1Simultaneous effect of habitat remnancy, exotic species and anthropogenic disturbance on
2orchid diversity and abundance
3
4Martín-Forés, I.1,2, Bywaters, S. L. 1,2, Sparrow, B. 1,2 & Guerin, G. R. 1,2
5
61 School of Biological Sciences, The University of Adelaide, Adelaide, South Australia 5005,
7Australia
82 Terrestrial Ecosystem Research Network (TERN), University of Adelaide, Adelaide, South
9Australia 5005, Australia
10*corresponding author: [email protected]
11
12
13
14
1 15ABSTRACT
16Orchids are potentially useful as ecological indicators because of their sensitivity to habitat
17fragmentation and anthropogenic disturbance. While many studies explore the effect of single
18factors on orchid diversity, few investigate how the extent, configuration and condition of
19surrounding habitat affect whole orchid communities. Here, we unravel the effect of biological
20invasions, anthropogenic disturbance (i.e. grazing pressure, ecological condition), habitat
21fragmentation and climate on an Australian orchid community. We sampled 39 plots across
22nine sites in the Mount Lofty Ranges, Australia. We recorded the number of orchid species and
23number of individuals per species in mid-winter, early-spring and late-spring to account for the
24effect of season on species visibility, with 115 surveys in total. We ranked grazing intensity and
25ecological condition, and estimated cover of exotic species. We analysed the response of
26richness and diversity through generalised linear mixed models, and differences in species
27composition through non-metric multidimensional scaling. We also explored fruiting success in
28two species associated with floral resources in the surrounding habitat. Habitat configuration
29in the surrounding landscape had different effects at increasing radii. Patch-level orchid
30diversity was positively correlated with habitat edges in the immediate area, and with habitat
31cohesion at medium scales, whereas diversity was negatively correlated with increasing
32habitat area across larger surrounding areas. Orchids co-existed with exotic species but were
33negatively affected once exotic cover exceeded 20%. Species composition was correlated with
34both exotic cover and level of disturbance. Fruiting success was unrelated to floral resources of
35the surrounding vegetation, although associated with certain bee-attracting species. Our
36findings reveal a complex relationship between orchid communities and their surrounding
37environments suggesting that while orchids benefit from a somewhat disturbed landscape,
38they fail to thrive once exotic cover exceeds 20%. These idiosyncratic responses suggest orchid
39diversity may be unreliable as early-warning indicators of habitat disturbance.
40
2 41INTRODUCTION
42Orchid species constitute the largest family of plants including over 700 genera and 27,000
43species (Chase et al. 2015; Govaerts 2016) occurring globally, except for the poles and
44extremely dry deserts (Jones 2006; Tsiftsis 2020). Due to the complexity of their symbiotic
45interactions with other species (e.g. dependence on specific associations with mycorrhizal
46fungi and pollinators; Hutchings 2010) and their narrow niche breadth and high levels of
47geographic endemism (Dixon et al. 2007; Orejuela-Gartner 2012), the consequences of global
48change appear to be particularly severe for orchid communities (Gale et al. 2018).
49 Indeed, orchid species are particularly sensitive to climate change (Seaton et al. 2010;
50Zhang et al. 2015), exotic species invasion (Chupp et al. 2015), anthropogenic disturbances
51(e.g. grazing pressure, fire regimes and illegal collection; Burgman et al. 2007; Roberts & Dixon,
522008, Newman et al. 2013), and land-use change (e.g. habitat fragmentation and land
53clearance; Kull & Hutchings 2006; Vogt-Schilb et al. 2016). Their sensitivity and reliance on
54specific interactions position orchids to be considered an early indicators of habitat
55disturbance and ecosystem degradation (Rose 1999).
56 The increase in mean annual temperature due to climate change can alter flowering
57times (Gallagher et al. 2009) and thereby decouple the phenological synchronicity of
58pollinator–orchid species and emergence times of their pollinators, leading to reduced
59reproductive success (Hutchings et al. 2018; Hoffmann et al. 2019). The effect of climate
60change on increasing the intensity of drought periods and wildfires may further restrict the
61suitable habitats available for orchids (Wraith & Pickering 2018). Most orchid species require
62specific temperature conditions, displaying narrow niche breadth; thus, extremes decrease
63orchid performance (Zhang et al. 2015; Djordjević & Tsiftsis 2020). Likewise, increased drought
64periods can produce variations in soil conditions including nutrient availability, moisture
65content and microorganism composition and as a result have an enormous impact on orchids
66in certain regions (Rasmussen et al. 2015; Djordjević & Tsiftsis 2020).
