on

local-scale factors; the characteristics of the edges and of the surrounding landscape. These

factors include the age of habitat edges, edge aspect, the combined effects of multiple nearby

edges, fragment size, the structure of the adjoining matrix vegetation, influxes of animals or

plant propagules from the matrix, extreme weather or disturbance events, and land use in the

surrounding landscape (Murcia 1995; Harrison & Bruna 1999; Noss et al. 2006a; Laurance et al.

2007). Such local scale factors can produce striking variability in edge effects within the same

region (Laurance et al. 2007). These local factors may cause difficulty in predicting the nature

and magnitude of edge effects and may also explain some of the conflicting results in the

literature (Harper et al. 2005).

While local variation in edge effects is increasingly well understood, the extent of

variation in edge effects among major ecosystems or biomes is not adequately studied (Murcia

1995). Harper et al. (2005) reviewed studies of vegetative and microclimate patterns across

edges. They suggested that edge effects should be more pronounced in regions with high patch

contrast, infrequent natural disturbance and low natural vegetation heterogeneity, high solar

radiation and low cloudiness, and relatively few pioneer species. None of these hypotheses have

been tested at continental or global scales.

The goal of this paper is to examine one of these hypotheses, that involving patch

contrast as elaborated by the Biomass Accumulation Hypothesis. This hypothesis (Hansen &

Rotella 2000) asserts that edge effects have the highest magnitude of influence in ecosystems

that accumulate high levels of standing aboveground vegetation biomass. We examine how well

the predictions of this hypothesis are supported by published studies from forest ecosystem types

spanning the gradient in global forest biomass accumulation. Our hope is that this paper will

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prompt additional tests of this and other hypotheses and lead to an improved understanding of

variation globally in the effects of habitat fragmentation. Such knowledge would better allow

land managers to identify and employee the conservation strategies that are likely to be most

effective in their particular ecosystems.

The Biomass Accumulation Hypothesis

The Biomass Accumulation Hypothesis is a logical extension of the considerable body of

work on the role of vegetation structure in edge effects (reviewed by Harper et al. 2005). The

abrupt edge transition comprising human-induced edges in forest systems is typically in

vegetation structure, from patches with lower vegetation height and/or density to forest stands

with higher vegetation height and/or density. Because dense vegetation buffers microclimate

and disturbances, forest patch interiors often have less extreme microclimates and disturbance

regimes than forest edges (e.g., Chen et al.1999). These more moderate interior forest conditions

often support plant and animal species that are uniquely adapted to such conditions (Matlack &

Litvaitis 1999). Creating edges within or near forest interior habitat can reduce this vegetation

buffer allowing more extreme conditions that may be intolerable for forest interior species.

Ecosystem energy is increasingly understood to be a major factor organizing community

diversity, species abundances, and ecosystem processes. Globally, radient energy and primary

productivity are the primary correlates with species richness (Waide et al. 1999, Mittelbach et al.

2001, Gaston 2000, Hawkins et al. 2003). Richness increases with diversity in low energy

systems, possibly because higher energy favors larger population sizes (Wright 1983, Srivastava

and Lawton 1998, Evans et al. 2007)). Richness sometimes decreases with energy in high

energy systems, possibly because it favors competitive dominance by a few species and because

variation in energy and thus species niches are reduced in high energy ecosystems (Rosenzweig

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and Abramsky 1993, Huston 1994). Ecosystem energy also influences vegetation structure and

disturbance frequency, both of which may bear on edge effects.

The major forest ecosystems of the world differ predictably in vegetation height and

density as determined by forest productivity and disturbance (Perry 1994). High levels of

vegetation structure result from high net primary productivity and/or infrequent loss of

vegetation to disturbance. Net primary productivity is favored by warm temperatures, high solar

radiation, high moisture, and fertile soils (Running et al. 2004). Stand replacing disturbances are

often less frequent in highly productive forests (White & Jentsch 2001). This is because fires are

inhibited by high precipitation and humidity within the forest, deep fertile soils inhibit

windthrow, and healthy, productive trees are generally less susceptible to catastrophic insect and

disease outbreaks. Across the forests of the earth, vegetation structure, expressed as

aboveground biomass of mid to late seral stages, is highest in wet tropical and temperate forests,

intermediate in moist temperate forests, and lowest in dry or cold forests (Perry 1994).

Merging concepts on drivers of edge effects and knowledge of global patterns of forest

productivity and structure, the Biomass Accumulation Hypothesis asserts that edge effects have

the highest magnitude of influence in ecosystems that accumulate high levels of standing

aboveground vegetation biomass (AGB). AGB accumulation refers to the total dry weight of

vegetation present within a forest in mid to late successional stages, typically quantified as tons

per hectare. High AGB forests are often characterized by high canopies, multiple canopy layers,

and high foliage height diversity.

