Why Do Some Species Have Geographically Varying Responses to ﬁ Re History?

Why do some species have geographically varying responses to ﬁ re history?

D. G. Nimmo , L. T. Kelly , L. M. Farnsworth , S. J. Watson and A. F. Bennett

D. G. Nimmo ([email protected]), S. J. Watson and A. F. Bennett, Landscape Ecology Research Group and the centre for Integrative Ecology, School of Life and Environmental Sciences, Deakin Univ., Burwood, VIC 3125, Australia. – L. M. Farnsworth and SJW, Dept of Zoology, La Trobe Univ., Bundoora, VIC 3086, Australia. – L. T. Kelly, School of Botany, Univ. of Melbourne, Parkville, VIC 3010, Australia.

A capacity to predict the eff ects of fi re on biota is critical for conservation in fi re-prone regions as it assists managers to anticipate the outcomes of diff erent approaches to fi re management. Th e task is complicated because species ’ responses to fi re can vary geographically. Th is poses challenges, both for conceptual understanding of post-fi re succession and fi re management. We examine two hypotheses for why species may display geographically varying responses to fi re. 1) Species ’ post-fi re responses are driven by vegetation structure, but vegetation – fi re relationships vary spatially (the ‘ dynamic vegetation ’ hypothesis). 2) Regional variation in ecological conditions leads species to select diff erent post-fi re ages as habitat (the ‘ dynamic habitat ’ hypothesis). Our case study uses data on lizards at 280 sites in a ∼ 100 000 km 2 region of south-eastern Australia. We compared the predictive capacity of models based on 1) habitat associations, with models based on 2) fi re history and vegetation type, and 3) fi re history alone, for four species of lizards. Habitat association models generally out-performed fi re history models in terms of predictive capacity. For two species, habitat association models provided good discrimination capacity even though the species showed geographically varying post-fi re responses. Our results support the dynamic vegetation hypothesis, that spatial variation in relationships between fi re and vegetation structure results in regional variation in fauna – fi re relationships. Th ese observations explain how the widely recognised ‘ habitat accommodation ’ model of animal succession can be conceptually accurate yet predictively weak.

An ability to predict the occurrence of species is critical in show that the response of species to fi re can vary geographi- regions prone to frequent disturbance, particularly when cally (Nimmo et al. 2012, Smith et al. 2013). Th is presents such disturbances are amenable to management (Noble and an apparent contradiction: the habitat accommodation Slatyer 1980). Fire is a disturbance that can dramatically model is conceptually accurate (i.e. habitat structure is the alter the distribution and abundance of species over long primary driver of post-fi re succession for many animal temporal scales, from decades to centuries (Watson et al. species) but, as applied, is often predictively weak. 2012). Evidence suggests that over such time-frames, fi re One hypothesis which can reconcile this contradiction mainly infl uences fauna indirectly via its infl uence on the proposes that although species ’ post-fi re responses are provision of resources during post-fi re succession of largely driven by habitat structure, the way in which habitat vegetation (Monamy and Fox 2000, 2010, Fox et al. 2003). structure responds to fi re may vary across space and time Th is is the basis of the ‘ habitat accommodation model’ (Fox (Driscoll et al. 2012). Th us, although faunal responses to 1982), which posits that species enter a post-fi re succession habitat structure are relatively predictable, the pattern of when their habitat requirements are met and leave, or occurrence of fauna post-fi re may not be so. Such a scenario decrease in number, when post-fi re changes in vegetation could occur if climate interacts with post-fi re vegetation or competitive interactions render it unsuitable. dynamics such that the response of vegetation to fi re Support for the habitat accommodation model comes diff ers across climatic gradients, or if other processes driving largely from small scale, experimental studies of small vegetation structure, such as grazing pressure by herbivores, mammal responses to fi re (Monamy and Fox 2000, Fox vary across space (Staver et al. 2009). We term this the et al. 2003). Correlative studies that have attempted to pre- ‘ dynamic vegetation’ hypothesis. A second hypothesis is dict species ’ responses to fi re a priori based on their habitat that across broad geographic regions, changes in ecological requirements have met with limited success (Driscoll and conditions (e.g. ambient temperature, the presence of diff er- Henderson 2008, Lindenmayer et al. 2008). Furthermore, ent competitors or predators) lead species to select diff erent larger-scale (in terms of spatial extent) correlative studies habitats, with the result that species ’ responses to habitat

