Detecting general plant functional type responses in fragmented landscapes using spatially-explicit simulations

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Detecting general plant functional type responses in fragmented landscapes using spatially-explicit simulations a,∗ ¨ Katrin Korner , Florian Jeltsch b a b

Leibniz-Centre for Agricultural Landscape Research ZALF Muencheberg, Germany Potsdam University, Plant Ecology and Conservation Biology, Germany

a r t i c l e

i n f o

a b s t r a c t

Article history:

Habitat loss and increasing landscape fragmentation are known to be key forces driving

Received 16 February 2007

the ongoing loss of plant species diversity. While the combined effects of increasing iso-

Received in revised form

lation and decreasing population size have been studied intensively; it is less understood

23 July 2007

how plant population performance in heterogeneous landscapes is affected by changes in

Accepted 7 August 2007

fragmentation alone.

Published on line 29 October 2007

To test whether general rules exist to describe the complex response of plant species to fragmentation, a model is developed to simulate plant populations in a spatially realistic

Keywords:

context. The performances of six ruderal plant functional types (PFT), as defined by Grime’s

CSR concept

CSR scheme, are compared among landscapes varying in their level of fragmentation.

Disturbances

In general, increasing fragmentation has a negative but varying effect on the measured

Dynamic landscape

set of regional fitness parameters of the PFTs. For several output variables, it is not only the

Dispersal

main functional types that differ in their relative competitive, ruderal and stress-tolerance

Fractals

features, but also their subtypes.

Landscape change Plant functional groups

Strictly ruderal types react most strongly to fragmentation intensity showing high regional extinction vulnerability. In contrast, competitive types are less affected.

Plant strategic types

Some specific traits like having dormant seeds have a positive impact on some regional

Regional ensembles

output variables measured. However, this positive impact is not valid for all output variables

Simulation

simultaneously, suggesting a trait syndrome centred view of PFT’s behaviour. Our simulation experiments show that a thorough categorisation based on plant functional types provides a suitable approach for improving our understanding of complex plant species responses in dynamic heterogeneous landscapes. © 2007 Elsevier B.V. All rights reserved.

1.

Introduction

Increasing human pressure on natural and semi-natural landscapes over the recent decades has been identified as an important cause of the decline of many previously common plant species (Oostermeijer et al., 2003; Kotze and O’Hara,

∗

2003; Jongejans and de Kroon, 2005; Soons et al., 2005). Ecologists are particularly concerned about the continued loss and fragmentation of species habitat. Habitat loss leads to smaller (sub) populations which are prone to higher extinction risks due to environmental or demographic stochasticity, reduced genetic variation, inbreeding depression and reduced poten-

¨ Potsdam, Maulbeerallee 2, D-14469 Potsdam, Germany. Tel.: +49 331 977 1913; fax: +49 331 977 1930. Corresponding author at: Universitat ¨ E-mail address: [email protected] (K. Korner). 0304-3800/$ – see front matter © 2007 Elsevier B.V. All rights reserved. doi:10.1016/j.ecolmodel.2007.08.002

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tial for evolutionary adjustments (Oostermeijer et al., 2003; Matthies et al., 2004; Bacles et al., 2005; Byers et al., 2005; Bruna and Oli, 2005). According to metapopulation theory, the local and regional survival of species is further reduced by increasing landscape fragmentation. This can be explained by the reduction of effective seed and pollen dispersal between subpopulations (Hanski, 1999; Verheyen et al., 2004; Purves and Dushoff, 2005). In addition, fragmentation is assumed to strongly reduce the ability of species to adapt to current and future climate change as well as their capacity to migrate to sites of suitable environmental quality (Higgins et al., 2003; Pearson and Dawson, 2005). Despite increasing awareness of the conservation problems caused by landscape fragmentation, the capability for predicting the response of a given plant species to these landscape changes is still limited (Schwartz, 1992; Malcolm et al., 2002; Matthies et al., 2004; Jongejans and de Kroon, 2005; Bruna and Oli, 2005; Vellend et al., 2006). The reason is twofold: First, a generic understanding of the mechanisms by which plant species respond to landscape patterns and dynamics has yet to be fully developed (Freckleton and Watkinson, 2002; Ehrlen and Eriksson, 2003; Cousins et al., 2003; Pearson and Dawson, 2003; Williams et al., 2005; Zartman and Nascimento, 2006). The key difficulty here is overcoming the practical challenges involved in undertaking and replicating a study on a sufficiently complex spatio-temporal scale. Second, the alternative, to make generalisations upon empirical research results of single species is problematic (Bruna and Oli, 2005; ´ ¨ Herault and Honnay, 2005; Munzbergov a´ et al., 2005). Several empirical studies show an effect of habitat fragmentation on plant populations (Lienert et al., 2002b,c; Byers et al., 2005; Piessens et al., 2005); in most species the response to fragmentation is negative, however, what these studies also show is that different species may respond differently to the same processes (Cunningham, 2000; Lindborg et al., 2005; Vellend et al., 2006; Helm et al., 2006). This can be caused by differences in the life cycle strategy, adaptation via dispersal in time and space or other adaptation mechanisms to local habitat characteristics (Matthies et al., 2004; Jongejans and de Kroon, 2005; ´ Herault and Honnay, 2005). To improve our capability for generalised predictions of plant species responses to changes in landscape fragmentation, a combined generalising approach both at the landscape level (i.e. testing a variety of landscapes) and at the species level (e.g. testing plant functional types instead of single species) is required. This allows us to systematically evaluate different degrees of fragmentation as well as identifying suitable criteria for grouping species in response to fragmentation. Here, we link a well-documented generalised landscape generator (With, 1997) with a spatial simulation model of plant functional types (PFTs) derived from Grime’s well-established CSR scheme (i.e. ‘competitor, stress-tolerator, ruderal’, Grime, 1974, 1979; see also Hodgson et al., 1999). PFTs are here used in the sense of ‘functional response groups’ (Lavorel and Garnier, 2002), i.e. species form user-defined groups with similar biological traits leading to similar responses to environmental resources and disturbances (Cousins et al., 2003). Despite extensive literature on the definition, identification and application of PFTs and plant functional groups (PFG; for helpful