The hypothesis predicts that forest ecosystem types with high AGB accumulation will

have greater contrast in microclimate, decomposition, vegetation structure, and disturbance rates

between edge and forest interior that forest ecosystem types with low AGB accumulation (Fig 1).

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This steeper gradient in resources and conditions may be more finely partitioned by plants and

animals, leading to a greater percentage of species specializing on forest interior, edge, or

disturbance patch interior conditions (need ref for this) in high compared to low AGB

accumulation forest ecosystem types.

This hypothesis was developed by Hansen and Rotella (2000) to account for differences

in the prevalence of edge and interior specialists between the temperate maritime rainforests and

the cold temperate continental forests in the Pacific and Inland Northwest US. Forests in this

region have broadly similar natural disturbance regimes, similar land use patterns, and broadly

overlapping pools of species. Hence, the influence of AGB accumulation on edge effects can be

examined in this region while controlling for the other hypotheses mentioned above. In the first

test of the Biomass Accumulation Hypothesis, WcWethy et al. (in press) found that more bird

species responded to changes in edge density in more productive west-slope Cascade forests than

less productive east-side Cascade forests and that bird community similarity in the productive

west-slope Cascade forests differed across low and high levels of edge density whereas no such

differentiation occurred in harsh, east-side Cascade forests. These findings lead us to examine

the hypothesis across a greater range of forest ecosystem types.

Objectives

This paper examines the degree to which previously published studies from a wide range

of forest ecosystem types support or refute the Biomass Accumulation Hypothesis. A key

conservation concern with regards to fragmentation is loss of species dependent upon interior

habitats. Consequently we choose as a meaningful measure of community response to

fragmentation the proportion of species in the community statistically associated with forest

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interiors because these are species that are likely to be lost if fragmentation proceeds. The

hypothesis suggests that degree of contrast in resources and conditions from edge to interior is

the mechanism underlying species responses. Thus, we also examine change in microclimate

from edge to interior.

The predictions tested were:

1. Contrast in microclimate from forest edge to interior is higher in forest ecosystem types

with higher AGB accumulation than those with lower biomass.

2. Because of the sharp gradient in vegetation and microclimate from forest interior to edge

in high AGB ecosystems, a greater percentage of species found in forest interiors are

significantly less abundant near forest edges than is the case in lower AGB ecosystems.

The notion that edge effects differ predictably across the world’s forests as a function of

their inherent abilities to accumulate biomass, while simple and intuitive, has not been

previously examined in the peer review literature nor discussed in forest conservation and

management texts. Increased knowledge on this hypothesis and others may eventually

contribute to a framework for prioritizing conservation and management strategies for habitat

fragmentation across the major forest ecosystem types.

Methods

Overview of Methods

We drew on published research on edge effects for forest ecosystem types ranging from

low to high in AGB. The goal was to summarize results on patterns of microclimate and species

distributions across abrupt forest edges. Thus, we focused on studies that quantified response

variables along transects placed perpendicular to forest edges, expressly, newly created edges

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between nonforest or early successional forest and mid or late-seral forest. This allowed us to

select studies that had similar study designs and allowed a direct test of the predictions.

We used standard literature search engines such as Web of Science to identify published

papers to consider for the study. Search terms such as “edge effects”, “ecotones”, “aboveground

biomass”, “microclimate”, and “species composition” were used. We also identified candidate

studies from the literature cited in published papers. Candidate papers where then scrutinized

relative to the criteria we specified for each of the predictions. The data were analyzed by

regressing the microclimate and interior species edge response variables across edges on AGB of

the forest.

Vegetation AGB

We estimated AGB for the selected studies by drawing on 10 published studies of AGB

from similar forest types (Fig. 2). These biomass studies synthesized results from a total of 59

ecosystems. We eliminated estimates from early seral forests, and non-native forests, which left

53 estimates of AGB in mid to late seral forests. These studies used various methods to estimate

AGB, including allometric, modeling, and remote sensing approaches. The accuracy of the AGB

data was not quantified. We grouped the results of these 53 ecosystems into one of seven biome

types (Olson et al. 2001) and averaged AGB within types to represent AGB levels typical of the

biome.

Prediction 1: Microclimate

Criteria for inclusion of published studies in this analysis were: 1) study designs

compared edge and/or matrix microclimate variables to interior microclimate variables; 2) the

disturbance patch adjacent to the forest had very low AGB and microclimate there approximated

that on ground uninfluenced by vegetation; 3) the mid and late-seral forests studied were in

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patches large enough to exhibit interior microclimate conditions; 4) the microclimate variables

were measured during the dry season in tropical evergreen forests or during the growing season

in tropical deciduous, temperate and boreal forests; and 4) the microclimate variables were

measured during mid-morning to mid-afternoon; 5) the results among studies were not rendered

incomparable due to edge orientation, season, or other factors. Thirteen studies met these criteria

and were included in the analysis (Table 1). These spanned the gradient in biome types and

AGB levels.