805 structure vary across space. In such a case, the response of mallee vegetation. Broad vegetation associations to the fauna to fi re history will be highly unpredictable. We term north and south of the river are similar (Haslem et al. 2010) this the ‘ dynamic habitat ’ hypothesis. and most reptile species occur on both sides of the river. It is possible to explore support for these alternate Here, we refer to these two parts of the region divided by hypotheses. If the dynamic vegetation hypothesis is true, the Murray River as the southern and northern ‘ subregions ’ , then distribution models based on proximate variables (e.g. respectively (Fig. 1). vegetation structure) should be able to predict species’ occurrence, even when models based on fi re history cannot. Conversely, if the dynamic habitat hypothesis is true, Site selection models based on vegetation structure are likely to be limited in their ability to predict species ’ occurrences. Th is study was undertaken as part of the Mallee Fire and Here, we test these two hypotheses by developing and Biodiversity Project: a landscape-scale study investigating comparing models of the occurrence of lizard species in the infl uence of fi re on biodiversity. Sites were nested 2 relation to fi re history (time since fi re) and habitat within 28 landscapes, each 12.56 km in size that were structure across a bioregion (∼ 100 000 km 2 ) in semi-arid stratifi ed according to the extent and composition of fi re- south-eastern Australia. We compare the predictive capacity age classes (see Nimmo et al. 2013 for further details). of three sets of species distribution models (SDMs): 1) those At each of 10 sites in each landscape, a pitfall trap line developed by Nimmo et al. (2012), which modelled species was established that consisted of ten 20 l buckets buried in occurrence in relation to a century of fi re history (i.e. a the ground at 5 m intervals and connected by a 50 m con- 100 yr post-fi re chronosequence), but allowed for a separate tinuous drift fence. Th ere were 280 sites in total, divided response curve for diff erent vegetation types; 2) models of equally between southern and northern subregions ϭ species ’ responses to fi re history alone; and 3) models of (i.e. n 140 sites to the north and south of the Murray species ’ occurrence based directly on site attributes (e.g. veg- River, respectively). Study landscapes were separated by Ն etation structure). We assess the ability of these models 2 km, with a mean distance of 130 km between them. to predict species ’ occurrence both within the geographic Sites nested within landscapes were separated by a mini- Ͼ area in which the model was calibrated and when projected mum of 200 m, and were selected to be 100 m from a into a novel geographic space. Using this approach, we aim fi re boundary. All sites were located in large conservation to determine whether changes in vegetation – fi re dynamics reserves ranging from 8155 to 631 942 ha in size. or changes in habitat preferences are likely to be responsible for spatially varying responses of fauna to fi re. Data collection

Reptile surveys were conducted over four trapping periods, Methods during the austral spring and summer in each of two years (2006– 2007, 2007– 2008), with each trapping session Study area being for fi ve consecutive days and nights. Th us, each site ∼ ² experienced trapping over 10 days/nights during spring and Th e study area of 104 000 km spans parts of three 10 days/nights during summer from 2006– 2008, for a total Australian states (Victoria, New South Wales and South of 200 trap nights per site; an overall total of 56 000 trap Australia) within the Murray Mallee region of south- nights. Individuals were marked (to identify re-captures eastern Australia (Fig. 1). Th e region is characterised by within the same period), identifi ed to species, and released at vegetation dominated by multi-stemmed ‘ mallee ’ euca- Ͻ the point of capture. lypts, generally 8 m in height (Haslem et al. 2011). Th e Vegetation surveys were conducted at all 280 sites region experiences a semi-arid climate, with a mean annual between June and December 2007. Data on vegetation rainfall of 200– 350 mm. Th e region is fi re prone: Ͼ structure and site attributes were collected as follows. 1) We large fi res 100 000 ha occur on a bi-decadal basis, established a 50 m transect at each site, running parallel to although the inter-fi re interval for individual sites is rela- Ͼ the pitfall trap line. At 1 m intervals (50 intervals tively long ( 40 yr) (Avitabile et al. 2013). When fi re per transect), we recorded contacts with a 2 m structure occurs (either wildfi re or planned burning), it essentially pole at four height intervals (0, 0.5– 1, 1– 2, Ͼ 2 m). removes all vegetation cover, both canopy and understorey 2) Two belt-transects were established: a 50 ϫ 4 m transect, (Haslem et al. 2011). As the time-since-fi re increases, within which attributes of trees and stems were recorded; vegetation regenerates and changes in habitat structure and a 50 ϫ 10 m transect within which the number of logs occur over many decades (Haslem et al. 2011). Planned was counted. We also recorded data on the soil type at each burning is commonly used within the region for both asset site. Explanatory variables used in the analyses are described protection and ecological management; however, as the in Supplementary material Appendix 1. region is sparsely populated, most planned burning takes the form of strategic burns to reduce the risk of large wildfi res, and to promote the maintenance of important Response and explanatory variables structural elements for fauna associated with long- unburned vegetation (Avitabile et al. 2013). We selected four species for investigation, chosen for three Th e Murray River and its associated mesic fl oodplain reasons. First, they represent a gradient in the predictive create a discontinuity in the extensive tracts of semi-arid capacity of fi re history models, as documented by Nimmo