reviews see Gitay and Noble, 1997; Duckworth et al., 2000), only a few studies have directly linked this approach to questions ´ relating to fragmentation (but see Cousins et al., 2003; Herault and Honnay, 2005). By applying a spatially-explicit simulation approach, we systematically evaluated the effects of landscape fragmentation on the dynamics of selected PFT populations. In contrast to previous modelling studies that combine investigations of habitat loss and fragmentation effects (e.g. Tilman et al., 1994, 1997; Klausmeier, 1998; Malanson, 2007) we here focus on fragmentation effects alone to better understand the relative effect of this type of landscape change. For this, we generated three artificial landscape types differing in their severity of fragmentation (With and King, 1999, 2004). For each landscape we simulated the dynamics of six different PFTs that differed in their relative location in Grime’s CSR scheme. To be able to better compare their relative response we focus on PFTs that all have certain ruderal features, i.e. local populations typically occur during early successional stages. With our simulation model, we conducted experiments to answer the following questions: (i) is the inclusion of PFTs based on Grime’s CSR scheme a suitable approach for gaining a generalised understanding of the effects of landscape fragmentation on plant populations? (ii) How do different PFTs respond to an increasing intensity of landscape fragmentation? (iii) Do different subtypes of the same PFT react differently to the same landscape structures? If so, does this difference depend on the intensity of landscape fragmentation? (iv) Can we predict the response of PFTs or even subtypes of a PFT to landscape changes based on the traits of these groups?

2.

Material and methods

We developed a stochastic, spatially-explicit simulation model to evaluate and compare the performances of different plant functional types in a regional context under different levels of spatial autocorrelation of suitable habitats, i.e. fragmentation extent of the landscape. The local population and habitat dynamics of all types follow the same general rules. However, they are modified by a range of functional type-specific parameters, which can be found in the model description (see below).

2.1.

Model description

The model is a stage-based, spatially-explicit cellular automaton (Jeltsch and Moloney, 2002). The spatial extension of the grid represents the regional distribution of one plant functional type and is divided into 129 cells × 129 cells, with grid cell sizes in the order of magnitude of 100s of meters. Cells are either unsuitable for plant growth or represent suitable habitats. Suitable cells may differ in habitat quality, but each cell is potentially able to accommodate one local population. We used periodic boundary conditions to avoid artefacts resulting from limited grid extent. Fig. 1 shows the processes of the model. After model initialisation, the simulation proceeds in annual time steps. At the beginning of each time step, the weather conditions and the

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Fig. 1 – Flow chart of the model used to simulate the spatial dynamics of plant functional types.

dynamics of each patch cell are determined. Afterwards, the model simulates the population dynamics of each suitable cell and the seed dispersal between cells (Fig. 2). In the following these processes are described in detail.

2.1.1.

Landscape generation

We used the midpoint displacement algorithm by Saupe (1988) to generate fractal landscape patterns. These patterns represent a gradient of fragmentation intensity with high, medium and low fragmentation. The algorithm is well-documented and tested (e.g. Hargrove et al., 2002) and produces realistic ‘neutral’ landscapes (With, 1997) with a three-dimensional fractal surface (see also Malanson et al., 2007). The landscapes can be characterised by two parameters, namely the spatial autocorrelation (Hurst-factor H between 0 and 1) and the variance in displacement of points ( 2 ). An increment of H leads to

an increment of the level of aggregation and thus determines the fractal dimension D, with D ≈ 3 − H for three-dimensional landscapes. While the first two dimensions represent space, the third dimension describes the local maximum population capacity of the habitats (Cmax ). In total, we assume only 15% of all grid cells to be suitable. Thus, we set Cmax of cells not belonging to the “top” 15% cells with the highest maximum population capacity to zero. Afterwards, we rescaled Cmax of the remaining 15% to a range between 1 and 1500 (Fig. 3a). In our simulation experiments, we systematically explored landscape patterns for three levels of fragmentation (D = 2.1, 2.5, 2.9;  0 = 30). We replicated each fragmentation scenario 100 times (see Fig. 3b–d for examples), which led to an overall number of 300 landscape scenarios. These scenarios allowed for systematically determining the effect of landscape fragmentation on the dynamics of PFT populations.

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Fig. 2 – Regional grid with occupied (grey), unoccupied (white) landscape cells and cells with seed rain from focal population (light grey, LDD: long distance dispersal). Box: local population dynamics, i.e. possible transitions between different stages. Germination and establishment occuring within 1 year, other arrows marking annual transitions.

2.1.2.