The microclimate variables reported differed among studies. The following variables

were studied frequently enough to include in our comparison: light levels; air temperature; soil

temperature; humidity; vapor pressure deficit; and soil moisture. All variables were measured at

intervals along transects from forest edge and/or matrix to forest interior using either a hand-held

sensor or a semi-permanent microclimate monitoring station situated near the ground or a

maximum of two meters in height. Measurements used were those taken simultaneously or

within two hours of each other along transects.

We extracted from each paper estimates for a given microclimate variable for forest

interior and forest edge. Interior values used were those that did not differ significantly as a

function of distance from edge. We assumed that this indicated that these locations were not

influenced by proximity to edges. Edge or matrix values were averages of those reported. The

distance from edge for interior conditions varied among studies (range: 30 m to 120 m). Exact

values were obtained from tables when available in the publications and estimations from figures

were used otherwise.

We used these data to estimate magnitude of edge influence (MEI). Following Harper et

al (2005), MEI was calculated as:

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(e-i)/(e+i)

e: value of the parameter at the edge

i: value of the parameter in the interior

MEI ranges between -1 and +1, with negative values indicating edge is lower than interior,

positive values indicating that edge is higher than interior, and a value of 0 indicating no edge

effect.

Prediction 2: Proportion Interior Species

Criteria for inclusion of papers in this analysis were: 1) species abundances were

quantified along forest edge to interior transects; 2) forest edges were relatively recently created

with matrix AGB being low; 3) sampling methods were comparable in sampling interval (spatial

and temporal); 4) the results among studies were not rendered incomparable due to edge

orientation, season, or other factors; and 5) statistical significances of species associations with

forest interiors were reported. Studies near urban or suburban development were excluded to

prevent complications due to species-human interactions. Most published studies we found

involved beetles, birds, and mammals, thus we restricted the analyses to these groups. A total of

20 studies remained after the final selection (Table 2).

Species response to edge was quantified as the proportion of species found in forests that

were significantly more abundant in forest interior than at the forest edge. This measure was

obtained for each study by dividing the total number of species associated statistically with

interior sites by the total number of species included in the statistical analysis of forest species.

MEI based on abundances of individual species was not appropriate because the species present

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differed between studies. Moreover, measures of total abundance among all species or species

richness are not considered highly relevant to conservation (Noss 2006a).

Statistical Analyses

Statistical inference from research findings across studies is increasingly drawn using

meta-analysis (Gurevitch & Hedges 1999, Osenberg et al. 1999). Meta-analysis requires

information on sample size and sampling variation for each study. Few of the studies we

selected for analysis reported sampling variation. Among the 31 studies included in the analyses,

only 5 reported sampling variance numerically. We concluded that a rigorous meta-analysis is

not possible with the current studies.

These studies do, however, represent the best current opportunity to evaluate the Biomass

Accumulation Hypothesis. If these studies provide support for the hypothesis, new well

designed research is justified. This approach is consistent with Gurevitch and Hedges (1999)

who argue that, “given a body of literature, some information regarding the overall findings is

much better than no information, and that therefore it is desirable to develop methods for data

synthesis of poorly reported data (e.g., where no estimate of sampling variance is published)” (pg

1146). They suggest unweighted standard parametric statistical tests such as ordinary least-

squares regression can sometimes be used and that the assumption of homogeneity of variances

of conventional statistics “does not always seriously compromise the Type I error rates of these

tests” (pg 1146). Sample variance is strongly influenced by sample size. If the sample size is

not biased relative to the magnitude of the predictor variable, regression may provide reliable

results. We examined the sample sizes for studies used in each of our analyses and found they

were either random relative to estimated AGB or were larger in lower AGB forests, which

increases the likelihood of significant relationships in low AGB forests and makes this analysis

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conservative for testing the hypothesis of greater differences in high AGB forests. We conclude

that use of regression to relate response variables to forest AGB is justified for these analyses.

MEI for each microclimate variable was regressed on AGB. Because sample sizes were

relatively small, we additionally used an analysis of covariance linear model for microclimate,

where microclimate type was the classification variable, MEI was the response variable, and

AGB and microclimate variables were the predictors. The model allowed the slope and intercept

to differ among microclimate variables. The absolute value of MEI was used in this analysis. If

AGB was significant in the model, we considered this as evidence in support of the AGB

Accumulation Hypothesis. Because sample sizes for species responses were higher, we analyzed

each taxonomic group separately. The proportion of interior species specialists was regressed

against AGB with linear models. Results were considered significant at the P<.05 level.

