The management elements examined in this study encompass common features of agricultural landscapes globally. However, our understanding of how they influence reptile assemblages is limited. New perspectives and methods are needed to tackle the global challenge of balancing biodiversity outcomes and agricultural production (Glamann et al . 2015 ; Tanentzap et al . 2015 ). Our research provides a key insight in understanding how appropriate management of the production landscape can improve habitat suitability for native biota.

Our study examined three management elements (remnant vegetation, paddock types and grazing regime) that provide contrasting conditions and resources likely to influence reptile abundance and richness in grazing landscapes (Prevedello & Vieira 2010 ; Driscoll et al . 2013 ) (see Table 1 ). Our research question was: How do remnant patches, paddock types and grazing regimes influence reptile assemblages in a grazing landscape? We developed predictions about each management element's influence on the reptile assemblage and stated our rationale for choosing each one (Table 1 ).

Several attributes of human‐modified landscapes have been suggested to influence native biota including landscape type (Kennedy et al . 2011 ; Pedro & Simonetti 2015 ), contrast between the human‐modified land cover and habitat patches (Prevedello & Vieira 2010 ), size and configuration of landscape elements (Templeton, Brazeal & Neuwald 2011 ; Russildi et al . 2016 ), and changes across time (Kupfer, Malanson & Franklin 2006 ; Driscoll et al . 2013 ). Human‐modified landscapes can have a major influence on movement, influencing dispersal between habitat patches (Kupfer, Malanson & Franklin 2006 ; Kay et al . 2016 ), mortality during dispersal (Ewers & Didham 2006 ), and tendency to depart patches and enter the matrix (Prevedello & Vieira 2010 ).

Agricultural landscapes often comprise patches of remnant vegetation surrounded by a matrix of other land use types. The matrix was once considered to be an inhospitable ‘sea’ between patches of habitat (Haila 2002 ). More recent studies define the matrix as the dominant (usually non‐native) land cover, in which other cover types are embedded, and in which species cannot form self‐sustaining populations (Driscoll et al . 2013 ). The matrix is species‐specific and context‐specific. For example, Blaum & Wichmann ( 2007 ) found that shrub‐dominated land cover acted as a matrix for the hairy‐footed gerbil Gerbillurus paeba during normal rainfall conditions, but became suitable habitat during exceptionally high rainfall events.

Human‐modified landscapes, including agricultural areas, cover the majority of the Earth's terrestrial land surface (Ellis & Ramankutty 2008 ). Some forms of landscape modification are causing a rapid decline in biodiversity (Barnosky et al . 2011 ; Venter et al . 2016 ). There is a need to understand how to best manage agricultural areas, to balance both human use and biodiversity conservation (Tilman et al . 2011 ).

For all GLMMs, we modelled the different reptile response variables using different error distributions to account for differences in mean/variance relationships (Table 2 ). Akaike's information criterion corrected (AICc) for small sample sizes was calculated using the ‘bbmle’ package (Bolker 2014 ). Models within two AICc of the lowest value were considered to be the best models. We calculated P ‐values using the ANOVA function in the ‘car’ package (Fox & Weisberg 2011 ) to identify significant components of the model. We used the general linear hypothesis method in the ‘multcomp’ package (Hothorn, Bretz & Westfall 2008 ) to determine the relative differences between the levels of the management element variables. These steps are necessary because AICc identifies the best model but does not differentiate among levels of a factor. Plots were drawn using the ‘ggplot’ package (Wickham 2009 ) (see Appendix S2 for r code).

We next used GLMMs to assess the influence of environmental variables, which we modelled: (i) alone, (ii) additive with the best management element (see below), and (iii) as an interaction with the best management element variables. A single environmental variable was modelled at a time. The environmental fixed effects were vegetation cover PCA1 and PCA2, proportion of woody vegetation within 3 km, remnant area, livestock animal and volume of in situ CWD. Correlations between environmental variables were 0·35 or less.

To test our predictions (Table 1 ), we used a suite of Generalised Linear Mixed Models (GLMMs) with the fixed effects based on if reptiles were captured in: (i) remnant or paddocks, (ii) the four paddock types (pasture, linear planting, CWD and fence), and (iii) sites subject to either rotational or continuous grazing. We modelled each fixed effect individually and their interactions, with transect nested in site fitted as random effects using the ‘glmmADMB’ package (Skaug et al . 2014 ). Models with just the paddock type variable analysed differences between the four transect types. We examined four aspects of the reptile assemblage (total abundance, total richness, and ‘rare’ species abundance and richness) and each of the common species individually (Table 2 ). ‘Rare’ species were those captured in ≤4 sites and for which there were <70 captures overall. These species could not be analysed separately, but ‘rare’ species, analysed together, can reveal different responses to the few common species (Schutz & Driscoll 2008 ).