3 67 Biological invasions can heavily alter ecological interactions (Pyšek et al. 2012).
68Invasive plant species can trigger competition for light and space and act as pollination
69generalists (Bartomeus et al. 2008), potentially dominating the diet of pollinators (Morales &
70Traveset 2009; Pyšek et al. 2011) and modifying dynamics in plant-pollinator networks (Vila et
71al. 2009; Vanbergen et al. 2017).
72 Despite these threats, land-use change, including land clearing and habitat
73fragmentation are recognised as the main threats to orchids diversity, having contributed to
74significant population declines in several parts of the world (Wraith & Pickering, 2019).
75Configuration of the surrounding landscape pattern (e.g. vegetation heterogeneity, area of
76suitable habitat, edge length, and degree of cohesion) is likely to influence species
77assemblages (Fischer & Lindenmayer 2007). The species-area relationship, hypothesizing that
78the greater the size of the habitat, the greater the biodiversity, has proved true in several
79orchid studies (e.g. Schödelbauerová et al. 2009; Traxmandlová et al. 2018). As such, many
80orchid species are at a higher risk because they are confined to habitats that have been
81fragmented or undergone loss of area (Brummitt et al. 2015). Moreover, fragmented patches
82are more likely to have poor ecological condition and increased levels of disturbance (McIntyre
83& Hobbs, 1999). Habitat fragmentation, together with increasing ecological disturbance can
84lead to the interruption of interspecific interactions such as the ones established by orchids
85(Faast 2010; Parra-Tabla et al. 2011), pollination failure and decreased reproductive success
86(Aguilar et al. 2006; Newman et al. 2013). Habitat fragmentation indirectly enhances grazing
87intensity as it affects the proportion of edges around patches and accessibility for herbivores
88(Faast & Facelli 2009). Additionally, proximity to roads and urban nuclei enhances the risk of
89orchids being collected and harvested illegally further threatening the conservation of orchid
90diversity in remnant habitats.
91 Orchid reproduction mainly depends on pollination success, normally by insects,
92although there are some orchid species that can undergo auto-pollination or asexual
4 93propagation by forming clonal populations (Roberts & Dixon 2008). Orchid pollination is often
94one-sided and specialized, with the orchid relying more on its pollinator than the pollinator on
95the orchid (Roberts & Dixon 2008). Orchid pollination can be based on nectar rewards or
96partial rewards (Shrestha et al. 2020a), although one-third of orchid species have developed
97deceit strategies, including food and sexual deception, in which orchids mimic a reward
98(mimicry of floral features and production of a copy of the insect pheromone or even
99resembling insect forms, respectively; Jersáková et al. 2006; Roberts & Dixon 2008). Diuris is an
100example of orchid species pollinated by a presumed deceptive mechanism, whereas Glossodia
101has a more generalist pollination strategy (Beardsell et al. 1986; Bates & Weber 1990; Faast
1022010). Both are reported to produce nectar (Shrestha et al. 2020b). Pollination success may be
103impacted by habitat disturbance, with pollinator activity typically enhanced by greater density
104and diversity of floral resources in the surrounding vegetation (Johnson et al. 2003; Faast
1052010).
106 The rapid decline of orchid communities suggests urgent conservation measures are
107required. From the orchid species that have already been assessed for the IUCN Global Red List
108to date, more than half have been included under some form of threatened or near-
109threatened category (IUCN, 2020; Wraith & Pickering 2018). Thus, from a conservation
110perspective, orchids constitute a key but complicated group (Reiter et al. 2016; Swarts & Dixon
1112017), for which process-based conservation actions or those preserving the habitat might be
112more appropriate than species-focused ones (Fay 2018). Therefore, to design successful
113conservation strategies for this plant family, we need to fully understand orchid dynamics by
114studying simultaneously multiple variables affecting their survival (Fay 2018).
115 Australian orchids provide a sound model for understanding the simultaneous effect of
116different threats on orchids diversity. Australia is a large and megadiverse continent that
117contains a high diversity of endemic orchid species (approximately 1800 species, 95% endemic;
118Backhouse 2007; Wraith & Pickering 2019). Orchids are over-represented among Australia's
5 119threatened species, making up 17% of nationally threatened plants (Faast & Facelli 2007). The
120susceptibility of orchids to environmental change is believed to be due to a complex set of
121threatening processes, including small population sizes, habitat modification and
122fragmentation, invasive species, and grazing and disruption of pollination interactions
123(Quarmby 2010; Wraith & Pickering 2019).