Results

MEI of light intensity increased significantly with AGB (n=13, F=8.65, p<0.01) (Fig 3).

MEI for forests with AGB of less than 200 t/ha ranged from 0.5 to 0.9 and was generally 0.9

to1.0 for forests higher in AGB. MEI for humidity was significantly negatively related to AGB

(n=6, F=27.49, p<0.006). This indicated a more humid environment in forest interiors than in

edges.

The remaining microclimate variables generally had the weakest MEI in low AGB

forests, however the univariate relationships were not statistically significant. VPD (n=5) was

similar to humidity showing moister conditions in forest interiors (Table 1). MEI for air

temperature data (n=7) generally increased with AGB, except for one observation in a low AGB

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Temperature Broadleaf forest in Hungary that showed a very high MEI. For soil moisture (n=3)

and soil temperature (n=4) there was no apparent relationship between MEI and AGB.

The overall model of microclimate MEI on AGB, controlling for microclimate variable

was significant (n=37, F=79.94, p<.0001). Within this model, AGB was possitively related to

the absolute value of MEI (F=251.02, p<0.0001).

Prediction 2: Interior Species

The percent of forest species specializing on forest interiors exhibited a positive

significant relationship with AGB for mammals (n=6, F=28.93, P<0.006) and birds (n=8,

F=6.57, P<0.043) (Fig. 4). The relationship for beetles was positive, but P-value slightly

exceeded the 0.05 cut-off for statistical significance (n=6, F=6.72, P<0.060). AGB explained

90%, 67%, and 65% of the variation in mammals, birds, and beetles respectively.

Among mammal studies, no forest interior species were found in three studies with forest

AGB below 140 t/ha. Percent interior species was 11-25 for mammals in forests of 312-412 t/ha.

Percent interior bird species increased in forests with AGB of 40 t/ha to about 200 t/ha. Above

this AGB level, the percent interior species remained near 30. The percentage for beetles was 0

in forests below75 t/ha, approximately 18 in forests 78-423 t/ha, and was 50 in one forest with a

AGB of 450 t/ha.

Discussion

The results were supportive of the two predictions of the Biomass Accumulation

Hypothesis. The microclimate analyses were limited by very small samples for four of the five

variables. Even so, there was a significant relationship between MEI and AGB for light and

humidity and the overall effect of AGB on MEI across microclimate variables was significant.

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These results suggest that the greater amount of vegetation in higher AGB forest ecosystems

more fully buffers the microclimate in forest interiors and creates sharp gradients from interior to

edge. The magnitude of MEI for microclimate in tropical rainforests is known to be sufficient to

have pronounced ecological effects such as tree and plant mortality (Laurance et al. 2002) and

substantially elevated fire frequencies (Cochrane & Laurance 2002; Cochrane 2003).

The proportion of species significantly more abundant in forest interior was also

positively related to AGB for two of the three taxonomic groups we examined. This was

especially the case with mammals for which three studies with AGB below 200 t/ha had no

forest interior specialists. These results are consistent with the prediction of the Biomass

Accumulation Hypothesis that interior species were poorly represented in these low AGB forests

because forest interiors and edges did not differ much in microclimate, vegetation structure, and

other direct edge effects. In support, several studies found significant correlations between

microclimate, vegetation structure, and species abundances along edge to interior gradients

(Didham et al. 1998; Magura et al. 2001).

The trends in percent interior species with AGB were generally similar for the three

taxonomic groups. The results suggest that beetle species may partition the edge-to interior

gradient in lower AGB forests than mammals and birds. The results also suggest that the

proportion of interior bird specialists plateaus at forest AGB levels above about 200 t/ha. It is

likely that differences in body size, home range size, and other aspects of scaling between

taxonomic groups influences the relationship between edge effects and AGB. More rigorous

study and larger sample sizes would be needed to test this hypothesis.

Scope and Limitations

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We acknowledge several limitations to our treatment of this issue. We choose to limit the

analyses to microclimate, beetles, birds, and mammals in forested systems. Forested ecosystems

were studied because the hypothesis most obviously applies to forests. Conceptually, the

hypothesis should apply to grassland and shrubland systems, but less so than for forests due to

their greater AGB. Moreover, studies of edge effects in nonforest vegetation types are fewer and

estimates of AGB are more difficult to obtain.

The analysis of species specialization was restricted to beetles, birds, and mammals

because too few studies were available for other taxonomic groups. Also, we suspect that edge

effects for tree species composition are more difficult to quantify because of the relatively slow

response of their distribution to changing conditions such as edge creation. Edge effects for

amphibians may also be difficult to assess because these species require various aquatic and

terrestrial habitats at different life stages (Becker et al. 2007).