Plant functional types

We focussed on generally ruderal species or PFTs that are displaced during succession and which long-term abundance is promoted by disturbances. This is reflected by the model assumption that habitat capacity is highest following a disturbance event and declines thereafter. Within this broad classification of ruderal species we distinguished three main types that differed in their stress-tolerance (‘S’) and competitiveness (‘C’) (Table 1). Thus, we distinguished between ruderals with additional competitive (RC), or stress-tolerance (RS) features, as well as ‘pure’ ruderals (RR) (Table 1). The more competitive type (RC) is implemented as a typical perennial with a relatively short life cycle. The higher competitive ability reduces the negative effect of succession. But, since stress-tolerance for competitive species is supposed to be low (Table 1), these species are assumed to experience more climatically ‘bad’ years. Typical examples of species for this type are Cirsium arvense, Artemisia vulgaris or Juncus atratus. The more stress adapted type (RS) is implemented as a longer living perennial with a more complex life cycle. Annual seed production is very low due to low annual allocation to seeds. Typical examples here are Stachys recta, Verbascum spp., or Gentiana pneumonanthe. The ‘pure’ ruderals (RR) are implemented as annual types with high annual seed production

but low competitive ability, i.e. they suffer from an increased effect of succession. Sample species here are Conyca canadensis, Senecio vernalis, Ranunculus arvensis, or Chenopodium album. For each of these three main types, we further distinguished between two subtypes differing between, (a) clonal (with low seed production, e.g. C. arvense or J. atratus) versus nonclonal reproduction (e.g. A. vulgaris or Senecio inaequidens) in the RC type, (b) stress-tolerating (e.g. G. pneumonanthe) versus stress-avoiding strategy (e.g. S. recta or Verbascum spp.) in the RS-type and (c) dispersal in space (e.g. C. canadensis or S. vernalis) versus dispersal in time (e.g. R. arvensis, C. album, or Cyperus fuscus) in the RR type (Table 2). The stress-tolerating RS-subtype does not suffer from ‘bad’ climatic conditions (for weather implementation see below), whereas the stress avoiding RS-subtype is characterised by dormant seeds allowing for longer local persistence under unfavourable conditions. The space dispersing RR-subtype has a higher proportion of long distance dispersed (LDD) seeds, whereas the ‘time disperser’ subtype with a ‘standard’ proportion of LDD-seeds produces more dormant seeds. Each of the six subtypes was represented by four variants, i.e. different parameter combinations, to allow for sufficient variation and the identification of general pattern (see Section 2.2 and Table 2).

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Table 1 – Attributes of simulated plant functional types (PFT) Strategy

Ruderal + competitive RC

Ruderal + stress-adapted RS

Ruderal RR

Relative growth rate (RGRmaxa )

Fast

Slow

Fast

Life formsa

Forbs, grasses RC1: perenn, clonal reproduction; RC2: perenn, seed reproduction Low

Lichens, perennial herbs

Annuals

Low

High

High

Medium

Low

Neutral

Lowc to neutral

Neutral

Higha

RR1: high, no; RR2: normal RR1: no; RR2: high

Low

Very good RS1: avoiding bad conditions RS2: tolerating bad conditions

Annual allocation to seedsa b

Competitive ability Dispersal In space In time

Stress-tolerancea (weather)

Neutral

All PFTs have dominant ruderal features, i.e. they benefit from local disturbances and suffer from succession, but differ in their relative portion of competitive or stress-tolerance features. For each PFT we further distinguish between two realisations, i.e. subtypes 1 and 2 (see text for further details). a b c

After Grime (1977). After Wilson and Lee (2000). In case of stress-avoidance-strategy.

2.1.3.

Model initialisation

After generating the landscape (see above) the current capacity C of each suitable habitat is set to a random value between 0 and Cmax . C indicates the successional stage after the last disturbance event. Since we here focus on ruderal species,

local capacity declines with ongoing succession (see below). Initial populations occupy 20% of the suitable habitats. The size of initial populations, which consist of generative adults only, is set to half of the current capacity of each habitat cell.

Table 2 – Parameter values used to represent the six simulated plant functional types Functional type

Ruderal + competitors RC RC1

Parameter Competetive ability Portion LDD-seeds Dependence to bad environmental conditionsa State transitions Germination rate establishment Seedl → Juv Seedl → VegAd Seedl → GenAd

RC2

Ruderal + stress-tolerators RS RS1

RS2

Ruderals RR RR1 −0.6 0.6 0

RR2 −0.6 0.1 0

0.6 0.1 −

0.6 0.1 −

0 0.1 0

0 0.1 +

0.5 0 0.2–0.25 0

0.5 0 0.3–0.35 0

0.15–0.3 0.5 0 0

0.4–0.5 0.5 0 0

0.15–0.2 0 0 0.15–0.45

0.1–0.2 0 0 0.2–0.4

Growth Juv → VegAd VegAd → GenAd

1 0.2–0.75

0 0.7–0.9

0.5 0.2–0.3

0.5 0.1–0.12

0 0

0 0

Survival Juveniles Vegetative adults Generative adults Fertility/clonal growth (VegAd) Fertility/seed production Seed dormancy

0.5 0.6 0.7 2.5 4–6 0

0.9 0.9 0.9–0.99 0 8 0.8

0.9 0.9 0.85–0.99 0 8 0

0 0.6 0.7 0 15–23 0

0 0 0 0 50–120 0

0 0 0 0 20–75 0.8

For each type we chose four different parameter combinations that led to persistence (population growth rate approximately 1) in a standard landscape. Realised parameter values are within the ranges given below. LDD: long distance dispersed seeds, i.e. seeds dispersed to other grid cells. a

“0” means 20% good years and 20% bad years for each of the three weather dependent processes (i.e. germination, seed production and mortality) “+” means reducing risk of bad years to 0%; “−” means increasing risk of bad years to 40% (see Table 1).