124 The aim of this study was to unravel the effect of the above factors on orchid
125community diversity in South Australia. The factors included: i) climatic and edaphic gradients,
126ii) habitat configuration in the surrounding landscape (including vegetation remnancy at
127increasing radii; adapting the method suggested by Parkes et al. 2003), and iii) local
128disturbance, including grazing intensity, ecological condition, and exotic species cover. We also
129explored how iv) species composition of surrounding floral resources influenced reproductive
130success (i.e. fruit set). Regarding habitat configuration in the surrounding landscape, we
131hypothesized that greater habitat remnancy and connectivity of patches would correlate with
132higher orchid richness, abundance and diversity. Regarding local patch condition, we
133hypothesized that orchid diversity would be higher in plots with lower levels of disturbance,
134i.e. where ecological condition is good, and there is low intensity of grazing and low cover of
135exotic species. Finally, regarding fruiting success rate, we expected floral resources (abundance
136and diversity) to be positively related to the number of bees’ visits and therefore with orchid
137fruit set (Beardsell et al. 1986). We further hypothesized that fruiting in the species Diuris
138pardina (Lindl.) would be enhanced by the presence of the legume Pultenaea largiflorens
139(F.Muell.) due to putative mechanisms of floral mimicry to resemble the orange native pea
140flowers (Beardsell et al. 1986).
141
142METHODS
143Study area
6 144The study was conducted in the Mount Lofty Ranges, South Australia, which are a biodiversity
145hotspot and climatic refugium at the continental scale due to their heterogeneity of climatic
146and topographic conditions (Crisp et al. 2001; Byrne 2008; Guerin & Lowe 2012). The area has
147a Mediterranean-climate. Mean annual precipitation ranges from 460 to 990 mm, whereas
148minimum and maximum annual mean temperatures range from 7.8 to 10.3 °C and from 18.1
149to 21.7 °C, respectively (Harwood et al. 2016). The Mount Lofty Ranges, like other
150Mediterranean-climate ecosystems around the world, have been subjected to intensive
151disturbance, including land clearing for agrarian purposes, habitat fragmentation, species
152introductions, and regular wildfire regimes (Westphal et al. 2007; Martín-Forés 2017; Martín-
153Forés et al. 2017; Casado et al. 2018).
154
155Data collection
156We selected nine sites from existing monitoring networks (Guerin et al. 2014; Bond et al. 2019)
157corresponding to remnant vegetation patches. Sampling took place between winter 2019 and
158spring 2020. Depending on the size of the patch, between three and five 30x30 m plots were
159established at each site, with a total of 39 plots collected. Plots comprised a gradient of land-
160use from heavily disturbed to relatively undisturbed areas. We surveyed each plot three times,
161in mid-winter, early-spring and late-spring, to ensure the most complete composition sampling
162possible across the main orchid flowering season, during which many species flower for short,
163specific periods. Due to logistic constraints, six plots were only visited twice and three were
164only visited once; thus, a total of 115 surveys comprised the final dataset.
165 In each plot, pairs of observers searched for orchids along 5m-wide panels. We
166recorded and photo-vouchered each orchid species and counted the number of individuals per
167species per plot. Records were uploaded to the WOW iNaturalist project for confirmation of
168species identifications. We differentiated clonal and non-clonal species to analyse potential
169differences in their abundance and response. For each plot, we ranked grazing intensity from 0
7 170to 3, we scored ecological condition based on Keighery Condition Scale (Keighery, 1994;
171Supplemental table S1) from 0 (completely degraded) to 1 (very good), and we estimated the
172percentage of cover occupied by exotic species.
173 To explore the effect of floral resources on fruit set, we first determined the fruiting
174success of two common orchid species and related the proportion of recorded flowers that
175developed into fruits, to both the density of flowers and the abundance and diversity of floral
176resources in the plot. For this, we systematically surveyed 18 30x30 m plots at Spring Gully
177Conservation Park. In early-mid September 2020, during the peak flowering period, where we
178counted the number of open flowers of the orchid species Diuris pardina and Glossodia major
179(R.Br.) (counting ~3,000 individuals; Fig. 4). To test the idea that fruiting success relates to the
180floral resources provided to pollinators in the surrounding vegetation at the time of flowering,
181we set a floral transect up in each plot in order to quantify the diversity and species
182composition of other floral resources available to insect pollinators. The transect consisted of a
1831.8 m wide strip along the western edge of the plot (54m2); we recorded number of individuals
184and the number of open flowers was scored for all surrounding vegetation except for non-
185insect pollinated species such as grasses and sedges. Flowers were either counted or, where
186necessary, estimated by counting the flowers on branchlets and multiplying by number of
187branchlets. We returned to the same plots in late October to count the proportion of orchid
188flowers that had set fruit.