An assumption of our approach for species distributions across edges was that no forest

interior species had become locally extinct after habitats had become fragmentation but before

the studies were conducted. Local extinction in forest fragments has been documented (Saunders

et al.1991: Robinson 1999). The patch sizes at which extinction occurs is known to vary with

trophic level, home range size, and population abundance, with top-level carnivores requiring the

largest habitat sizes for persistence (Woodroffe & Ginsberg 1998). The species sampled in the

studies we used were relatively small in body size and sufficiently abundant to be detected at

levels adequate for statistical analysis by the multispecies sampling methods used in these

studies. Thus, these species likely had relatively small home ranges. Moreover, all the studies

were done in relatively large forest stands. We conclude that it is unlikely that our results were

influenced by previous local extinctions of forest interior species.

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The study considered only the magnitude of edge influence and not depth of edge

influence. Depth of edge influence (DEI) is the distance that edge effects penetrate the habitat

(Chen et al. 1999; Harper et al. 2005). This was done to make this initial analysis more tractable.

We speculate that DEI has a unimodal relationship with AGB, reaching a maximum in forests

with intermediate AGB. In forests higher in AGB forests, the dense vegetation should inhibit

depth of penetration of direct and indirect edge effects. In forest with lower AGB, DEI should

diminish as conditions in the forest interior are similar to those in the matrix.

Within the response variables we chose for analysis, sample sizes were relatively small.

This was because of the moderate number of edge studies that have been published and because

we excluded many of the published papers because of incompatible study designs, lack of

rigorous statistical analysis, or because of confounding factors. To best draw inference from the

limited sample, we reported results for both the relationships between AGB and MEI of

individual microclimate variables and across all microclimate variables and controlling for

variable type.

Although the studies included in the analyses were selected based on the stated criteria,

they differed to some extent in site-level factors such as edge age and development, orientation,

and matrix type. We assume that these biases were random relative to the effects of AGB.

Laurance et al. (2007) describes how such local factors can cause considerable variation in edge

effects within a given region. The fact that significant relationships were found in our analyses

despite the variability introduced by differences in the site conditions of studies we drew upon,

suggests that the effect of AGB on edge effects was strong enough to be detected despite the

variability due to confounding factors.

The tropical studies only sampled a small fraction of local diversity in many cases and it is likely that the species chosen were not a random sub-sample. It is certainly not possible to conclude

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from a study with 4 species about the effects on a whole community of dozens or even hundreds of species (in the case ofbeetles). It is likely that investigators chose species that are likely to show an edge effect. It appears that his bias is greater in the tropics where communities were seriously under-sampled. The analysis should be repeated using only the studies that have a decent number of species sampled (a minimum of 8 or even 10).

Finally, the studies did not provide adequate information to allow formal meta-analysis (see

Methods), we feel the regression methods used were justified for this analysis.

Despite these limitations, we feel that this analysis is an appropriate first test of the

hypotheses. Given the results of this analysis, the cost of conducting a replicated experiment

across biomes may now be justified.

Review alternative hypotheses

If the edges are a recent man-made phenomenon in a region, one would not expect

edge-specialist species. Perhaps a better hypothesis is that ecosystems with little history of

edges and open habitats should have more species intolerant of the stresses associated with

open habitats. It is not a matter of steepness of gradients but consequence of lacking

adaptation because of no previous exposure. Could we test the disturbance history

alternative hyp? I think it is a good one, having been to suriname. The tropics jump out as

having less large patch creating disturbances of any system I have seen. Is disturbance

history something we could control for in the analysis? I suspect both are happening.

This is a good example where confounding high latitude clear cut edges with low latitude agricultural field edges is a problem. Boreal forest mammals like moose find excellent forage in clear cuts, while moose would avoid a cow pasture or soybean field.

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Similarly not all mammals are alike. The boreal forest has mostly ground dwelling mammals like moose and beaver, whereas tropical forests have many arboreal mammals, which obviously depend on trees. Thus there is a latitudinal difference in species guilds that affects edge effects independent of biomass. Tree dwelling marsupials of the tropical rainforest in Queensland Australia will not enter a cow pasture, regardless of the tree biomass in the forest. Such latitudinal differences in species guild composition are an important dimension of the edge effect question that needs to enter the discussion. Moreover, it implies that the apparent relationship between biomass and edge effect strength may have little to do with the microclimatic explanation given here. To meet the scholarship standards of CB, such possible alternative interpretations need to be at least mentioned.

