Habitat-based models for predicting the occurrence of ground-beetles in arable landscapes: two alternative approaches

Agriculture, Ecosystems and Environment 95 (2003) 19–28

Habitat-based models for predicting the occurrence of ground-beetles in arable landscapes: two alternative approaches Sandrine Petit a,∗ , Karen Haysom b , Richard Pywell c , Liz Warman c , David Allen d , Roger Booth b,1 , Les Firbank a a

Centre for Ecology and Hydrology, CEH Merlewood, Grange-over-Sands, Cumbria LA11 6JU, UK b CABI Bioscience, Silwood Park, Buckhurst Road, Ascot, Berkshire SL5 7TA, UK c Centre for Ecology and Hydrology, Monks Wood, UK d ADAS Wolverhampton, Woodthorne, Wergs Road, Wolverhampton WV6 8TQ, UK Received 20 March 2002; received in revised form 22 August 2002; accepted 30 August 2002

Abstract The potential of habitat-based models was explored to predict the occurrence of carabid beetles in arable conditions. It was hypothesised that: (i) the habitats surrounding a location were good predictors of the occurrence of the most common carabid species; (ii) the current knowledge on habitat associations for some individual species was sufficient to develop accurate predictive models. The performance of knowledge-based models was assessed for eight well-studied carabid species. Rule sets were produced using an extensive database describing the nature and condition of the habitats recorded within a 50 m radius of the sampling sites. The performance was compared to a more classical approach based on logistic regression (LR) models, using the same original information summarised into 19 variables by correspondence analysis (CA). The performance of the rule-based (RB) models was higher than expected by chance for species occurring in less than 70% of the sites (k > 0.4) and was relatively consistent across the three areas of England where they were tested. Models developed for widespread species had a high prediction success (PS) but no discriminatory ability (low k value). LR and RB approaches gave comparable results for species of average prevalence (30–70%) while for species occurring in less than 30% of the sampled sites, the RB approach performed significantly better than the LR one. It is suggested that knowledge-based approaches could be used more widely to predict the distribution of invertebrate species. The effect of species prevalence and the potential application of knowledge-based habitat-models in the context of biodiversity assessment are discussed. © 2002 Elsevier Science B.V. All rights reserved. Keywords: Carabids; Expert knowledge; Logistic regressions; Rule-based models; Species monitoring; Species prevalence

1. Introduction During the last decades, modern farming methods have led to a decline of many wildlife groups in rural ∗ Corresponding author. Tel.: +44-1539-532-264; fax: +44-1539-534-705. E-mail address: [email protected] (S. Petit). 1 Present address: Department of Entomology, The Natural History Museum, Cromwell Road, London SW7 5BD, UK.

landscapes (Stanners and Bourdeau, 1995; Jongman, 1996; Chamberlain et al., 2000) and agricultural intensification remains the main threat to biodiversity in most parts of western Europe (Petit et al., 2001). To address this situation, some agri-environment schemes have been promoted to maintain existing uncultivated habitats or create new ones (Ovenden et al., 1998). Monitoring is a tool which provides guidelines for making decisions on how to manage land and as such

0167-8809/02/$ – see front matter © 2002 Elsevier Science B.V. All rights reserved. PII: S 0 1 6 7 - 8 8 0 9 ( 0 2 ) 0 0 1 7 3 - 1