We first analysed vegetation cover data using principal components analysis (PCA) with the ‘FactoMineR’ package (Husson et al . 2015 ) because the vegetation cover categories were correlated and we wanted to examine the main gradients of variation in vegetation cover.

We calculated area and proportion of woody vegetation around each remnant using Zonal statistics (ESRI 2013 ). We calculated the proportion of native woody vegetation in a 3 km radial circle around the mid‐point of each remnant using data of the extent of native woody vegetation in 2011 obtained through TERN Auscover ( http://www.auscover.org.au ) (Office of Environment and Heritage 2015 ). Vegetation cover at this scale was considered an indicator of isolation. Area was calculated by drawing polygons around each remnant with the edges determined visually using vegetation maps and satellite images.

We collected data on environmental variables expected to influence reptile occurrence. We measured percentage vegetation cover (grass, shrubs, forbs, ferns/rushes, bare ground, leaf litter, cryptogams, rock and native trees) and in situ CWD volume (>5 cm diameter) in a 10‐m diameter circle which was centred on the middle of each trapping array. We averaged these measurements across the three arrays in each of the remnant and paddock halves of the transect.

We surveyed every farm five times during the austral spring/summer between January 2014 and March 2015. Traps were open for 5 days for each survey, during which captured animals were marked and released. We performed a total of 25 200 trapping nights across all farms, surveys and traps. We pooled all captures across the five survey periods and three arrays in each of the remnant and paddock type halves of each transect, giving one sample from each half of the transect.

We surveyed reptiles using 160 m long transects extending from within a remnant into one of the four paddock types (Fig. 1 ). Six farms contained four transects (one for each paddock type). The other six contained three transects as they were missing the planting. Along each transect, trapping arrays were located at 20, 50 and 80 m in the remnant, and 20, 50 and 80 m in the adjoining paddock type (= three trapping arrays for each half of the transect). Each trapping array consisted of a 10‐m long drift fence running parallel to the remnant edge and perpendicular to the transect with two pitfall traps (15 L bucket) and two funnel traps (Terrestrial Ecosystems, Perth, Australia), one of each on each side of the drift fence. These trap types are complementary and result in the capture of a broader range of species than any single trap type alone (Greenberg, Neary & Harris 1994 ). We checked under the pieces of CWD for reptiles at approximately 12 and 15 months after installation and analysed these data separately to understand if there were temporal effects post‐installation.

Each farm (site) was subject to one of two grazing regimes by domestic livestock. Seven farms had a continuous grazing regime, and five had a rotational grazing regime. Continuous grazing regimes involve leaving livestock in the same paddock for extended periods. Under rotational grazing, livestock is moved between paddocks every few days and the livestock do not return to the same paddock for weeks or months. Rotational grazing regimes can result in increased natural tree regeneration (Fischer et al . 2009 ) although some studies have found no difference in vegetation structure between grazing regimes (e.g. Dorrough et al . 2012 ).

We selected 12 farms that contained remnant woodland patches that were directly adjacent to four different habitat types within a managed paddock (hereafter ‘paddock type’; Fig. 1 ). Remnant patches were between 0·7 and 400 ha (mean = 62·9 ha, SD = 101·6) (see Table S2 , Supporting Information), comprised open woodland, and were typically not grazed during spring and summer. Land‐uses adjacent to each patch formed our four paddock types: (i) open grazed pasture, (ii) fenced linear planting within pasture, (iii) grazed pasture with coarse woody debris (CWD) added, and (iv) fence within pasture (Fig. 1 , Table 1 ). These paddock types were chosen because they are common in grazing landscapes both within our region and globally (Table 1 ).

We conducted our study in the central and southern Tablelands of New South Wales, south‐eastern Australia (Fig. 1 ). This area contains remnants of critically endangered Box Gum Grassy Woodland (EPBC Act 1999 ). This ecological community is characterised by a heterogeneous cover of yellow box Eucalyptus melliodora , Blakely's red gum Eucalyptus blakelyi and white box Eucalyptus albens interspersed by native grasslands. The study area has been highly modified and cleared for agriculture in the 200 years since European arrival. The limited remaining woodland is highly fragmented, isolated and often degraded due to clearing and livestock grazing.