806 Figure 1. Map of the study area showing all study landscapes (circles), with the northern and southern sub-regions separated by the Murray River (blue line). Grey shading indicates mallee vegetation. An inset shows an example of a study landscape, including the position of 10 sites at which site-scale data were collected. Th e diff erent colours represent diff erent fi re ages. Two photographs illustrate a recently burned and longer unburned site in mallee vegetation.

et al. (2012). Th e models for the Mallee dragon Ctenophorus could be relatively confi dent that observed absences were fordi (snout-vent length ϭ 58 mm; SVL) and the Murray ‘ real ’ absences (∼ 80% or greater; Supplementary material striped skink Ctenotus brachyonyx (SVL ϭ 83 mm) had Appendix 2). reasonable discriminative capacity within at least one subre- For each species, the response variable was the detected gion, but some or no predictive capacity across subregions. presence or absence of the species at a site over the duration Th e fi re history model for the painted dragon Ctenophorus of sampling (Nimmo et al. 2012), assumed to have a pictus (SVL ϭ 65 mm) had some discriminative capacity binomial distribution of errors. We selected fi ve explana- both within and between subregions, except within the tory variables (Supplementary material Appendix 1) that southern subregion. Models for the desert skink Liopholis represent components of vegetation structure, ground cover inornata (SVL ϭ 84 mm) showed reasonable predictive and soil. Th ese were chosen on the basis of knowledge capacity, both within and between sub-regions in all of the life history of the study species, with the aim of cases. Second, these reptile species are all terrestrial, including variables known to be used directly by these spe- heliothermic, and with the exception of the desert skink cies for shelter or foraging. Th e proportion of ground L. inornata (considered crepuscular) are diurnal in activity. covered by litter was positively correlated with canopy Th ese shared characteristics limited the number of explana- cover and negatively correlated with the combined cover of tory variables to be considered during model selection. Th ird, bare ground and cryptogramic crust (Spearman Rank Ͼ for all species, analysis of nightly detection probabilities correlation: rs 0.65). Consequently, we used the fi rst using the ‘ unmarked ’ package in R (Fishe and Chandler component from a principal components analysis of these 2011) suggested that, over the 20 night sampling period, we three variables, which explained 83.6% of the variance, as