The other low biomass forest in the bird dataset is boreal. As mentioned before, the clear cut edges in the boreal forests are very different than temperate forest that are fragmenting due to agriculture and development. Specifically, as the authors mention in the introduction, edge effects on birds are frequently due to brood parasitism or predation by small predators. The boreal forest neither has brood parasites like cow birds nor do small and medium sized predators concentrate along clear cut edges. In contrasst areas subject to higher human population densities have abnormally high concentrations of small predators (like domestic cats)which like to hunt along forest edges. These points are important because in the savanna case the biomass effect results from a complete lack of "forest interior" habitat in the landscape, and in the other types of forest the mechanism has more to do with predator and brood parasite behavior than with forest biomass or micro-climate.

Percent of landscape disturbed and seperateing area effects and patch size effects.Is another evolution time?

Review well known studies that were not included here

Comparison with Current Knowledge

Previous efforts to develop general theory on the situations where edge effects are most

pronounced have focused largely on site-level factors (Donovan et al. 1997; Laurance et al.

2002). These include: attributes of the habitat such as vertical structure and soil depth; edge

attributes such as age, orientation, and vegetation contrast; and matrix characteristics such

vegetation structure and composition and land use. The susceptibility of major ecosystem types

to edge effects has been little studied. A recent paper by Harper et al. (2005) suggested that the

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traits of ecosystems that are especially prone to edge effects include: climate (high mean air

temperature and solar radiation, low cloud cover, frequent extreme winds); disturbance

(infrequent stand replacing disturbances); community structure (many early-seral and invasive

species); landscape patterns (low inherent habitat patchiness). Additionally, Hansen & Urban

(1992) suggested that the life history attributes of species in the community vary among

ecosystems and influence direct biotic edge effects.

The Biomass Accumulation Hypothesis integrates several the ecosystem factors

described by Harper et al. (2005) and Hansen & Urban (1992). Forests with high AGB

accumulation tend to have warm temperatures, high solar radiation, periods of low clouds, high

primary productivity, infrequent stand replacing disturbance, and low natural patchiness in

forest/non-forest conditions (Brown & Lugo 1982). Moreover, species in high productivity and

high AGB systems tend to have small home ranges, low dispersal, and habitat specialization.

Hence, biomass accumulation appears to represent a syndrome of ecosystem characteristics that

cause magnitude of edge effects to be pronounced.

Implications

This study is the first to test the Biomass Accumulation Hypothesis. The results support

the hypothesis that biomass accumulation influences the magnitude of one component of

fragmentation, edge effects. Edge effects, in turn, are a component of patch size effects (Turner

et al. 2001; Noss et al. 2006b). Less area within a patch will display habitat interior conditions

when edge effects are pronounced. Consequently, we expect that extinction rates of interior

species in small patches will be higher in high biomass ecosystems.

Our results suggest that habitat fragmentation has the strongest negative effects on

ecosystems with high productivity and biomass accumulation, such as wet tropical and wet

21


temperate forests. Thus, efforts to minimize or mitigate fragmentation are most appropriately

applied to such high biomass ecosystems. It is important that managers in high biomass forests

not be confused by the ambiguous results of edge studies globally and take seriously the

management of habitat configuration within their forests. In ecosystems with low biomass

accumulation such as boreal, dry, or cold forests, conservation strategies to manage edge and

patch effects may be a lower priority. Further work should be directed towards identifying

thresholds in the relationship between forest biomass and edge effects that could be used to more

precisely guide the management of landscape configuration to local biophysical conditions.

Note the value of this, it is the general message that conservation should be tailored to

local conditions.

Page 21 re: cross biome experiments: I think it would be a waste of money. Biomass has a spurious correlation at best for the data presented. Most likely there is a thresholdbiomass from closed canopy forests to open savannas, but other wise biomass seems to have little explanatory power. Conservationists already have much better arguments for conserving large contiguous habitats. For example range requirements for large animals and minimum viable population size. Knowing the minimum patch sizesneeded in particular environmental contexts will do far more for conservation than exploring a global relationship between biomass and strength of edge effects. As someone that works for a conservation NGO, I can’t see any way how such a global approximation could be useful in practice. The exercise proposed is purely academic. I find that even academically it has little merit, given that it ignores causal mechanisms.

o

Overall, application of the Biomass Accumulation Hypothesis is an example of using

fundamental ecosystem properties as a basis for tailoring conservation to local ecosystem

conditions, a needed next step for conservation biology.

Acknowledgements

22


We thank Diane Debinski and Jiquan Chen for comments on earlier drafts of the

manuscript. Jim Robinson-Cox provided guidance on statistical analyses. We also thank the

authors of the numerous publications that made this study possible.

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Table 1. Studies of microclimate across forest edges included in the analyses. Biomes are defined as defined by

Olson et al. 2001 except that the only Temperate Coniferous samples included in the study were Temperature

Coniferous Rainforest and are thus termed that. AGB refers to aboveground standing vegetation. MEI is magnitude

of edge influence as defined in Methods.