189 We combined observations of orchid community composition, including richness,
190abundance and diversity metrics with spatial data and climatic and edaphic variables. We
191calculated ‘fragstats’ from spatial data with the function ClassStat from the SDMTools R
192package (VanDerWal et al. 2015) and the package raster (Hijmans 2020). We calculated habitat
193configuration (Jaeger 2020), and assessed the effect of the area of the remnant native
194vegetation, total edge length, perimeter/area ratio and habitat cohesion on orchids at
195increasing spatial scales (i.e. increasing radii around the plots; Hirao et al. 2008). We extracted
8 196data for 25 climatic variables from Harwood et al. (2016) and for twelve edaphic variables from
197the CSIRO Data Access Portal (https://data.csiro.au/, accessed in May 2020; Viscarra Rossel et
198al. 2014) (Supplementary material S2 and S3).
199
200Data analyses
201We conducted Principal Component Analyses for both climatic and edaphic variables and
202extracted the first axes (hereafter PC1_climate and PC1_edaphic), which explained 71.1% and
20350% of the variance, respectively. We used this approach because we were interested in
204overall environmental effects rather than focusing on the effect of individual bioclimatic
205predictors. The first climatic axis, PC1_climate, was positively correlated with precipitation,
206and negatively correlated with temperature.
207 We tested for the effect of season, and climatic and edaphic conditions in orchid
208richness, abundance and Shannon diversity index. We included season (i.e. the time during the
209main orchid flowering season in which the survey took place: winter, early spring and late
210spring), PC1_climate and PC1_edaphic as fixed factors. We also included the interaction
211between PC1_climate and season, as it is known that different climatic conditions affect orchid
212performance more when they occur in late spring.
213 We tested for differences in orchid species richness, abundance and Shannon diversity
214index associated with the effect of habitat configuration in the surrounding landscape. For
215orchid species richness and abundance, we conducted a suite of Generalized Linear Mixed
216Models (GLMM) fitted to a Poisson error distribution whereas for Shannon diversity index we
217used Linear Mixed Effects Models (LMM) fitted to a gaussian error distribution. To check for
218those effects at patch scale, we included season, patch area and patch length as fixed effects.
219To check for those effects at landscape scale, we conducted comparisons of sets of mixed-
220effects models at four different landscape scales, including circular areas around the plot of
221one, two, five, and ten kilometres radii. Fixed effects included season, area of the remnant
9 222vegetation, edge length, perimeter/area (ratio) and habitat cohesion (VanDerWal et al. 2015).
223We included plot nested within site as a random effect to account for potential spatial
224autocorrelation. To test for the effect of disturbance in species richness, abundance and
225Shannon diversity index, we conducted the same suite of models but including season, grazing,
226ecological condition and cover of exotic species as fixed effects. We included plot nested
227within site as random effects.
228 Kruskal-Wallis tests were performed to look for differences among the three survey
229times. In every case we extracted the best subset of models based on ΔAICc ≤ 2. Afterwards,
230we calculated marginal (i.e. the proportion of variance explained by fixed effects) and
231conditional (i.e. the proportion of variance explained by fixed and random effects) R2 for the
232LMMs (i.e. Shannon diversity models) with the ‘MuMIn’ R package (Barton 2018). We
233estimated R2 for the species richness and abundace GLMMs with the delta method from a
234model with a Poisson distribution also with the ‘MuMIn’ R package. For the best subsets of
235models, we computed ANOVA-like tables testing the inclusion of random-effect terms in the
236models; we used the function ranova from the ‘lmerTest’ R package for the LMEMs. For the
237GLMEMs, we compared the models with and without the inclusion on the random effect, with
238and without nesting the effect of plot within site, and with only one (either site or plot) as
239random effect with an anova function.
240 We explored orchid species composition as a response to disturbance (including
241ecological condition, grazing intensity and percentage of cover of exotic species) by conducting
242non-metric multidimensional scaling. We used as input a matrix of species abundances per
243survey. We calculated Bray-Curtis dissimilarities among each sampling event (i.e. plot and
244survey) with the vegdist function of the vegan package (Oksanen et al. 2019). We explored the
245ideal number of dimensions to consider with the NMDS.scree function from vegan and
246afterwards set the number of dimensions to three (displaying stress <0.1).