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is an integral part of efforts to stop the loss of biodiversity (Niemela, 2000). Although exhaustive surveys of all taxa and habitats might be attempted on a local scale, in many areas and for many taxonomic groups, setting up extensive monitoring programmes would by far exceed the resources available to ecologists (Lawton et al., 1998). One way to assist predicting the ecological effect of specific land management measures is to develop habitat-models, based on various hypotheses as to how environmental factors control the distribution of species (Owen, 1989; Fraser, 1998). The development of such models has increased (Leemans, 1999; Guisan and Zimmermann, 2000) and they have been used to assess the impact of land use (Swetnam et al., 1998) and climate change (Kienast et al., 1998) on the distribution of species. Habitat-models have been developed mostly to predict the distribution of birds (Fielding and Haworth, 1995; Manel et al., 1999; Boone and Krohn, 1999) and few attempts have been made to use this approach for invertebrates (Rushton et al., 1996; Petit and Burel, 1998; Cowley et al., 2000; Manel et al., 2001; Bonn and Schroder, 2001). Carabidae is a good candidate family; it is species rich, many of which are abundant in arable conditions where their habitat distribution has been studied extensively (Luff, 1987; Lovei and Sunderland, 1996; Kromp, 1999). They are often aggregated in patches located in the crop or/and field margins, these associations being temporally stable over short time-scales (Thomas et al., 2001). Carabid diversity in arable conditions is usually enhanced by crop diversification in terms of heterogeneity, weediness, inter-cropping and field boundaries (Kromp, 1999). The structural and vegetative properties of field margins are also important in determining beetle composition (Thiele, 1977). It was assumed that: (i) the nature and condition of habitats surrounding a location might be good predictors of the occurrence of the most common carabid species; (ii) the current knowledge on habitat associations for some individual species could be sufficient to develop accurate predictive models. These two hypotheses were tested based on knowledge-based models developed for eight well-studied carabid species. Existing literature and expert knowledge about the ecological requirements of species were translated into a number of rules for predicting their occurrence. This knowledge-based approach was compared to a more

classical approach, i.e. logistic regression (LR) models were developed in parallel from the empirical data and validated using independent data sets. 2. Methods The study sites were located in three distinct parts of England: East Anglia, West Midlands, North Yorkshire (Fig. 1). In East Anglia, 43 sites on 12 distinct arable farms were surveyed in 1999 or 2000. In the West Midlands, 40 sites on 11 arable farms were surveyed in 2000. In both regions, a range of habitats was sampled i.e. crops, overwintered stubbles, undersown spring cereals, conservation headlands, grass field margins, beetle banks, uncropped or planted strips. In North Yorkshire, the 12 study sites were grassy field margins of various widths located on Manor Farm, an arable enterprise of 164 ha (Telfer et al., 2000) and surveyed in 1999. Carabid beetles were sampled by pitfall trapping in 1999 (28 sites) and 2000 (55 sites) in East Anglia and the West Midlands. Each sample site comprised five polypropylene containers (82 mm diameter), placed

Fig. 1. Location of the three regions of England where sampling occurred. EA: East Anglia, WM: West Midlands, NY: North Yorkshire.

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Table 1 The occurrence frequency and life-history traits of the eight carabid species considered in the analyses along with their percentage of occurrence in the study locationsa Species

Distribution in England

Ecological requirements

1. Harpalus rufibarbis (Fabricius) Eastern England Open/shaded places 2. Carabus violaceus (Linnaeus) Widespread Park, gardens, forest 3. C. fuscipes (Goeze) Rather eastern Open habitats drained soils 4. P. cupreus (Linnaeus) Mainly southern Open, warm 5. Nebria brevicollis (Fabricius) Widespread Forest to open habitats 6. Bembidion lampros (Herbst) Common Open, sunny, short vegetation 7. Trechus quadristriatus Widespread Open habitats (Schrank) 8. Agonum dorsale (Pontoppidan) Common Dry open habitats, gravel

Distribution in farmlandb

Breeding seasonc

Size (mm)

Non-E, non-U Non-E, U E, U

S A A

6–9.5 ? 20–30 – 10–14 –

19 25 39

E, non-U E, U E, U

S A S

11–13 + 10–14 −/+ 3–4 +

57 67 77

E, U

A

3.5–4

+

83

E, U

S

6–8

?

84

Flying statusd

Occurrence (%)

a Species were numbered according to the percentage of occurrence in the data set (species 1 is occurring in the least sites while species 8 is occurring in the most sites). b U: ubiquitous; E: eurytopic found in more than five habitats. Distribution in farmland from Eversham et al., 1996. c S: spring breeders; A: autumn breeders. d +: Flight frequently recorded; −/+: flight rare; −: does not fly; ?: status not clear.