807 Ͼ ∼ π well as being strongly correlated (rs 0.85) with each raw O ij Bernoulli( ij ); variable. Th is new variable, ‘ Ground ’ , represents a gradient π ϭαϩ ϩε ϩε logit( ij ) f (TSF i ): factor(VTij ) i (LS) ij of increasing canopy and litter cover, and decreasing π cover of bare ground. Rank correlations between the fi nal where ij depends on the time since the last fi re (TSF) and Ͻ set of fi ve explanatory variables were low (r s 0.4) as were the vegetation type of the site (VT). Th e interaction between variance infl ation factors (Ͻ 3.0). TSF and VT was fi tted using the ‘ by ’ command in gamm4. Th is interaction term allows species responses to track changes in vegetation structure that may diff er between Model fi tting vegetation types. However, fi re history is often used as a sur- rogate for biodiversity conservation at a coarser level, ignor- We used generalized additive mixed models (GAMMs; ing potential diff erences in successional dynamics between Wood 2006) to analyse the relationship between species’ vegetation associations. Th erefore, we generated a second occurrence and environmental variables, separately for each set of fi re history models in which only the variable ‘ time- subregion. GAMMs are a regression modelling technique since-fi re ’ was included, to assess the comparative perfor- that allows for non-linear relationships between explanatory mance of these simpler models in predicting species’ and response variables, and for the specifi cation of both occurrence. Th ese ‘ time-since-fi re only models ’ took the random and fi xed eff ects (Zuur et al. 2009). As sites were form: nested within landscapes, we included ‘ landscape ’ as a random factor to account for possible spatial structure in ∼ π O ij Bernoulli( ij ); the data. All environmental variables were fi xed factors. To logit(π ) ϭ α ϩ f (TSF ) ϩ ε ϩ ε generate GAMMs, we used the package gamm4 (Wood ij i i (LS) ij 2009) in R ver. 2.10.1 (R Development Core Team). Th is Th ese models also were generated separately for each species package closely approximates log-likelihood, allowing model for each subregion (north or south of the Murray River). comparisons using log-likelihood methods (Wood 2009). Th e amount of smoothing applied to continuous variables was determined internally during the model-building Model evaluation process using maximum likelihood (Wood 2009). We considered all combinations of the fi ve explanatory Two methods were used to assess the ability of models to variables describing habitat structure, resulting in n ϭ 31 discriminate between the presence or absence of a species: models. Th e global form of these ‘ habitat association models ’ 1) a measure of how well the model predicts within the took the general form: subregion in which the model was calibrated (‘ within- region predictive capacity ’ ), and 2) a measure of how well a ∼ π O ij Bernoulli( ij ); model calibrated in one sub-region can discriminate the π ϭαϩ ϩ ϩ ϩ ϩ logit( ij ) f (Gi ) f (T i ) f (Si ) f (Li ) factor(SLj ) presence or absence of the species in the other sub-region ϩε ϩε i (LS) ij (i.e. north or south of the Murray River; ‘ between-region predictive capacity ’ ). π where the probability of occurrence of species i at site j We used 7-fold cross validation for within-region pre- depends on the continuous variables; ground (G), Triodia dictive capacity (Pearce and Ferrier 2000). Th is involved cover (T), shrub cover (S), logs per ha (L); and a categorical partitioning each data set into seven ‘ folds ’ (equally variable indicating soil type (SL). Each continuous numbered groups of sites), calibrating a model on data explanatory variable is fi tted as a smoother f , α is the inter- from six folds, and making predictions to the seventh fold. cept; ε is the normally distributed error with expectation 0 Th e discriminative ability of the model was evaluated by σ2 ε and variance ; and i (LS) is the normally distributed error calculating the area under the Receiver-Operator Curve. associated with the random eff ect for ‘ landscape ’ (LS). (AUC) from comparison of the predicted and observed We used Akaike ’ s information criterion corrected for data. Th is process was undertaken seven times, allowing all small sample sizes (AICc ) and associated model weights sites to be evaluated and a mean value of AUC to be calcu- ( wi ) to compare the level of support for alternative lated. Because AUC values are infl uenced by the random models. Following this, we calculated relative weights of nature of data partitioning during cross-validation (i.e. the Σ evidence ( w i ) for each explanatory variable (Burnham and sites that are used in model building), we repeated this Anderson 2002). As GAMMs use smoothing splines, overall procedure fi ve times and selected the median parameter estimates could not be model-averaged in an AUC value. We followed Pearce and Ferrier (2000) in equivalent way as for generalised linear models. We used evaluating the predictive ability of models: AUC Յ 0.5 ϭ the deviance explained (D ² ) by the global model as the no predictive capacity (random or worse), 0.5 – 0.7 ϭ some measure of model fi t. predictive capacity (better than random), 0.7 – 0.9 ϭ We compared the predictive capacity of habitat associa- reasonable predictive capacity, and Ͼ 0.9 ϭ very good pre- tion models with equivalent models from Nimmo et al. dictive capacity. Cross validation was undertaken using (2012), which considered the occurrence of the same species source scripts in R ver. 2.10.1 (R Development Core Team). in relation to time-since-fi re (years), accounting for the For measuring between-region predictive capacity, we pro- possibility of diff erent response-curves between broad jected a model calibrated in one subregion onto the other vegetation associations. Th ese ‘ time-since-fi re ϩ vegetation subregion (i.e. north to south and vice versa), again using type models ’ were of the form: AUC to quantify the model’ s ability to discriminate

808 between presences and absences. Model evaluation was (AUC ϭ 0.76) was slightly greater than that for within- assessed by using the global model throughout. region models (AUC ϭ 0.75) (Fig. 3, 4), potentially owing to the larger amount of data on which projected models were built (i.e. 140 sites compared with 120, due to the removal Results of 20 sites during cross validation of within-region models).