10 247 We conducted NMDS with the metaMDS function setting the maximum random starts
248to 100. We fitted environmental factors (i.e. ecological condition, grazing intensity, cover of
249exotic species) as correlation vectors onto the obtained ordination with 999 permutations to
250test significance of associations. We obtained the projections of points onto vectors that had
251maximum correlation with corresponding environmental variables, the squared correlation
252coefficient of these vectors with the obtained ordination and the associated p-values.
253 To determine the fruiting success, we calculated the proportion of recorded flowers
254that develop into fruit. We related the fruiting success of D. pardina and G. major to the
255species richness and Shannon diversity of floral resources and the abundance of flowers in the
256plot through Pearson correlations. We explored surrounding floral resources affecting fruiting
257success by conducting pairwise correlations for D. pardina and G. major with the abundances
258of each of the recorded species. For those species that we observed greater values of
259correlation coefficients (i.e. r > 0.35), we conducted linear models between D. pardina and G.
260major fruiting success rates and the species abundance to check for the significance of the
261effect of that particular flowering species on enhanced pollination.
262 All statistical analysis and calculations were performed using R (R Core Team 2020)
263employing the packages vegan (Oksanen et al. 2019), SDMTools (VanDerWal et al. 2015),
264corrplot (Wei & Simko, 2017), Hmisc (Harrel Jr 2020), xtable (Dhal et al. 2019), ggplot2
265(Wickham 2016), lme4 (Bates et al. 2015), AICcmodavg (Mazerolle 2019), lmerTest (Kuznetsova
2662017) and MuMIn (Barton 2019).
267
268RESULTS
269Effect of environmental conditions
270We recorded a total of 68 orchid species. The best models for both orchid richness and
271abundance included season, PC1_climate and the interaction between them as fixed effects
272(i.e. Richness ~ Season*PC1_climate + (1|Site/Plot) and Abundance ~ Season*PC1_climate +
11 273(1|Site/Plot)). Regarding orchid Shannon diversity index, among the best subset of models, the
274most parsimonious one only included the effect of season (See supplementary materials S4 &
275S5 for details of the best subsets of models, and tables with the estimates, statistics and
276significance for the fixed effects of the best models, respectively). The effect of season is
277discussed in Supplementary material S6. The inclusion of the random effects significantly
278improved the model (see details in Supplementary material S7).
279
280Effect of spatial configuration
281The effect of habitat configuration in the surrounding landscape differed among spatial scales.
282The best models of the effect of habitat configuration in an area of 1 km radius richness
283included season and edge, with edge having a significant effect on species richness and
284abundance, whereas for orchid Shannon diversity, the best model only included season. The
285best models for habitat configuration in an area of 2 km radius always included season and a
286positive and significant effect of habitat cohesion. At 5 km, we obtained a similar result for
287Shannon diversity, whereas the best models for richness and abundance included also a
288negative effect of the total area of the habitat. However, at greater spatial scale (i.e. 10 km
289radius) the best models for the diversity metrics included season and total habitat area, with
290the last one displaying a significantly negative effect on species richness and Shannon diversity
291index. Only season was included in the best model. (Fig 1; Supplementary materials S4, S5 &
292S7).
293
294Effect of ecological disturbance
295The best model for orchid species richness and Shannon index with regards to the effect of
296ecological disturbance included season and exotic species; with the effect of exotic species in
297both cases being significantly negative (Supplementary materials S4 & S5). The best and most
298parsimonious models for orchid abundance included either the effect of season and exotic
12 299species, or the effect of season and ecological condition. Exotic species had a negative impact
300on orchid diversity in any case, whereas ecological condition had a positive one
301(Supplementary materials S4, S5 & S7). However, although the effect of exotic species cover
302was negative overall, the most diverse orchid communities coexisted with intermediate levels
303of exotic cover (i.e. 15-20%; Fig. 2); when the cover of exotic species surpassed 25%, the
304impact on orchids became stronger with highly invaded sites displaying few or no orchids (Fig.
3052). Similarly, sites with very low values of ecological condition (i.e. ranked condition < 0.4) did
306not harbour any orchids (Fig. 2).
307
308Floristic differences among sites and among surveys
309NMDS showed that orchid species composition differed among sites but also within them.
310Within some sites, such as LIT and to a lesser extent HOR, there was a lot of variability in
311orchid species composition among plots, whereas plots within some other sites such as BLA or
312MAC, were quite similar (Fig. 3). Additional overlap in species composition was associated with
313season (Supplementary material S6).