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2.1.4.

Habitat dynamics

The capacity of suitable habitats is annually modified as a result of succession and stochastic disturbance events. Since our general investigations focus on different types of ruderal species, succession is here defined as a gradual reduction of the current carrying capacity (Ct ) of the cell (i.e. current capacity declines with increasing matrix vegetation and the corresponding reduction in suitable (micro)sites)



C(t+1) = Ct 1 −

rp 1 + compAbil



1−

Ct

 (1)

Cmax

where rp is a constant patch quality decrease rate and compAbil the species-specific competitive ability. Disturbance events occur in each habitat with a yearly probability. There is no spatial or temporal autocorrelation in disturbance events. Disturbances set the number of plants in the cell to zero and the current capacity of a habitat to its maximum value (Cmax ). Seed bank size is not affected by disturbance.

2.1.5.

Weather conditions

In each time step the weather conditions are chosen randomly. Weather conditions are characterised by the combination of the year’s suitability (good, average or bad) for each of the relevant processes, namely germination, seed production and mortality. ‘Good’ (‘bad’) years are defined by a 50% increase (decrease) in germination rate or seed production and by a 50% decrease (increase) in mortality compared to ‘average’ years (see Section 2.1.6). The probabilities of good (20%) and bad (20%) years are equal for all PFTs except for the competitive RC types and the stress-tolerant type RS2. The RC-subtypes experience a higher proportion of bad years (40%) due to higher sensitivity to bad environmental conditions, and the stresstolerating type RS2 has a zero probability for bad years due to lower sensitivity to bad conditions. Weather conditions apply to the entire grid. Thus, there is no spatial heterogeneity in weather conditions in this version of the model.

2.1.6.

Local population dynamics

The age structure of a PFT population comprises up to five life cycle stages: seed, seedling, juvenile, vegetative adult and generative adult (see Fig. 2). Seeds germinate with a germination rate dependent on weather conditions (see above). Within the time step of 1 year, seedlings can establish and reach a juvenile, a vegetative, or even a generative life stage. The establishment of seedlings follows a density dependent function with rate Ek (Table 2: transitions seedling to juvenile, vegetative or generative) to stage k:

 Pestabk,t = Ek Fig. 3 – Tested levels of landscape fragmentation calculated with a midpoint displacement algorithm (Saupe, 1988). The fractal dimension D gives the level of habitat fragmentation; (a) 3-dimensional presentation: z-values represent habitat quality, z-values below a certain threshold are considered as unsuitable habitats. The x- and y-data represent the spatial location of a habitat patch; (b–d) D = 2.9, 2.5, 2.1; respectively, H = 0.1, 0.5, 0.9,  = 30.

 1−

Nk,t

Ct

 ;

0 ≤ Pestabk,t ≤ 1

(2)



Nk,t is the where Ct is the current carrying capacity and current local sum of individuals in all life stages. The transition of older (juvenile or vegetative) individuals is described by constant probability rates (see Table 2). Each year, generative adults produce seeds (seed production) dependent on weather conditions. In clonal types, adults are able to produce ramets that enter the juvenile state. After

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seed production, an individual may die. Here, the survival probability Psurv depends on the individual’s life cycle stage k (“survival”, Table 2) and varies with weather condition factor Q and population size N:

⎛



⎜ ⎝

k

Psurvk,t = Qt Sk ⎜2 −

Nk,t

Ct

⎞ ⎟ ⎟ ; 0 ≤ Psurvk,t ≤ 1 ⎠

(3)

where Sk is the given survival-parameter for stage k and C is the current habitat capacity. For the survival of adult individuals only the adult population size is considered in the calculation of Eq. (3).

2.1.7.

Model outputs were analysed in R (R Development Core Team, 2006) using linear-mixed effects models (Pinheiro et al., 2006) with a random effect of the parameter set. In a first analysis, we tested for an interaction in the effects of main classification (CR/SR/RR) and habitat fragmentation on species responses. In a second analysis, we tested whether the corresponding subtypes (a) differed in response at fractal dimension D = 2.5 and (b) whether they responded differently to fragmentation. Thus, we compared the slopes of response to fragmentation degree, i.e. strength of effect, rather than mean or intercept of regression lines. The latter were only compared if the slopes of corresponding subtypes were not significantly different. Additionally we tested for the slope of each subtype to be different from zero (d.f. = 1).

Dispersal

A fixed proportion (1-portion LDD-seeds, see Table 2) of produced seeds is added to the local seed bank. The other seeds are distributed to other cells as long distance dispersal (LDD). All LDD-seeds of a population are dispersed randomly with a probability p according to a negative exponential dispersal kernel (p = 7 × exp(−2 × distance); where distance is the Euclidean distance between the centres of the population and the target cell) that is cut off at a maximum dispersal distance of 11 cells (source cell excluded). Seeds are repeatedly dispersed from each population until the amount of LDD-seeds is reached.

2.2.