Model selection and evaluation Comparison with fi re history models Models that included an interaction between time-since-fi re Habitat association models and vegetation associations (i.e. time-since-fi re ϩ vegetation Of the eight ‘ species by subregion’ combinations (i.e. north type models; Nimmo et al. 2012) performed similarly to and south of the Murray River for each of four species), habitat association models when cross-validated in the six combinations had more than one model with substantial Δ Ͻ region in which the model was calibrated (Fig. 3). Six of the support (i.e. AICc 2) (Table 1). All species except eight species by region combinations had reasonable C. pictus exhibited diff erences between subregions in terms predictive capacity, the same as the equivalent habitat asso- of the most highly ranked model. However, despite these ciation models. Models that included only time-since- diff erences, there was a relatively high degree of congruence Σ fi re performed less well, having reasonable predictive capacity in terms of important variables (as judged by w i ), between for only three of the eight species by region combinations. subregions (Supplementary material Appendix 3). Response Th e remaining models had poor predictive capacity. A curves of species’ occurrence in relation to continuous vari- greater disparity between model types, in terms of their ables which were identifi ed as the most important showed predictive capacity, was observed when models were pro- that the form of the relationship was generally similar across jected into novel space (i.e. between-region predictive the range of data (Fig. 2), indicating relatively consistent capacity). In comparison with six of the eight species-by- relationships with key habitat variables between subregions. region combinations producing reasonable predictions for Th e habitat association models varied in their ability to habitat association models; for both sets of fi re history discriminate between the presence and absence of species models (i.e. with and without an interaction term for for the subregion within which the model was calibrated vegetation type) only two and four models, respectively, had (Fig. 3). Overall, six models had reasonable discriminative reasonable predictions across subregions (Fig. 4), with capacity (AUC ϭ 0.7 – 0.9) and two had some (but poor) ϭ mean AUCs of 0.64 for both model sets (compared to 0.76 discriminative capacity (AUC 0.5 – 0.7) (Fig. 3). Th e for habitat association models). Habitat association models highest within-region predictive capacity was for ϭ also tended to provide better fi t than models based on time C. brachyonyx in the southern subregion (AUC 0.86), since fi re, with the diff erence particularly noticeable when while the lowest was for C. pictus in the northern subregion, compared to time-since-fi re only models (Table 2). for which the discriminative capacity was AUC ϭ 0.64. Mean within-region AUC was 0.75. Of the habitat association models projected between Discussion subregions (i.e. from one side of the Murray River to the other), six models had reasonable discriminative capacity, To date, most attempts to predict the response of reptiles to whereas the remaining two had some capacity to discrimi- fi re have been based on simple conceptual models that nate (Fig. 3). Th e mean between-region predictive capacity assume faunal responses to fi re directly track changes in key

Table 1. Habitat association models for four species of lizards for each of two sub-regions: north and south of the Murray River, respectively. Models are presented for which Δ AICc Ͻ 2.0. The AIC weight (wi) for each model is also given. ω Δ Species Subregion Model structure i i Ctenotus brachyonyx North Triodia 0.50 0.00 Ctenotus brachyonyx South Triodia ϩ Logs 0.36 0.00 Triodia ϩ Logs ϩ Shrubs 0.14 1.81 Triodia 0.14 1.89 Ctenophorus fordi North Triodia 0.47 0.00 Ctenophorus fordi South Triodia ϩ Shrubs ϩ Ground 0.38 0.00 Triodia ϩ Ground 0.23 1.01 Liopholis inornata North Soil 0.27 0.00 Soil ϩ Ground 0.13 1.43 Liopholis inornata South Ground ϩ Shrubs ϩ Logs ϩ Soil 0.22 0.00 Ground ϩ Logs 0.22 0.04 Ground ϩ Logs ϩ Soil 0.20 0.20 Ground ϩ Shrubs ϩ Logs 0.10 1.54 Ctenophorus pictus North Ground ϩ Soil 0.25 0.00 Ground 0.24 0.07 Ground ϩ Soil ϩ Logs 0.10 1.86 Ctenophorus pictus South Ground ϩ Soil 0.34 0.00 Ground ϩ Soil ϩ Triodia 0.22 0.86

809 C. fordi C. pictus 1.0 1.0

0.8 0.8

0.6 0.6

0.4 0.4

0.2 0.2

0.0 0.0 0 1020304050−1.79 −0.79 0.21 1.21 2.21 Triodia cover (%) Bare ground/ Ground cover PCA Leaf litter/ sparse canopy greater canopy