Biome Study Region AGB

(t/ha)

Variable MEI

Boreal Burke & Nol

1998

Ontario, Canada 63 Light 0.60

Boreal Messier et al.

1998

Quebec, Canada 63 Light 0.78


1998



1998


Temperate

Coniferous (sub-

alpine)

Vaillancourt 1995 Wyoming, USA 100 Light 0.51

Temperate

Broadleaf

Luken &

Goessling 1995

Kentucky, USA 104 Light 0.88

Temperate

Broadleaf

Reifsnyder et al,

1971

Connecticut, USA 142 Light 0.83

Temperate

Broadleaf

Matlack 1993 Pennsylvania,

USA

180 Light 0.82

Subtropical Moist McDonald & New Zealand 343 Light 0.95

33


Broadleaf Norton 1992

Subtropical Moist

Broadleaf

Young &

Mitchell 1994

New Zealand 378 Light 0.86

Tropical Moist

Broadleaf

Kapos 1989 Brazil 400 Light 0.94

Tropical Moist

Broadleaf

Ghuman & Lal

1987

Nigeria 412 Light 0.94

Tropical Moist

Broadleaf

Chazdon &

Fetcher 1984

Costa Rica 450 Light 0.97

Temperate

Broadleaf

Magura et al.

2001

Hungary 78 Air temperature

(°C)

0.10

Temperate

Broadleaf

Gelhausen et al.

2000

Illinois, USA 100 Air temperature

(°C)

0.03

Temperate

Broadleaf


USA

180 Air temperature

(°C)

0.05

Tropical Moist

Broadleaf

Turton &

Frieburger 1997

Australia 270 Air temperature

(°C)

0.01

Subtropical Moist

Broadleaf

Young &

Mitchell 1994

New Zealand 378 Air temperature

(°C)

0.04

Tropical Moist

Broadleaf

Kapos 1989 Brazil 400 Air temperature

(°C)

0.08

Temperate

Coniferous

Chen et al. 1993 Washington, USA 687 Air temperature

(°C)

0.10

34


Rainforest

Boreal Burke & Nol

1998

Ontario 63 Humidity (%) 0.00

Temperate

Broadleaf

Magura et al.

2001

Hungary 78 Humidity (%) -0.07

Temperate

Broadleaf

Gelhausen et al.

2000

Illinois, USA 100 Humidity (%) -0.06

Temperate

Broadleaf


USA

180 Humidity (%) -0.04

Tropical Moist

Broadleaf

Ghuman & Lal

1987

Nigeria 412 Humidity (%) -0.26

Temperate

Coniferous

Rainforest

Chen et al. 1993 Washington, USA 687 Humidity (%) -0.29

Temperate

Broadleaf


USA

180 VPD (mb) 0.17

Tropical Moist

Broadleaf

Turton &

Frieburger 1997

Australia 270 VPD (mb) 0.50

Subtropical Moist

Broadleaf

Young &

Mitchell 1994

New Zealand 378 VPD (mb) 0.15

Tropical Moist

Broadleaf

Kapos 1989 Brazil 400 VPD (mb) 0.68

Boreal Burke & Nol Ontario, Canada 63 Soil Moisture -0.07

35


1998 (%)

Temperate

Broadleaf

Gelhausen et al.

2000

Illinois, USA 100 Soil Moisture

(%)

-0.22

Tropical Moist

Broadleaf

Kapos 1989 Brazil 400 Soil Moisture

(%)

-0.14

Boreal Burke & Nol

1998

Ontario 63 Soil

Temperature

(°C)

0.07

Temperate

Broadleaf

Magura et al.

2001

Hungary 78 Soil

Temperature

(°C)

0.05

Tropical Moist

Broadleaf

Turton &

Frieburger 1997

Australia 270 Soil

Temperature

(°C)

0.01

Temperate

Coniferous

Rainforest

Chen et al. 1993 Washington, USA 687 Soil

Temperature

(°C)

0.04

36


Table 2. Studies of animal species composition used in the analyses. AGB refers to

aboveground standing vegetation.

Biome Study Region AGB

(t/ha)

Taxonomic

Group

Total

Species

Percent

Interior

Specialists

Boreal Heliola et al.

2001

Finland 40 beetle 34 0

Boreal Koivula et al.

2004

Finland 75 beetle 12 0

Temperate

Coniferous

Strong et al.

2002

Vermont,

USA

130 beetle 11 18

Temperature

Broadleaf

Magura et al.

2001

Hungary 78 beetle 17 17

Tropical

Moist

Broadleaf

Didham et al.