314 Both condition and exotic species were significantly correlated to the NMDS ordination
315configuration (Condition: NMDS1 = -0.97, NMDS2 = -0.25, R2 = 0.10, p-value < 0.01; Exotic
316species: NMDS1 = 0.43, NMDS2 = 0.90, R2 = 0.08, p-value < 0.05), whereas grazing intensity
317was not (Condition: NMDS1 = 0.34, NMDS2 = 0.94, R2 = 0.02, p-value > 0.1).
318
319Fruiting success
320Fruit set in Diuris pardina and Glossodia major was low (0–18%) and not obviously related to
321either the density of flowering orchids, nor the amount or composition of floral resources in
322the surrounding habitat. Glossodia major and D. pardina exceeded 5% of fruit success only in 5
323and 2 plots, respectively (Fig. 4). Fruiting success for D. pardina was especially low, with eleven
324plots showing flowers but not fruits (Fig. 4; Supplementary material S9). The success rates of
13 3250–18% appeared random with respect to surrounding floral resources at time of flowering.
326Diuris pardina fruiting success was correlated with its flower density (adjusted r-square = 0.39,
327p-value < 0.01), but there was no apparent trend between flowering and fruiting G. major
328individuals (adjusted r-square = -0.05, p-value = 0.71).
329 Contrarily to our predictions, fruiting success for D. pardina was not significantly
330correlated with the abundance of Pultenaea largiflorens (adjusted r-square = 0.02, p-value =
3310.28), although it appeared positively correlated with the abundance of Lavandula stoechas
332(adjusted r-square = 0.23, p-value < 0.05). Fruiting success for G. major was significantly
333correlated to Wurmbea dioica flowers (adjusted r-square = 0.24, p-value < 0.05), and
334marginally to Drosera auriculata (adjusted r-square = 0.14, p-value < 0.1).
335 In addition, fruiting success for both species was neither significantly correlated to
336overall floral resources (i.e. number of flowers from all the species present in the plot), nor to
337overall floral diversity in terms of both, species richness and Shannon diversity.
338
339DISCUSSION
340Contrary to our expectations, the area of remnant vegetation in the surrounding landscape did
341not have a positive effect on orchid diversity. The effect of habitat configuration in the
342surrounding landscape on orchid diversity varied with spatial scale. Orchid diversity was
343positively correlated with the total edge of the habitat in surrounding areas of 1 km radius,
344while when considering medium-sized surrounding areas (2-5 km radii) orchids were favoured
345by habitat cohesion. At larger surrounding areas (5-10 km radii), we observed a negative effect
346of the area of the remnant vegetation on the patch-level diversity. As expected, high
347disturbance negatively affected orchid diversity, with exotic weed cover > 20% being the main
348factor impacting orchid diversity. However, orchid communities appeared to be most diverse
349in patches that had some level of disturbance. Finally, fruiting success appeared to be low and
350unrelated to surrounding floral resources for the orchid species studied, although it was
14 351associated with the presence of certain species in the surrounding vegetation that are known
352to attract bees, and for D. pardina was positively correlated with number (density) of flowering
353plants.
354
355Effect of environmental conditions
356The effect of climate varied among the three biodiversity metrics. Environmental conditions
357differentially affected orchids richness and abundance among seasons. For example, it is
358known that low temperatures typical from winter in Mediterranean-type ecosystems can
359inhibit orchid seeds germination and flowering (Acharya et al. 2011; Djordjevic & Tsiftsis 2020).
360Likewise, pollinators associated with orchid reproduction may be inactive during periods of
361cold winter temperatures (Zhang et al. 2015). Moreover, increased precipitation in winter and
362higher temperatures in late-spring can be associated with the ability of seeds to germinate and
363to increased photosynthetic efficiency and flower production, respectively (Djordjević &
364Tsiftsis, 2020). Regarding our results for orchid abundance, it is necessary to keep in mind that
365clonal orchid species might display asexual reproduction triggered under certain climatic
366conditions.
367
368Effect of spatial configuration
369The effect of habitat configuration in the surrounding landscape at increasing radii, varied
370associated with the spatial scale. At local scale (i.e. 1 km radius), we found a significant and
371positive effect of edge on orchid species and abundance. This can be related to the small
372spatial scale of the surrounding area considered, and to the fact that within a forest matrix, the
373edges can sustain equal or even more orchid species than the forest core (Parra Sánchez et al.