C. brachyonyx L. inornata 1.0 1.0 Probability of occurrence

0.8 0.8

0.6 0.6

0.4 0.4

0.2 0.2

0.0 0.0 0 1020304050−1.79 −0.79 0.21 1.21 2.21 Triodia cover (%) Bare ground/ Leaf litter/ sparse canopy Ground cover PCA greater canopy

Figure 2. Response curves from generalized additive mixed models (GAMMs) of changes in the probability of occurrence ( Ϯ 95% CI; shaded area) of lizard species in relation to the most important vegetation structural variables. Predictions are made holding all other continuous variables at their mean, and with the categorical predictor ‘ soil ’ speciﬁ ed as ‘ Loamy ’ (the most common soil type in the dataset). Dotted lines ϭ models developed south of the Murray River; solid lines ϭ models developed north of the Murray River.

habitat attributes. Such a priori predictions have performed poorly (Driscoll and Henderson 2008, Lindenmayer et al. 2008). Furthermore, regional variation in fauna– fi re relationships have questioned whether the mechanisms underlying species responses to fi re are consistent with the conceptual models on which such predictions are based; the habitat accommodation model (Smith et al. 2013). Our results support the dynamic vegetation hypothesis, that spatial variation in the relationship between fi re history and key habitat attributes causes regional variation in fauna – fi re relationships. Th is explains how the habitat accommodation model can, at the same time, be conceptually accurate and yet predictively weak. Figure 3. Th e comparative ability of the three model types (time-since-fi re only, time-since-fi re ϩ vegetation type, and habitat association models) to discriminate between presences and absences Consistency of fauna– habitat relationships for the sub-region in which the model was calibrated (blue boxes) and when projected into a novel sub-region (yellow boxes). Habitat association models produced reliable predictions Lines represent random (lower line) and reasonable (upper line) for six of the eight species-by-region combinations for both discrimination capacity. within and between-region discrimination, and all models

810 Figure 4. Relationship between the ability of species distribution models to discriminate between presences and absences for the subregion in which the model was calibrated (y-axis) and when projected into a novel sub-region (x-axis). Squares ϭ habitat association models (this study), triangles ϭ time-since-fi re ϩ vegetation type models (from Nimmo et al. 2012), circles ϭ time-since-fi re only models. Labelled points are examples of how models of the same species in the same region may vary: C. brachyonyx north of the Murray River; C. fordi south of the Murray River). had some capacity to discriminate between presences and interaction. For example, a suite of Triodia- dwelling species, absences. Th is suggests habitat associations across the study including species from the same genus (e.g. Ctenotus atlas) are region are relatively consistent. Species generally displayed a common in sites with high Triodia cover in the northern similar response to key habitat variables in terms of the direc- subregion, but do not occur in the southern subregion. Th e tion and form of the response. One exception was C. brachyonyx, presence of such species might alter habitat selection by which provided some support for the dynamic habitat C. brachyonyx. Despite the varying response of C. brachyonyx hypothesis, that species selected sites with diff erent vegetation to Triodia, only 8% (n ϭ 23) of sites had Triodia cover of attributes in diff erent regions. Th is species displayed a mono- Ͼ 30% (i.e. where the relationship becomes negative in the tonic relationship with Triodia cover in the southern subre- northern subregion), meaning that comparatively few predic- gion, but a quadratic relationship in the northern subregion tions would have been made to such sites and, consequently, (Fig. 2). Such varying relationships might be due to an altered habitat models for C. brachyonyx still provided good discrim- thermal environment across the study region or competitive inative capacity even when projected between subregions. Th e distribution of C. brachyonyx in the southern subre- Table 2. Model fi t (deviance explained, D 2 ) for three kinds of gion also presents an interesting case. Despite its strong rela- models of the occurrence of lizard species in the Murray Mallee 2 tionship with Triodia cover – a plant species known to be region: D is the proportion of deviance explained by the fi tted strongly aff ected by fi re (Haslem et al. 2011) – C. brachyonyx model relative to a null model. does not show a signifi cant response to fi re in this subregion Model type Time-since- Time-since- Habitat (Nimmo et al. 2012). Such seemingly counter-intuitive ϩ fi re only fi re veg association results have been shown for other Triodia dependent species models models models Species SubregionD 2 D 2 D 2 (Driscoll and Henderson 2008, Driscoll et al. 2012). Such relationships are likely to be a consequence of the highly C. fordi North 13 19 24 non-linear nature of post-fi re changes in Triodia cover, South 22 43 39 C. pictus North 7 8 12 together with non-linear relationships between fauna and South 24 25 18 Triodia , and the large proportion of unexplained variance in C. brachyonyx North 9 29 31 both relationships (Haslem et al. 2011). Th us, although South 2 19 43 any fi re event that changes the cover of Triodia is likely to E. inornata North 14 15 12 aff ect C. brachyonyx , the eff ects are diffi cult to generalise South 11 16 27 across multiple diff erent fi re events (Watson et al. 2012). Ϯ Ϯ Ϯ Ϯ Average SD 13 7 22 11 26 12 Th is underscores the complexity of post-fi re responses, it