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Simulation design

To allow for systematic comparison between PFTs and their response to modified landscape fragmentation we identified standard parameter sets (Table 2) for all PFTs. We derived these sets by tuning the model so that after an initial phase of less than 200 years all functional types reached a dynamic population equilibrium (Fig. 4), i.e. dynamics showed an overall growth rate of  ∼ 1, and facilitated 1000 years of species survival in at least half of the simulation runs. Deriving parameter values by tuning a model for initial species persistence or coexistence is a relatively common approach in models that do not parameterize the processes from actual species (e.g. Jeltsch et al., 1996; Malanson et al., 2007). The dynamics of PFTs represented by the 24 parameter sets were simulated for 300 landscape scenarios that differ in fractal dimension (D = 2.1, 2.5, and 2.9). We conducted 100 replicate runs for each parameter set-landscape combination. In each run we simulated PFT dynamics for 1000 years. To reduce the sensitivity of the output variables to initial conditions, we neglected the first 100 years of each simulation run in the calculations. The evaluation of model outputs included the following variables: overall abundance (OverallAbun), mean population size (PopSize), turnover rate (TurnoverRate; colonisation rate divided by extinction rate) and minimum habitat occupancy in runtime (MinHabOccup). For each parameter set-landscape combination we calculated mean values for each output variable and inter-annual coefficient of variation (CV) for the variables overall abundance (AVOverallAbun) and mean population size (AVPopSize) to account for climate conditions. This results in six output variables which were log transformed (except for TurnoverRate) to normalise model residuals.

3.

Results

Proportion of occupied habitats and the population size in occupied habitats (mean of 100 runs, Fig. 4) show, that in moderately fragmented landscapes all functional types reach a steady state that varies within each subtype with regard to the specific parameter realisation. However, parameter settings of the four subtype variants lead to the same magnitude of output values indicating subtype specific behaviour of the system. Substantial differences occur between the three main plant functional types (PFTs), namely RC, RS, and RR, but also between some of the subtypes. These differences appear in the magnitude of values as well as in the shape of the time series, e.g. for mean population size in RC1 versus RC2 or RS1 versus RS2. Ruderal types (RR1, RR2) generally have a very low population size and show little inter-annual fluctuations at the regional level, whereas the stress-tolerating (RS2) and the clonal competitive (RC1) types show high population sizes and stronger fluctuations. The response of the remaining types (RC2 and RS1) is located between these extremes. Note that low values of the mean population size can be caused by a relatively large number of patches with a persistent soil seed bank (as in RR and RS1 types) in habitats where the current successional stage does not allow seedling establishment. These non-extinct populations still contribute to the denominator in the calculation of mean population size thus causing low numbers. The same effect leads to high values of the portion of occupied habitats in RR types where the actual number of occupied patches (including those with an existing seed bank only) can exceed the number of currently suitable habitat patches.

3.1.

General effect of habitat fragmentation

As expected, increasing habitat fragmentation generally causes a negative response of the measured population characteristics for all plant types (Fig. 5). The overall abundance and mean population size decreases, habitat occupancy declines and inter-annual variation of the overall abundance and mean population size increases. To further access the effects of fragmentation on output variables we calculated a linear regression of the three fragmentation response values (Fig. 5). The slope analysis confirms the expected negative effect of increasing habitat fragmentation on the simulated types: slopes of all simulated

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Fig. 4 – Time series of the mean habitat occupancy (mean portion of occupied habitats) and the mean population size (value of every year represents arithmetic mean of 100 runs). Parallel curves are means of four parameter sets per type; D = 2.5, medium fragmentation level.

plant types are significantly different from zero for the output variables ‘total sum of individuals’ (OverallAbun; L-Ratio = 14.3 to 21.8, p < 0.001), ‘mean population size’ (PopSize; L-Ratio = 14.5 to 23.0, p < 0.001), ‘turnover rate’ (L-Ratio = 10.5 to 16.0, p < 0.01) and the ‘minimum portion of occupied habitats’ (MinHabOccup; L-Ratio = 11.6 to 16.1, p < 0.001). For the ‘inter-annual variation in the overall abundance’ (AVOverallAbun) three of six subtypes show no significant response to habitat fragmentation (RC2: L-Ratio = 3.4, p = 0.065; RS2: L-Ratio = 0.0, p = 0.941; RR2: LRatio = 3.2, p = 0.074), the other subtypes show an increase with fragmentation. In five functional subtypes the inter-annual variation in the mean population size (AVPopSize) increases significantly with increasing fragmentation. Only the stresstolerating RS2 subtype shows no significant response to varying fragmentation (RS2: L-Ratio = 3.7, p = 0.053).

3.2.

Regional survival of main functional types

The three main functional types (RR, RS, RC) differ significantly in their responses to increasing fragmentation for all output variables except overall abundance (OverallAbun) (see Table 3). The two ‘pure’ ruderal types (RR) are most strongly affected by increasing fragmentation, as indicated by the steepest slopes among all functional types in Fig. 5. The responses of both ruderal types are similar despite differing dispersal strategies in relation to space and time. Ruderal types appear to be most at risk of regional extinction given the sensitivity of the minimum habitat occupancy (MinHabOccup) to fragmentation. Inter-annual variation of overall abundance (AVOverallAbun) was also strongly affected by increasing fragmentation. This indicates a high sensitivity of the ‘pure’

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Fig. 5 – Response of the six output variables to different levels of habitat fragmentation. Symbols denote mean values for four parameter combinations of each subtype; lines indicate linear fits for each subtype (RC1: ♦, RC2: ×, RS1: , RS2: , RR1: +, RR2: ). p-Values denote significance of interaction between subtypes, i.e. subtypes responding different to varying fragmentation degree.