1998

Brazil 423 beetle 32 19

Tropical

Moist

Broadleaf

Hill 1995 Australia 450 beetle 4 50

Boreal Hansson 1994 Sweden 39 Bird 16 0

Temperate

Broadleaf

Baker 2001 Australia 100 Bird 27 11

Tropical Berry 2001 Victoria, 157 Bird 13 0

37


Intermediate

Broadleaf

Australia

Temperate

Broadleaf

Noss 1991 Florida,

USA

181 Bird 27 15

Temperate

Broadleaf

Kroosma et al.

1982

Tennessee,

USA

244 Bird 26 35

Temperate

Broadleaf

King et al.

1997

New

Hampshire,

USA

266 Bird 7 29

Tropical

Moist

Broadleaf

Restrepo 1998 Columbia 552 bird 25 32

Temperate

Coniferous

Rainforest

Brand &

George 2001

California,

USA

600 bird 14 29

Boreal Hansson 1994 Sweden 39 mammal 9 0

Temperate

Coniferous

Sekgororoane

et al. 1995

New

Brunswick,

Canada

84 mammal 9 0

Temperate

Broadleaf

Heske 1995 Illinois,

USA

137 mammal 11 0

Tropical

Intermediate

Pardini 2004 Brazil 314 mammal 9 11

38


Broadleaf

Tropical

Moist

Broadleaf

Laurance 1994 Australia 400 mammal 4 25

Temperate

Broadleaf

Lehman et al

2006

Madagascar 412 mammal 6 17

Table 2: The edges in these studies include hard edges like agriculture in Queensland Australia to forest clear cuts in eastern Canada. The effect on edge effects and species is very different. A regenerating clear cut provides good habitat with natural forest vegetation whereas dairy pastures do not. This difference creates a bias because northernsites tend to involve clear cuts while southern sites tend to involve cattle pastures.

39


Figure Legends

Figure 1. Conceptual model of edge effects in ecosystems with high and biomass accumulation.

Depicted are adjacent early and late seral forest patches. Aboveground vegetation biomass (line

1) increases abruptly between early seral and late seral forest patches, causing a strong gradient

in microclimate (line 2) from forest edge to forest interior, and allowing species (line 3) to

specialize in either forest interior, edge, or early seral habitats resulting in narrow distributions in

abundance of guilds across the edge to interior gradient. In lower biomass ecosystems, the

gradient in biomass is less pronounced, consequently microclimate is less different between early

and late seral patches and species do not specialize highly on interior or edge habitats.

Figure 2. Average aboveground biomass of late seral forest in seven forested ecoregions

(defined in Olson et al. 2001). Data derived from published biomass values (Brown et al. 1993;

Penner et al 1997; Lefsky et al 2002; Myneni 2006; Brown & Lugo 1984; White et al 2000; Tate

et al 1997; Gerwing et al 2000; Means et al. 1999; Cairns et al. 2003). Temperate coniferous

studies were from extremely high biomass systems (California redwoods, Pacific Northwest

rainforest) and is not likely representative of the broader ecoregion.

Figure 3. Magnitude of edge influence (MEI) for microclimate variables across AGB levels.

MEI ranges between -1 and +1, with negative values indicating edge is lower than interior,

positive values indicating that edge is higher than interior, and a value of 0 indicating no edge

effect. VPD is vapor pressure deficit. Data are from the studies in Table 1.

40


Figure 4. Percentage forest interior species for birds, beetles, and mammals across aboveground

biomass levels. Data are from studies in Table 2.

41


42

Early Seral Patch Later Seral Patch

2. Microclimate (e.g., wind speed)

1. Vegetation biomass

3. Species Guild Abundances

High Biomass Ecosystem




Low Biomass Ecosystem

Mag

nitu

de

Hig

hLo

w

Mag

nitu

de

Hig

hLo

w

Edge

Early Seral Patch Later Seral PatchEdge

Early Seral Patch Later Seral Patch




High Biomass Ecosystem




Low Biomass Ecosystem

Mag

nitu

de

Hig

hLo

w

Mag

nitu

de

Hig

hLo

w

Edge

Early Seral Patch Later Seral PatchEdge


43


0 100 200 300 400 500

0.0

0.4

0.8

Light Intensity

AGB (t/ha)

ME

I y= 0.0006 * AGB + 0.70R2=0.44

0 100 200 300 400 500 600 700

-0.6

-0.3

0.0

Humidity

AGB (t/ha)

ME

I

y= 0.0005 * AGB + 0.001R2= 0.87

44


Birdsy = 0.07x - 0.6979

R2 = 0.6782

Beetlesy = 0.0771x + 2.3499

R2 = 0.6457

Mammalsy = 0.0664x - 5.7805

R2 = 0.8958

-10

0

10

20

30

40

50

60

0 100 200 300 400 500 600 700Biomass (t/ha)

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need general theory to predict consequences of … · web viewbiomass, conservation principles,...

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