811 refl ects the issues associated with modelling from indirect while other species respond to changes in food resources variables such as fi re (Guisan and Zimmerman 2000), and it driven by climatic events (Letnic and Dickman 2010). For implies that ‘ null ’ results should be treated with extreme these species, changes in vegetation structure post-fi re are caution in fauna – fi re studies (Smith et al. 2013). less likely to infl uence occurrence, and hence the application of predictive models based solely on vegetation structure would be inappropriate. Comparative performance of fi re history

Models based only on fi re history generally provided a poor Acknowledgements – We thank the following agencies for funding: fi t to the data and had limited discriminative capacity, both Parks Victoria, Dept of Sustainability and Environment (VIC), when applied within a subregion and when transferred Mallee Catchment Management Authority, NSW National Parks between subregions. Again, this is despite the knowledge and Wildlife Service, Dept of Environment and Climate Change (NSW), Lower Murray Darling Catchment Management that the occurrence of each of the study species was aff ected Authority, Dept for Environment and Natural Resources (SA), by habitat variables that are themselves infl uenced by Land and Water Australia, Natural Heritage Trust, Birds Australia fi re (Haslem et al. 2011). Overall, models developed (Gluepot Reserve), Australian Wildlife Conservancy (Scotia previously (Nimmo et al. 2012), which allowed a separate Sanctuary), and the Murray Mallee Partnership. Th anks to the time-since-fi re response curve for the two dominant vegeta- Mallee Fire and Biodiversity Project Team, especially Mike Clarke, tion types within the region, performed better in terms of and many volunteers who helped with data collection. both model fi t and predictive capacity. Th e diff erence between the three sets of models was particularly obvious for the two Triodia dwelling species References (C. brachyonyx and C. fordi ), as building extra complexity into the models resulted in an obvious shift towards them Avitabile, S. C. et al. 2013. Systematic fi re mapping is critical for being more transferable across subregions (Fig. 4). Models fi re ecology, planning and management: a case study in the semi-arid Murray Mallee, south-eastern Australia. – Landscape developed for C. brachyonyx in the north and C. fordi in Urban Plann. 117: 81 – 91. the south, based only on fi re history, provided near-random Burnham, K. P. and Anderson, D. R. 2002. Model selection discrimination when transferred into a novel subregion. Th e and multimodel inference: a practical-theoretic approach, 2 ed. inclusion of an interaction between fi re history and vegeta- – Springer. tion type improved the transferability of both models, Driscoll, D. A. and Henderson, M. K. 2008. How many common but were still unable to provide reasonable discrimination reptile species are fi re specialists? A replicated natural experiment (Fig. 4). By contrast, habitat association models were gener- highlights the predictive weakness of a fi re succession model. ally transferable both within and between regions. Th ese – Biol. Conserv. 141: 460 – 471. Driscoll, D. et al. 2012. Reptile responses to fi re and the risk of results reinforce the view that the eff ects of fi re on fauna can post-disturbance sampling bias. – Biodivers. Conserv. 21: be strongly contextual (Parr et al. 2004, Watson et al. 1607 – 1625. 2012), and building greater complexity to refl ect the Elith, J. and Leathwick, J. R. 2009. Species distribution models: ecological context of fi res will improve their predictive ecological explanation and prediction across space and capacity. It also highlights that when considered across a time. – Annu. Rev. Ecol. Evol. Syst. 40: 677 – 697. broad geographic area, even a species with predictable habi- Fox, B. J. 1982. Fire and mammalian secondary succession in tat associations can have unpredictable responses to fi re. an Australian coastal heath. – Ecology 63: 1332 – 1341. We note that both the vegetation association and fi re Fishe, I. and Chandler, R. 2011. Unmatched: an R package for fi tting hierarchical models of wildlife occurrence and abundance. history models explained only a fraction of the variation in – J. Stat. Softw. 43: 1 – 23. the data, ranging from just 2 to 43%. Th is means that Fox, B. J. et al. 2003. Experimental manipulation of habitat other factors that determine these species’ distributions structure: a retrogression of the small mammal succession. remain outside of the parameters fi tted in the current mod- – J. Anim. Ecol. 72: 927 – 940. els. Although it was not our purpose to develop the most Greenville, A. C. and Dickman, C. R. 2009. Factors aff ecting best-fi tting or predictively accurate model, additional vari- habitat selection in a specialist fossorial skink. – Biol. J. Linn. ables that would aid in predicting species distributions in Soc. 97: 531 – 544. fi re-prone regions include other aspects of the fi re regime, Guisan, A. and Zimmermann, N. E. 2000. Predictive habitat distribution models in ecology. – Ecol. Model. 135: such as the interval between successive fi res or the spatial 147 – 186. extent and intensity of fi res. In addition, species inter- Haslem, A. et al. 2010. A framework for mapping vegetation over actions, including both competition and predation, are broad spatial extents: a technique to aid land management likely to further aff ect where lizard species occur within across jurisdictional boundaries. – Landscape Urban Plann. the region (Nimmo et al. 2011), but are diffi cult to measure 97: 296 – 305. and model (Elith and Leathwick 2009). Haslem, A. et al. 2011. Habitat or fuel? Implications of long-term, Another important caveat to our fi ndings is that the post-fi re dynamics for the development of key resources habitat accommodation model is relevant only to species for fauna and fi re. – J. Appl. Ecol. 48: 247 – 256. Letnic, M. and Dickman, C. R. 2010. Resource pulses and strongly linked with components of vegetation structure. mammalian dynamics: conceptual models for hummock Other species respond to alternative processes with little grasslands and other Australian desert habitats. – Biol. Rev. regard for vegetation structure. For instance, some fossorial 85: 501 – 521. lizard species select habitat on the basis of soil characteristics, Lindenmayer, D. B. et al. 2008. How predictable are reptile not vegetation structure (Greenville and Dickman 2009), responses to wildfi re? – Oikos 117: 1086 – 1097.