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Table 3 – Test statistics for the difference between the main functional types (RC, RS, and RR) in their response to increasing fragmentation: analysis of covariance for interaction of main types with fragmentation degree (d.f. = 2) Variable (transformed)

L-Ratio

Minimum habitat occupancy (log) Overall abundance (log) Annual variation in overall abundance (log) Mean population size (log) Annual variation in population size (log) Turnover rate

9.69** 1.47 18.29*** 24.61*** 16.77*** 14.73**

∗∗

Table 4 – Test statistics (ANOVA with random factors, see methods) for the differences between each of the three pairs of contrasting subtypes in a medium fragmented landscape structure (D = 2.5; d.f. = 1) Variable

Type

L-Ratio

Minimum habitat occupancy (log)

RC RS RR

4.93* 7.59** 3.25

Overall abundance (log)

RC RS RR

18.70*** 9.12** 1.57

Annual variation in overall abundance (log)

RC RS RR

13.28*** 23.62*** 0.73

Mean population size (log)

RC RS RR

27.07*** 14.65*** 0.70

Annual variation in population size (log)

RC RS RR

17.75*** 24.07*** 1.30

Turnover rate

RC RS RR

0.90 6.14* 7.53**

p < 0.01. ∗∗∗ p < 0.001.

ruderal type to ‘bad’ climatic conditions in fragmented habitats. Since climatic conditions are assumed to be homogenous in the simulated landscape, all local populations are affected simultaneously. Despite this the mean population size (PopSize) of ‘pure’ ruderal types are found to be least affected by fragmentation. In contrast, the stress-tolerant and competitive types (RSand RC-types) show greater sensitivity in mean population size to fragmentation (Fig. 5) but less sensitivity in minimum habitat occupancy. The perennial RC and RS types are also similar in that they have comparable slopes for the inter-annual variation of the overall abundance (AVOverallAbun) and the mean population size (PopSize).

3.3.

Differences between subtypes

In addition to our analysis on main types we also assessed responses of contrasting subtypes within each PFT. Specifically, we evaluated: (a) whether there are inherent differences between two contrasting subtypes in a medium fragmented landscape structure (D = 2.5), and (b) whether subtypes of a main functional type respond differently to increasing landscape fragmentation. Table 4 shows output variables for different subtypes across a moderately fragmented habitat. In most cases, there are significant differences between alternative subtypes within each main type. Exceptions can be found for the variable turnover rate, where the RC subtypes do not differ significantly, and the RR subtypes, which do not differ for any output variable except for the turnover rate (see Table 4). Across the full range of fragmentation levels significant differences were evident among subtypes for all output variables (i.e. slope or intercept) barring only one exception: turnover rate between RC types. Significant differences in the strength of a response to increasing fragmentation (slopes in Fig. 5 and Table 5) were found between at least one pair of subtypes for each output variable. Slopes between subtypes of the RC type differed for three out of six output variables including interannual variation of total sum of individuals (AVOverallAbun) and inter-annual variation of population size (AVPopSize). Both variables indicate sensitivity to environmental stochasticity with the subtype reproducing only via seeds (RC2) responding more sensitive to fragmentation than the corresponding type reproducing clonally (RC1). Also for the ‘pure ruderal’

∗

p < 0.05. p < 0.01. ∗∗∗ p < 0.001. ∗∗

main type RR, subtype slopes differed significantly in three out of six output variables: overall abundance (OverallAbun), mean population size (PopSize) and, especially, turnover rate. The annual ruderal subtype dispersing in space (RR1) exhibits the strongest response for turnover rate among all subtypes. Despite of the moderate general response of the two stress adapted subtypes (RS1 and RS2) to increasing habitat fragmentation, stress adapted subtypes show the greatest variation in their response to fragmentation. For two output variables, the minimum habitat occupancy (MinHabOcc) and the mean population size (PopSize), the stress avoiding type (RS1) is significantly more negatively affected than the stress-tolerating type (RS2). The interaction for mean population size is the most highly significant among all within-PFT comparisons.

4.

Discussion

Habitat loss and landscape fragmentation are presumed to have profound influences on plant species’ regional abundance and performance (Schwartz, 1993; Debinski and Holt, 2000; Oostermeijer et al., 2003). However, only a few studies have systematically tried to distinguish between these two separate types of landscape change. Whereas habitat loss clearly leads to population decline (Fahrig, 2003), most studies on fragmentation where habitat loss is controlled for tend to show a variety of contrasting effects on populations (Cunningham, 2000; Fahrig, 2003). Clearly, such ‘pure’ fragmentation changes are only an idealisation of the dynamics that occur in natural landscapes, for example the construction of road or railway lines may lead to relatively little habitat loss

e c o l o g i c a l m o d e l l i n g 2 1 0 ( 2 0 0 8 ) 287–300

Table 5 – Test statistics for the difference between subtypes in their response to increasing fragmentation (compare Fig. 5): hierarchical analysis of covariance for interaction of (corresponding) subtypes with fragmentation degree (d.f. = 1), and parameter combination as random factor Variable

Type

Minimum habitat occupancy (log)