3742016). Edges are in general related to ecotone habitats, with considerably lower levels of
375competition for edaphic and light resources (i.e. low abundance of dominant plant species and
376low cover of trees and shrubs); these open niches can be colonized by pioneer species with
15 377weak competitive ability or high light requirements such as orchids (Arditti & Ghani, 2000;
378Fekete et al. 2020). In this sense, the surveyed areas are close to urban nuclei, showing
379anthropogenic habitats that provide refuge for orchid species, such as cemeteries, orchards
380and roadsides; thus, the edges can provide refugial conditions (Fekete et al. 2017) and act like
381ecological corridors by facilitating pollination and seed dispersal between suitable habitats on
382therefore spread of the orchid taxa (Van Rossum & Triest, 2012).
383 The positive effect of habitat cohesion at intermediate spatial scales (i.e. surrounding
384areas of radii 2 km and 5 km) is supported by previous findings. Greater habitat cohesion
385implies reduced fragmentation and consequently associated disturbance. It is well known that
386the dynamics of pollinator bees, and therefore the orchid-pollinator relationship, as well as the
387fruit set are positively associated with both the density and the condition of the surrounding
388vegetation matrix (Parra-Tabla et al. 2011; Akhalkatsi et al. 2014).
389 At larger spatial scales (i.e. surrounding landscape of 5 km and 10 km radii), we found
390an a priori unexpected negative effect of the total area of remnant vegetation on the diversity
391of orchid communities at patch-scale. It has been previously stated that peripheral orchid
392populations can perform better than populations living at the optimal conditions, despite being
393under more stressful conditions (García et al. 2010). In this sense, larger areas of remnant
394habitat could diminish orchids’ potential to find edges or semi-disturbed conditions in which
395they might perform better. In addition, it has been observed that habitat fragments of modest
396size can be ideal to maintain functional pollinator populations (Cane 2001). Considering the
397spatial scale pollinators can cover, it makes sense that habitat fragmentation negatively
398impacts orchids at smaller spatial scales while a trend that would seem opposite prevails at
399larger spatial scales.
400
401Effect of ecological disturbance
16 402High cover of exotic species had a negative effect on all orchid biodiversity metrics, whereas
403low cover seemed to not negatively affect orchid communities (Fig. 2). In addition, cover of
404exotic plant species had a significant effect on the orchid community composition, most likely
405related to the ability of the different orchid species to compete for resources (e.g. light, water,
406pollinators) with generalist exotic plants. Exotic plant species pose a threat to 65% of the
407orchid species present in Australia (Nevill 2010; Wraith & Pickering, 2019). Exotic plants are
408often pollination generalists (Bartomeus et al. 2008), dominating the diet of pollinators
409(Morales & Traveset 2009; Pyšek et al. 2011), and achieving high reproductive success
410(Thompson & Knight, 2018). Moreover, greater presence of exotic species is often associated
411with greater grazing intensity (Wraith & Pickering, 2019); in this sense, the orchid species that
412are able to survive in more invaded places could be benefiting from grazing being mainly
413directed to palatable exotic grasses. In addition, in cases where exotic plants also form
414mycorrhizal associations, native orchids can become displaced due to overlapping webs of
415fungi (Bonnardeaux et al. 2007).
416 Ecological condition positively influenced orchid individual abundance, although
417surprisingly it did not have an effect in the models for species richness and Shannon diversity
418index. Previous studies showed that good ecological condition (e.g. greater native vegetation)
419is related to diminished proportion of orchids at risk of extinction (Wraith & Pickering, 2019).
420The lack of effect of this factor in our models can be due to the fact that poor ecological
421condition is linked to habitat modification and fragmentation and increased levels of grazing
422and exotic cover (Wraith & Pickering, 2019). In this sense, the fact that we separated each
423effect showed that the main risk for orchids in disturbed habitats is the one posed by exotic
424plant species. However, we observed a threshold of ecological condition below which orchid
425species were not able to survive (Fig. 2).
426 It has been previously stated that orchids are negatively affected by high levels of
427irradiation and intensive grazing pressure (Akhalkatsi et al. 2014; Prowse et al. 2019); however,
17 428most of the orchid species restricted to Mediterranean climate ecosystems prefer habitats that
429appear disturbed to a certain degree (Martin-Fores, 2017; Tsiftsis & Antonopoulos, 2017).