There was a higher probability of A. muricatus presence in sites grazed by sheep rather than cows ( P = 0·03) and this species was more commonly associated with larger remnants ( P = 0·021) (Fig. 4 d,e).

Other responses: (a) total reptile abundance was higher with greater tree and leaf litter cover, (b) Lampropholis guichenoti was more abundant with greater tree and leaf litter cover, (c) Morethia boulengeri was more abundant with greater tree and leaf litter cover, (d) Amphibolurus muricatus had greater presence in larger remnants, (e) Amphibolurus muricatus was present more in sites grazed by sheep rather than cows. Grey polygons indicate 95% confidence intervals and estimates are plotted on the original scale.

Carlia tetradactyla was 2·5 times more abundant in fence transects and 3·0 times more abundant in plantings transects compared to CWD transects ( P = 0·028 and P = 0·03) (Fig. 3 c, Table S4 ). Higher captures of this species were associated with cryptogams, rocks and shrubs ( P = 0·005) (Fig. 3 f).

Reptile responses that included paddock type: (a) rare species had highest abundance at fences and lowest in CWD, (b) greatest rare species richness in plantings and lowest in CWD, (c)abundance was influenced by paddock type, (d) rare species abundance increased with higher proportions of woody vegetation, (e) rare species richness increased with higher proportions of woody vegetation, (f)abundance was higher with greater shrubs, rock and cryptogam cover. Grey polygons and error bars indicate 95% confidence intervals and estimates are plotted on the original scale. CWD, coarse woody debris. [Colour figure can be viewed at wileyonlinelibrary.com

Rare species abundance and richness responded to paddock type plus the proportion of surrounding woody vegetation (Table S3 ). Rare species were more abundant in plantings and fences than in CWD transects by 2·4 and 2·3 times, respectively, ( P = 0·002) (Fig. 3 a, Table S4 ) and the CWD transect supported the lowest richness ( P = 0·002) (Fig. 3 b, Table S4 ). Rare species abundance and richness were positively associated with the proportion of surrounding woody vegetation (abundance P < 0·001, richness P = 0·017) (Fig. 3 d,e); there were 2·6 more rare species and 5·7 more counts of rare animals in sites with 50% compared to 5% woody cover.

Reptile responses that included the remnant/paddock variable: (a) total species richness was higher in remnants associated with cryptograms, shrubs and rocks and higher in paddocks associated with forbs, trees and litter, (b) there was greaterpresence in remnants than paddocks. Shaded polygons and error bars indicate 95% confidence intervals and estimates are plotted on the original scale. [Colour figure can be viewed at wileyonlinelibrary.com

Reptile species richness was influenced by an interaction between remnant/paddock and PCA2 ( P = 0·002). Higher richness in remnants was associated with cryptogams, shrubs and rocks, whereas higher richness in paddocks was associated with forbs, trees and litter (Fig. 2 a, Table S3 ). Lampropholis delicata was more commonly present in remnants than paddocks (Fig. 2 b, Table S4 ).

We identified two distinct gradients of variation among our sites. PCA1 (22·5% of variation) described a gradient from primarily grass to woodland‐like cover of native trees and leaf litter (Appendix S1 ). For PCA1, remnants had positive (i.e. woodland) values and the paddock types of pasture, CWD and fences mostly had negative (i.e. grassy) values (Appendix S1 ). Plantings, however, were differentiated from the other paddock types along PCA1 and generally contained more tree cover and litter or bare ground (Appendix S1 ). PCA2 (16·1% of the variation) encompassed a gradient of cover from cryptogams, shrubs and rocks to forbs, litter and tree cover and the paddock types did not differ strongly across this axis.

We made 1186 captures, comprising 28 reptile species (Table S1 ). Most captures were from the Family Scincidae (19 species). All species captured more than three times were captured both within remnants and in paddocks. Seven of the 12 species captured three or fewer times were never captured in paddocks and three were only captured in paddocks. Approximately 60% of all reptile captures were in remnants, and of the captures in the paddocks, 17% were in CWD, 23% were along fences, 15% were in pasture and 44% were in plantings (of the six sites that contained plantings). From a separate analysis of the number of reptiles under the CWD pieces, we found greater reptile counts at approximately 15 months compared to 12 months after the CWD installation ( P < 0·001) (Fig. S1 ).