RC

Slope Intercept

0.12 5.89*

RS

Slope Intercept

7.45** –

RR

Slope Intercept

3.76 5.87*

RC

Slope Intercept

1.53 18.06***

RS

Slope Intercept

4.48* –

RR

Slope Intercept

4.52* –

RC

Slope Intercept

10.83** –

RS

Slope Intercept

4.14* –

RR

Slope Intercept

0.01 3.87*

RC

Slope Intercept

4.07* –

RS

Slope Intercept

18.37*** –

RR

Slope Intercept

8.90** –

RC

Slope Intercept

8.62** –

RS

Slope Intercept

2.61 25.10***

RR

Slope Intercept

0.28 10.41**

RC

Slope Intercept

0.80 0.04

RS

Slope Intercept

5.77* –

RR

Slope Intercept

7.79** –

Overall abundance (log)

Annual variation in overall abundance (log)

Mean population size (log)

Annual variation in population size (log)

Turnover rate

Source

L-Ratio

Intercepts of regression lines were only compared when slopes were not significantly different. ∗

p < 0.05. p < 0.01. ∗∗∗ p < 0.001. ∗∗

but large changes in fragmentation. However, by exploring the effects of fragmentation in isolation we can better understand complex species’ responses evident in real landscapes (Fahrig, 2003). Here, we combined a well-established plant functional type (PFT) approach (Grime, 1974, 1977) with computer simulations

297

in artificial landscapes to derive a more general understanding of plant species responses to landscape fragmentation without the confounding effects of habitat loss. We focussed our study on conceptual functional plant types with ruderal characteristics, all are early successional species but main types differ in their relative competitive and stress-tolerating strength. In summary, all simulated PFTs showed a negative trend with increasing fragmentation intensity. However, the detailed population responses quantified by several output variables differ not only between the main PFT affiliations but also for contrasting subtypes within each main functional type. Perhaps the most striking result is that ‘pure’ ruderals showed the strongest negative responses to fragmentation. At first glance this appears to contradict previous modelling studies on the effects of habitat destruction on species extinction. Recent models of competition among sessile organisms predict that as habitat is destroyed the species first at risk of extinction are the superior competitors (Tilman et al., 1994, 1997; Klausmeier, 1998; Malanson, 2002; Malanson et al., 2007). This result largely derives from the assumption that a trade-off exists between competitive and colonisation ability. Habitat destruction is modelled as the loss of habitable sites, thereby decreasing the overall landscape capacity and the effective colonisation rate of all species. The poorest dispersers, which under such models are assumed to be the best competitors, were among the first driven to extinction by habitat destruction. Because such extinctions can occur generations after habitat destruction they represent an ‘extinction debt’ (Tilman et al., 1994). Tilman et al. (1997) concluded that inferior competitors are better able to persist by virtue of their greater dispersal ability and/or lower mortality rates. Malanson et al. (2007) combined a related competitioncolonisation approach with spatially-explicit simulations of forest stands in fragmented landscapes using the same landscape generating algorithm as applied here. Although the results depend on specific parameter settings and disturbance scenarios investigated, the basic lessons from competitioncolonisation models of these previous examples stand. The key difference between these previously mentioned studies (as well as other modelling studies exploring the effect of landscape changes on plant species, e.g. (Schwartz, 1993; Gu et al., 2002; Cousins et al., 2003) and our model is that we explicitly exclude habitat loss in order to focus on the effects of habitat fragmentation alone. The frequently reported finding that the competition-colonisation trade-off is able to explain species coexistence in heterogeneous landscapes (e.g. Tilman et al., 1997; Malanson et al., 2007, but see Higgins and Cain, 2002, for a critical evaluation of this trade-off and related ‘competition-colonisation’ models) is based on two contrasting strategies: local survival versus dispersal to new habitats. Habitat destruction, as modelled by Tilman et al. (1994, 1997), Klausmeier (1998) or Malanson et al. (2007), includes habitat loss reducing patch-level and overall landscape capacity. This would clearly have a stronger negative effect on those species investing in local survival, i.e. better competitors, than under a scenario where local conditions remain equivalent but there are changes to the spatial structure of its existing suitable habitat. In contrast, for species such as ruderals which depend on and invest in dispersal any increase in distances between

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habitable sites will have negative consequences despite maintenance of local habitat capacity. Interestingly, a review of the empirical literature by McCarthy et al. (1997) revealed no evidence of an increased extinction risk among superior competitors (see also Klausmeier, 1998). This further supports our view that in order to gain a better understanding of the response of sessile organisms to landscape changes it is necessary that one distinguishes between the effects of habitat loss and fragmentation. Both factors differ in their importance with regard to species’ traits determining local survival (e.g. competitive abilities) or dispersal.

4.1. Empirical studies on fragmentation effects on plant populations Several empirical studies have addressed fragmentation effects on species or species diversity (for summaries see Saunders et al., 1991; Debinski and Holt, 2000; Fahrig, 2003; Henle et al., 2004). However, most of these studies focus on (mobile) animal species and only a relatively few publications deal with fragmentation effects on plant species (but see Hobbs and Yates, 2003). Additionally, very few of these plant studies investigate more than one or two plant species (Zschokke et al., 2000; Lienert et al., 2002a; Hooftman et al., 2003; Lienert and Fischer, 2003; Bruna and Oli, 2005; but see Kolb and Diekmann, 2005) and experimental studies are mostly restricted to small scales for methodological reasons (Robinson et al., 1992; Zschokke et al., 2000). In contrast to existing empirical studies, where fragmentation can also have positive effects on population fitness parameters (Fahrig, 2003; Henle et al., 2004), our results show exclusively negative responses to increasing fragmentation. This can be explained by the fact that we did not include possible positive edge effects, such as additional input of resources at the boundaries. These are reported or implicitly included in empirical studies (Cunningham, 2000; Zschokke et al., 2000) and are typically reported for higher mobile species. However, positive edge effects for plants are only shown for small scale fragments (Zschokke et al., 2000). The importance of dispersal abilities in fragmented landscapes as highlighted by this and previous modelling studies is supported by empirical studies (Piessens et al., 2005). For example, Kolb and Diekmann (2005) found that emergent subgroups of herbaceous plants in fragmented deciduous forests exhibit an array of different dispersal abilities. Those groups with poorer dispersal abilities (including the higher portion of annuals) showed a higher sensitivity to habitat connectivity than better dispersers, but both groups showed a similar response to habitat area.