2 m apart on a line and sunk with the lip flush with the ground. At the beginning of each trapping period, 2 cm ethylene glycol was added for a period of 14 days. Wire mesh was placed over the traps to minimise the capture of non-target animals. Two trapping periods occurred in 1999 (mid-June and early July), three in 2000 (October 1999, April/May and July). In North Yorkshire, a site comprised eight traps similar to those described above, opened 29 days in spring 1999 and 7 days in autumn 1999. Carabidae were identified according to Lindroth (1974). Data from each site (5 or 8 traps) were aggregated to derive presence/absence of individual species at each site. Eight carabid species were selected following two criteria: (i) presence in at least 20–80% sites; (ii) fair knowledge of the species ecological requirements (Table 1). The overall environmental quality of the sampling sites was assessed by describing all habitat patches present within 50 m radius of the centre of each line of pitfall traps, based on Welsh (1990) finding that most carabid movements occur within a 50 m distance. Table 2 lists the habitat types used and the number of associated descriptors. For each sampling site, there were as many entries as habitat patches within 50 m. The rules developed to predict the occurrence of individual species at the different sites were based on

Table 2 List of habitat types and number of associated descriptors Main categories of habitat

Habitat type

Woodland

Woodland Individual tree Line of tree

Number of descriptors 5 5 5

Scrub

Scrub

5

Unimproved grassland

Unimproved grassland

5

Farmland

Improved grassland Ley Arable land Land not in production Set-aside

5 10 4 5 5

Non-crop habitats

Grass margin Beetle bank Uncropped strip Planted area Crop margin

9 10 9 10 9

Boundaries

Hedgerow Fence Stone wall Earth bank Ditch

13 3 3 5 5

Structures

Buildings/amenity Track and road

4 4

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S. Petit et al. / Agriculture, Ecosystems and Environment 95 (2003) 19–28

Table 3 List of environmental variables used at the habitat level Variables

Description

H

Shannon diversity index of habitats

Wd Lt Sh WD

Number of woodland patches Number of patches of lines of trees and individual trees Number of patches of scrub Total number of woody patches (woodland, hedges, lines of trees, scrubs)

Grl BDV GR

Number of grassland patches Number of patches of vegetated bank/ditch/verge > 1 m wide Total number of grassy patches (grass margins, beetle banks, sown margin, BDV, grassland)

JX1, JX2, JX3

Correspondence analysis (CA) scores for presence and vegetation structure of non-crop habitats. JX1 presence/absence, JX2 bare ground/vegetation cover, JX3 grasses/forbs Litter cover of non-crop habitats Width of the widest non-crop habitat

LitJ WidJ HX1, HX2 GapH LitH SX1, SX2

CA scores for presence and ground layer vegetation structure of hedges. HX1 presence/absence, HX2 mixed/grass dominated Gappiness of hedge Litter cover of hedge CA scores for presence and vegetation structure of set-aside/land not in production. SX1 presence/absence, SX2 bare ground/grass, vegetation cover

literature review (Penney, 1966; Lindroth, 1974, 1992; Thiele, 1977; Luff, 1987, 1998; Stork, 1990) and expert knowledge, the principle being to identify the landscape features listed in the protocol which provided adequate or inappropriate habitat. Between 6 and 18 landscape features were used for the eight carabid species, including both crop and non-crop habitats. The environmental database was queried using Microsoft Access (2000) and sites with suitable features were predicted to contain the species. The environmental database were synthesised into a few environmental variables by running multivariate analyses (CA) and using the score on the relevant axis as variables. The list of variables used in the regression analyses is presented in Table 3. Robust predictions make use of independent testing data, not used to develop the prediction model (training data). The data set was split randomly into 70 rows for training and 25 rows for testing. To decrease the effect of chance, the procedure was repeated five times and created five distinct training sets that generated five distinct models. This increased confidence in the explanatory environmental variables used and gave a standard deviation for the overall performance measures of the regressions (Binary Logistic Regression, Minitab). The overall performance of LR models was