430
431Fruiting success
432According to our results, fruiting success for D. pardina and G. major is mainly stochastic and
433not obviously controlled by local floral resources and surrounding vegetation, contradicting a
434priori predictions. Fruiting plants of both species were often observed in clusters, which
435suggests chance encounters with pollinators that visited neighbouring plants. It is also possible
436that developing fruits could have been grazed by kangaroos, although grazing pressure in the
437area did not seem to be high and many spent flowers were observed during fruiting surveys,
438confirming that they had not been pollinated. Overall, the success rate of the studied species
439appeared to be low; however, for food deceptive orchids, it is common that the reproductive
440success rate does not reach 20% (Gill 1989; Jacquemyn & Brys 2010). In this sense, populations
441of these species might rely on a small number of individuals producing seed to replenish the
442population or even long-lived individuals that are rarely pollinated, contrarily to other species,
443such as orchids from the genus Thelymitra for which developing fruits were observed
444frequently in the same plots. Contrary to our expectation, fruiting success of Diuris pardina
445was not significantly correlated with the native pea it mimics, Pultenaea largiflorens, despite
446bees being observed visiting P. largiflorens individuals during sampling. However, fruiting
447success of D. pardina positively correlated with the abundance of Lavandula stoechas, an
448introduced labiate woody species which strongly attracts honeybees (Kantsa et al. 2018). This
449trend was also observed in Australia for Diuris magnifica, which did not increase its
450reproductive success associated with the presence of native pea plants but appeared to be
451influenced by another non-model plant (Scaccabarozzi et al. 2019). The fact that G. major was
452positively correlated with the abundance of Wurmbea dioica might be due to the fact that
453male individuals of this dioecious lily attract two species of Australian native bees, and, to a
18 454lesser extent, honeybees (Vaughton & Ramsey 1998). Previous studies focusing on the
455pollination and fruiting success of the Australian orchid Caladenia versicolor also observed in
456their plots of study co-flowering plants of Wurmbea dioica and two species from the genus
457Drosera (Reiter et al. 2019).
458
459Orchids as indicators
460The combined effects of factors on orchid communities is complex to interpret and our results
461suggest that orchids are not ideal as a general or early-warning ecological indicators. Despite
462being sensitive to ecological conditions, orchids also appear to be favoured by edges at local
463scale, and small size habitat patches at larger scales. Preventing degradation of ecosystems
464and promoting high ecological condition and low exotic species cover is crucial to preserve
465orchids. However, the recommendations towards habitat configuration appear unclear, and
466need to be carefully considered at different spatial scales in order to inform management
467strategies. Previous literature suggested that the effect of habitat configuration in the
468surrounding landscape can differentially influence orchid population viability depending on the
469pollination syndrome displayed by the different species and on their specificity with the
470pollinator involved (Newman et al. 2013). Further studies should aim to elucidate the effect of
471the habitat configuration on orchid diversity controlling for the species’ pollination syndrome,
472clonal ability, and tolerance to disturbance. Therefore, unravelling responses of different
473functional groups within the orchid family to environmental factors such as the ones
474considered in this study would be necessary as a preliminary step to be able to use orchids as
475reliable indicators. This will also help to design improved conservation strategies adjusted to
476the configuration of the area to be managed.
477
478ACKNOWLEDGEMENTS
19 479We thank the TERN Ecosystem Surveillance team, Rosalie Lawrence, Robert Lawrence, Penny
480McLachlan, Katie Irvine, Michael Starkey, Sally O’Neill, Candy Guerin, Anthelia Bond, the
481District Councils of Mount Barker and Alexandrina and the South Australian Department for
482Environment and Water. This work has been funded by the grant 327-2018 – Australian Orchid
483Foundation - Climate and habitat condition controls on orchid populations – research
484outcomes associated with a citizen science program. Likewise, it has been possible thanks to
485the grant from the Department of Industry, Innovation and Science – Inspiring Australia,
486Citizen Science Grants – Wild Orchid Watch. We have no conflicts of interest to declare.
487
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28 FIGURES
Fig. 1. Representation of the effect of habitat parameters on orchids diversity at different spatial scales and the sign of the effect. See supplementary material S4 for the best subsets of models, and S5 for estimates, statistics and significance for fixed effects of the best models.
29 Fig. 2. Orchid species richness and individual abundance at different levels of disturbance, with regards to cover of exotic species, ecological condition and grazing intensity.
30 Fig. 3. Non-metric multidimensional scaling, in which the differences in orchids communities among sites can be differentiated by the colours of the polygons. Vectors show the disturbance variables that were significantly correlated to the ordination space.
31 Fig. 4. Number of flowers and fruiting success for Diruis pardina and Glossodia major. Photos of both species are original from the authors and were taken at Spring Gully Conservation
Park, South Australia.
32