4.2.

Answers to initial research questions

Our results support the hypothesis that the approach of plant functional groups based on Grime’s CSR scheme is suitable to gain a better understanding of the effects of landscape fragmentation on plant populations: for five out of six output variables (exception: total sum of individuals) simulations showed significant differences between the responses of main RC-, RS- and RR-strategies.

Though nearly all types, as well as subtypes, showed negative responses to habitat fragmentation for a range of variables indicative of population viability (i.e. minimum portion occupied habitats and turnover rate decrease, inter-annual variations of overall abundance and population size increase) and/or variables related to fitness (i.e. mean values of overall abundance and population size decrease) some plant types are less vulnerable than others. One of the least vulnerable subtypes to increasing habitat fragmentation is the stress-tolerating RS2 subtype. This longer living perennial type is adapted to variable climatic conditions. Clearly the ability to cope with bad weather conditions is a more successful strategy than the other RS-subtype’s strategy of maintaining a seed bank. However, as expected, subtypes with dormant seeds (“temporally” dispersing RR2 and stress avoiding RS1) are better able to cope with habitat fragmentation than corresponding subtypes alternatively adapted to disperse in space. Piessens et al. (2005) has demonstrated the importance of seed banks for the maintenance of isolated populations in Belgian heathland areas. Only the competitive RC subtypes show similar resilience as seed bank-types. This can be explained by the high competitive ability and the longer local population persistence of RC-types during succession. Thus, they do not rely as much on spatial dispersal as the other types and are more “pre-adapted” to habitat fragmentation. The general finding that turnover rates decrease with increasing fragmentation – an indication of higher extinction rates and/or lower colonisation rates – corresponds well with research conducted by Hill and Caswell (1999). They found that the equilibrium fraction of vacant suitable habitats increases with increasing landscape fractal dimension. Interestingly, all simulated functional types showed an increase in inter-annual variation in overall abundance; in the case of strictly ruderals population size was also variable. This corresponds well with the ‘hyperdynamism’ concept, i.e. an increase in the temporal variance of population, community or landscape characteristics with increasing fragmentation (Laurance, 2002; Malanson et al., 2007). Our simulation results not only show that the main functional types, based on the CSR-concept, can be distinguished in their response to fragmentation but also most pairs of contrasting subtypes. This finding clearly indicates that in addition to the basic CSR-classification a further differentiation into functional or life cycle subtypes is necessary in order to better predict the response of plants to fragmentation processes. With regard to ecological forecasting our results also indicate that in most cases a single trait is not sufficient to predict a species response to fragmentation. Instead, it is essential to look at whole trait syndromes, i.e. functional types. For illustration, consider a type that produces dormant seeds. Basing our predictions on this trait alone would lead us to conclude that, in respect to turnover rate, such a type would be less sensitive to fragmentation. However, when we model the functional type as a whole we find that the regional vulnerability (minimum habitat occupancy and overall abundance) of this type is sensitive to fragmentation. Thus, other type-related traits may compensate for possible positive effects of a persistent seed bank.

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Clearly, it is an ongoing task to identify which trait combinations describe best how (plant) species respond to fragmentation or other aspects of landscape change (Henle et al., 2004). However, this study shows that functional type models are a suitable platform to derive an improved understanding of the differential sensitivity of species to fragmentation which can be the basis for future predictions and its use in applied conservation.

5.

Conclusion

In tracking species of the known CSR-allocation (here for species in the ruderal corner of the triangle) with a rather general plant population model we were able to explore and develop hypotheses on plant species’ responses to habitat fragmentation. Clearly, this approach is not necessarily restricted to the CSR approach. Other categorisation concepts (e.g. Westoby, 1998) could be used as a basis to identify functional types. By exclusively focussing on changes in the spatial pattern of suitable habitats we could show that it is important to distinguish between two dominating aspects of landscape change. Fragmentation per se can lead to different, unintuitive responses regarding a species vulnerability compared to modelling studies which have explored the combined effects of habitat loss and fragmentation. These conclusions are relevant for species conservation. Although habitat destruction causes both loss and fragmentation (e.g. deforestation) the management of species in surviving areas may depend on the relative strength of either process. However, our findings also support the results of previous modelling studies which found that landscape changes are likely to lead to extinction debts, i.e. species declines may only become evident generations after the initial landscape change. Though the current model focuses on fragmentation, the basic approach can be applied to other aspects of environmental change, e.g. climate or dynamic land use changes. Given the complexity of combined landscape-climate changes we urgently need approaches that help us to derive a predictive understanding of key mechanisms as a basis for conservation and management.

Acknowledgements We thank the editor, Brian Fath and two anonymous referees whose thorough reviews helped to improve the paper. Thanks also to Richard Walters who helped to improve the English and the style. We acknowledge the funding of the Deutsche Forschungsgemeinschaft (DFG) for this work (BU-1386/1).

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