estimated by the concordance, a measure of association between the model predictions and the observed validation set (n = 25). A species was concordant if it was present at a site where its probability of occurrence was >0.5, or absent at a site where its probability of occurrence was <0.5, or discordant if it occurred where it was not predicted (or vice versa), or tied if the probabilities of presence/absence were equal. The performance of the presence/absence model was summarised using a confusion matrix of the observed and predicted patterns where a is the true positive, b the false positive, c the false negative and d the true negative cases (Fielding and Bell, 1997). From this matrix, several performance measures can be derived (Table 4). The Cohen’s kappa statistic k (Fleiss, 1981) was used to test the agreement between the observed proportion of correct classified sites and the proportion expected by chance. Prediction is in fair to good agreement beyond chance for k > 0.4 and excellent agreement when >0.75. Other measures were the overall prediction success (PS), negative and positive predictive power (NPP and PPP) and the specificity and sensitivity (Sp and Sn). The performance of the rule-based (RB) models was assessed using the whole data set (n = 95). Differences in performance were considered with respect to

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Table 4 Performance measures of the models derived from the confusion matrix (modified after Fielding and Bell, 1997 and Manel et al., 2001) Performance measure and definition

Formula

Cohen’s kappa k Proportion of specific agreement Prediction success Percentage of cases correctly predicted Sensitivity Percentage of true positives correctly predicted Specificity Percentage of true negative correctly predicted Positive predictive power Percentage of predicted presences that were real Negative predictive power Percentage of predicted absences that were real

[(a + d) − ((a + c)(a + b) + (b + d)(c + d))/n]/ [n − ((a + c)(a + b) + (b + d)(c + d))/n] 100(a + d/n) 100(a/(a + c)) 100(d/(b + d)) 100(a/(a + b)) 100(d/(c + d))

the prevalence of each species and within the three geographical regions separately (t test). The success of the two approaches, a priori selection of variables by RB and analysis of summarised variables by LR models were compared by running the RB models on the five validation data sets (n = 25) created to test the LR models. We compared the k values and the PS of both approaches for each species (t test).

3. Results Table 5 presents the performance of the models developed for the eight carabid species. There was a relatively good agreement for species 1–5 while the agreement was not significantly higher than expected by chance for species 6–8. Several performance measures were highly correlated with the species prevalence (Fig. 2). The k values were inversely related to species occurrence (r = Table 5 Performance measures of the RB models when tested on the whole data set (n = 95) (see Table 4 for formulae) Species

k

PS

PPP

NPP

Sn

Sp

1. 2. 3. 4. 5. 6. 7. 8.

0.60 0.39 0.29 0.42 0.48 0.06 0.04 −0.01

88 78 67 72 78 80 80 78

73 57 60 71 80 80 83 84

91 83 69 76 69 50 25 14

61 50 49 87 87 98 96 91

94 86 79 54 58 5 7 8

H. rufibarbis C. violaceus C. fuscipes P. cupreus N. brevicollis B. lampros T. quadristriatus A. dorsale

−0.803, n = 6, p < 0.02), mainly because species 6–8 had k values close to 0, significantly lower than k values of species 1–5. The specificity of the models decreased when species occurrence increased (r = −0.957, n = 8, p < 0.001) while their sensitivity tended to increase (r = 0.895, n = 6, p < 0.005). The ability of models to predict absence (NPP) decreased significantly when species were common (r = −0.879, n = 6, p < 0.02) while ability to predict presence (PPP) increased (r = 0.805, n = 6, p < 0.02). The comparison of the k values of RB models between the three regions of England was difficult because many species occurred in either 0 or 100% of the sites in at least one of the regions, resulting respectively in a k value of 0 or in a k value that cannot be estimated. The k values seemed consistent across regions for Calathus fuscipes and Pterostichus cupreus (Table 6). The PS of models was relatively consistent across the three regions of England and the mean PS per region (all species pooled) did not vary significantly between regions. The mean concordance between the LR models developed and the actual observations is presented in Table 7. For species 1–5, significant models were developed for C. fuscipes for which one model was not significant. The most successful predictors were generally associated with non-crop habitats and described their presence (JX1), vegetation cover (JX2) and litter cover (LitJ). The relationships between the environmental variables selected in this study and species 6–8 were less clear and regression models were often not significant.