812 Monamy, V. and Fox, B. J. 2000. Small mammal succession is Pearce, J. and Ferrier, S. 2000. Evaluating the predictive performance determined by vegetation density rather than time elapsed since of habitat models developed using logistic regression. – Ecol. disturbance. – Austral Ecol. 25: 580 – 587. Model. 33: 225 – 245. Monamy, V. and Fox, B. J. 2010. Responses of two species of Smith, A. L. et al. 2013. Successional specialization in a reptile heathland rodents to habitat manipulation: vegetation density community cautions against widespread planned burning thresholds and the habitat accommodation model. – Austral and complete fi re suppression. – J. Appl. Ecol. 50: Ecol. 35: 334 – 347. 1178 – 1186. Nimmo, D. G. et al. 2011. Th e decoupling of abundance and Staver, C. et al. 2009. Browsing and fi re interact to suppress species richness in lizard communities. – J. Anim. Ecol. tree density in an African savanna. – Ecol. Appl. 19: 80: 650 – 656. 1909 – 1919. Nimmo, D. G. et al. 2012. Predicting the century-long post-fi re Watson, S. J. et al. 2012. Eff ects of time since fi re on birds: how responses of reptiles. – Global Ecol. Biogeogr. 21: 1062 – 1073. informative are generalized fi re response curves for conservation Nimmo, D. G. et al. 2013. Fire mosaics and reptile conservation management? – Ecol. Appl. 22: 685 – 696. in a fi re-prone region. – Conserv. Biol. 27: 345 – 353. Wood, S. 2009. gamm4: generalized additive mixed models using Noble, I. R. and Slatyer, R. O. 1980. Th e use of vital attributes to mgcv and lme4. – Ͻ http://cran.r-project.org/web/packages/ predict successional changes in plant communities subject to gamm4/gamm4.pdfϾ . recurrent disturbances. – In: Maarel, E. (ed.), Succession. Wood, S. N. 2006. Generalized additive models: an introduction Springer, pp. 5 – 21. with R. – Chapman and Hall/CRC Press. Parr, C. L. et al. 2004. Response of African savanna ants to Zuur, A. F. et al. 2009. Mixed eff ects models and extensions in long-term fi re regimes. – J. Appl. Ecol. 41: 630 – 642. ecology with R. – Springer.

Supplementary material (Appendix ECOG-00684 at Ͻ www.oikosoﬃ ce.lu.se/appendix Ͼ ). Appendix 1 – 3.

813