Discussion

The influence of remnant patches and other vegetation characteristics As predicted (Table 1, point 1), there was generally higher overall reptile abundance and richness in remnants than paddocks. We found a positive influence of three vegetation characteristics on the abundance and richness of the reptile assemblage: (i) remnant woodland patches, (ii) proportion of woody vegetation within 3 km and, (iii) local vegetation characteristics of tree cover and leaf litter, and to a lesser extent, rocks, cryptogams, forbs and shrubs. We found tree cover and litter were positively associated with reptile abundance across the landscape and with species richness in paddocks. Tree presence and cover can affect reptile abundance and richness (Dorrough et al. 2012; Michael et al. 2015), and both leaf litter and rocks are important habitat features for many reptiles in this study (Michael et al. 2015). Vegetation structure and cover is likely to influence the microclimate and therefore influence reptile thermoregulation (e.g. Ackley et al. 2015). Isolation and area of remnants can be important factors driving species distribution (Andrén 1994; Prevedello & Vieira 2010). Fahrig (2013) posited that the total amount of habitat in an area, not patch size, is important for species richness. Supporting this, we found greater proportions of woody vegetation within 3 km resulted in increased rare species richness and abundance. Patch size was important for only one species in our study; probability of A. muricatus presence increased with larger remnants. There have been conflicting findings about the influence of patch size on populations in fragmented landscapes for a range of taxa including reptiles (e.g. Jellinek, Driscoll & Kirkpatrick 2004; Antongiovanni & Metzger 2005; Pardini et al. 2005). These conflicts relate to the extent of patch dependence and the influence of the surrounding matrix on patches (Ewers & Didham 2006; Prevedello & Vieira 2010). We used the same sample effort regardless of patch size, and found local abundance and alpha diversity did not vary with patch size. However, if beta diversity was high within patches, then overall richness may be higher in larger patches. The reptile responses to vegetation quantified in this study highlights the importance of retaining native vegetation of all patch sizes in modified grazing landscapes, and the negative consequences clearing and habitat loss have on native biota (Andrén 1994; Bonte et al. 2012; Baguette et al. 2013). These results highlight the deleterious effects conventional intensification processes have on reptile populations due to changes to vegetation structure and extent, which has also been seen in birds (Green et al. 2005).

The influence of paddock type A key finding was the positive response of rare species and C. tetradactyla to linear plantings and fences. These findings are congruent with our predictions that plantings and fences would result in increased reptile abundance and richness (Table 1, point 2). This is consistent with previous studies showing that linear plantings have positive impacts on native biota, including reptiles in agricultural landscapes, but are generally not a replacement for remnant vegetation (Kavanagh et al. 2005; Cunningham et al. 2007; Munro, Lindenmayer & Fischer 2007). As a vast interconnected network within our study system, fences have the potential to be conduits for movement of some reptiles. While limited research has been conducted on the impacts of fences on reptile movement, most studies on a range of taxa, including reptiles, amphibians and mammals, have found fences to be barriers (e.g. Lasky 2011). One study found turtles in particular, and other reptiles to a lesser extent, were negatively impacted by a predator‐proof fence which was less permeable than the fences examined in this study (Ferronato, Roe & Georges 2014). However, networks of hedges can have positive impacts on reptile diversity in agricultural landscapes (e.g. Nopper et al. 2017) and our results suggest fences have a positive impact on some small reptiles. This is an area of landscape ecology that warrants further research. The CWD did not result in greater reptile captures in the traps compared to grazed pasture. This was contrary to our prediction (Table 1, point 2c) with low capture rates likely due to the short time between timber installation and surveys. There was an increase in reptiles under the timber after 15 months compared to 12 months after installation (Fig. S2). This suggests CWD addition has the potential to increase habitat suitability of grazing landscapes by reptiles over the longer term. The shorter term impact was limited and even resulted in lower capture rates of rare reptiles. It is not possible to determine if the lower capture rates of rare reptiles in CWD is due to actual lower presence or reduced movement due to increased shelter. Other studies have found the influence of CWD on reptile abundance is affected by timber size and type, how long it is in place, vegetation structure and the level of grazing in the surrounding area (Michael, Lunt & Robinson 2004; Manning, Cunningham & Lindenmayer 2013).

The influence of grazing regime Although livestock grazing has many ecological impacts and can strongly influence reptile populations (Fleischner 1994; Driscoll 2004), that is not always the case (Dorrough et al. 2012). We found the grazing regime did not result in significant differences in the reptile assemblage, possibly because there was substantial variation in grazing intensity and historical grazing practices among our sites. This may have obscured differences between our two grazing regimes.