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Fig. 2. PS, kappa (k), Sn, Sp, PPP and NPP for the eight examined carabid species as a function of their occurrence (from left to right, species 1–8).

The performance of the two modelling approaches is presented in Table 8, C. fuscipes and species 6–8 being excluded from this comparison. When habitat variables were selected a priori and translated into rules,

the k values for species 1–2 were significantly higher than for LR models (t = 2.26, p = 0.05). Species 2–5 also had a higher Sp of RB models (t = −2.76, p = 0.01).

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Table 6 Occurrence of species, kappa statistics (k) and PS of the eight RB models in three areas of England; EA: East Anglia (n = 43), WM: West Midlands (n = 40), NY: North Yorkshire (n = 12)a Species

1. 2. 3. 4. 5. 6. 7. 8.

EA

H. rufibarbis C. violaceus C. fuscipes P. cupreus N. brevicollis B. lampros T. quadristriatus A. dorsale a

WM

NY

Occ.

k

PS

Occ.

k

PS

Occ.

k

PS

40 37 37 65 50 67 91 86

0.50 0.48 0.37 0.24 0.52 0.06 −0.08 0.10

76 76 71 69 76 69 86 81

0 20 50 43 83 85 75 83

– 0.14 0.36 0.50 0.06 0 0.03 −0.12

100 75 68 75 78 85 71 75

8 0 8 58 83 100 83 83

0 0 −0.16 0.38 0.63 0 0 0.63

92 100 50 75 92 100 100 92

The kappa statistics cannot be calculated when species occurrence is null and is equal to 0 when species occurrence is 100%.

Table 7 Mean and S.D. of percentage of concordance and discordance of the five LR models tested on independent test data sets Species

na

Concordance mean (S.D.)

Discordance mean (S.D.)

Variables significant in all five models

1. 2. 3. 4. 5. 6. 7. 8.

5 5 4 5 5 3 2 3

68.0 69.0 31.8 66.0 71.7 36.6 64.7 57.0

26.3 22.4 40.4 31.6 22.7 52.1 23.7 6.9

JX1, LitJ, –b JX1, JX2, – – –

H. rufibarbis C. violaceus C. fuscipes P. cupreus N. brevicollis B. lampros T. quadristriatus A. dorsale a b

(14.8) (17.1) (17.9) (10.6) (6.5) (13.0) (20.0) (11.4)

(14.8) (16.9) (16.5) (9.3) (6.3) (18.0) (5.3) (7.17)

JX2 BDV LitJ LitJ

Number of significant models. Refers to cases where at least one model was not significant.

Table 8 Mean and S.E. k and PS of RB and LR models tested on five sets of 25 sites (see Table 4 for formulae) Species

1. 2. 3. 4. 5. 6. 7. 8.

H. rufibarbis C. violaceus C. fuscipes P. cupreus N. brevicollis B. lampros T. quadristriatus A. dorsale

RB models

Logistic regressions

Occ.

k

19 26 44 55 65 77 83 84

0.57 0.45 0.22 0.39 0.43 0.00 0.00 0.00

PS (0.28) (0.07) (0.13) (0.11) (0.12) (0) (0) (0)

4. Discussion and conclusion The performance of the habitat-models was satisfactory for the least widespread species 1–5. When these species were pooled, the ruled-based models

87 78 62 70 74 88 91 93

k (7) (2) (8) (5) (10) (9) (9) (7)

0.24 0.25 NA 0.09 0.35 NA NA NA

PS (0.24) (0.13) (0.16) (0.29)

77 (10) 75 (3) NA 56 (10) 63 (28) NA NA NA

had an overall k value above 0.4, which indicates fair to good agreement of the models beyond chance. LR models also performed better for these species. This indicates that the surrounding habitats were good predictors of the presence/absence of carabid

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beetles, in accordance with the work of Duelli (1997). The performance measures Sp and Sn were dependent on the occurrence of species and should be handled carefully. The shape of the relationship between these two performance measures and species prevalence was comparable to those performed on birds (Manel et al., 1999) and on aquatic invertebrate species (Manel et al., 2001). The present results support the conclusions of Manel et al. (2001) who recommended that ecologists reduce reliance on PS as an indicator of model performance in favour of measures unaffected or only negligibly affected by the prevalence of species, such as Cohen’s k statistics. Cowley et al. (2000) also developed habitat-models for 26 butterflies species and found significantly lower k values for the eight most prevalent species. Boone and Krohn (1999) demonstrated for birds that some attributes of species such as abundance, niche width or range indicate that some species are more likely to be modelled correctly than others. This effect of species traits on the likelihood to develop accurate habitat-models is worth exploring for future invertebrate modelling exercises. The second objective of this paper was to examine whether the available ecological knowledge of carabid beetles was sufficient to develop accurate habitat-models. It appears that knowledge-based models performed better than expected by chance. This suggests that if an exhaustive list and descriptors of surrounding habitat patches were available, expert knowledge would be capable of predicting the occurrence of some of the most common carabid beetles. Very few attempts have been made to use RB models to predict the distribution of species (Daunicht et al., 1996; Bock and Salski, 1998), probably because it is felt that too little is known about the ecological requirements of species. For invertebrates, existing models focus on species numbers rather than on the distribution of individual species (Kampichler et al., 2000). Although models presented in this paper were relatively successful, the application of habitat-models in the context of biodiversity assessment is not straightforward. The species modelled in this paper were selected because knowledge about their ecological requirements was considered sufficient for the modelling exercise to be carried out. For most invertebrate groups, experts consider that knowledge of species

biology is not sufficient for such an approach to be applied (McCracken and Bignal, 1998). Also, the likelihood of developing accurate models depends on the prevalence of species (e.g. Boone and Krohn, 1999; Cowley et al., 2000). Habitat-models are less likely to be successful for species either too rare or too widespread. For the most widespread species, models may give a good representation of the actual species distribution (high PS value), even if the discriminatory ability of the models is low (low k value), whereas models developed for scarce species are likely to show low PPP (Boone and Krohn, 1999). Several authors have indicated that habitat-models often lack generality and are therefore limited in their application (Fielding and Haworth, 1995). It would be unwise to move to a system where models replaced species surveys, but the results presented here suggest that the development and potential applications of habitat-models should be explored further. The development of habitat-models targeted on species holding an umbrella effect (Simberloff, 1998), i.e. a species whose habitat requirements encapsulate the demands of other species, could be a way forward. If accurate and general habitat-models could be developed for such umbrella species, their ability to give predictions for a large number of other species would make them an important complementary tool in monitoring exercises. This approach was applied successfully to predict the assemblage of carabid beetles in alluvial forests from habitat-models initially developed for two individual species (Bonn and Schroder, 2001).

Acknowledgements This work was funded by the Department for Environment, Food and Rural Affairs formerly the Ministry of Agriculture, Fisheries and Food, contract OC9801. Thanks to two anonymous reviewers for their useful comments on an earlier version of the manuscript. Thanks are also due to the many farmers who allowed research on their land and to ADAS field workers, particularly Jim Dustow and Bobby Werb for providing information on study sites. We are grateful to Professor Valerie K. Brown, Dr. Ruth Feber and Dr. Liz Asteraki for their input and advice at many stages of this project. Thanks also to Alex Brook,

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