Ecological effects of fire severity and time since fire on the diversity partitioning, composition and niche apportionment models of post-fire understory vegetation in semi-arid oak forests of Western Iran

Ecological Engineering 143 (2020) 105694

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Ecological effects of fire severity and time since fire on the diversity partitioning, composition and niche apportionment models of post-fire understory vegetation in semi-arid oak forests of Western Iran

T

Hadieh Moradizadeha, Mehdi Heydaria, , Reza Omidipourb, Arash Mezbanic, Bernard Prévostod ⁎

a

Department of Forest Science, Faculty of Agriculture, Ilam University, Ilam, Iran Department of Rangeland and Watershed Management, Faculty of Agriculture, Ilam University, Ilam, Iran c Senior Forestry of Darreh Shahr Natural Resources Office, Ilam, Iran d Irstea, Aix Marseille Univ., UR RECOVER, Mediterranean ecosystems and risks, Aix-en-Provence, France b

ARTICLE INFO

ABSTRACT

Keywords: Vegetation Diversity Species abundance distribution Composition Fire Semi-arid forest

Monitoring and evaluating changes of the various characteristics of ecosystems after disturbances are essential to protect the services provided by each ecosystem. In the present study, different ecological characteristics including structure, composition and diversity at different post-fire times (one, five and ten years) in low and high fire severities were investigated in semi-arid oak (Quercus brantii L.) forests of western Iran. One hundred twentysix 1.5 × 1.5 m plots were sampled in 14 patches (12 burned and 2 unburned). Alpha and beta diversity indices as well as species abundance distribution models of ecological niche apportionment were produced. The results showed that the distribution pattern of species, composition and diversity were influenced by the severity and time since fire. Based on Tokeshi's niche apportionment models, the low severity and short post-fire time generated a pattern with higher heterogeneity (MacArthur fraction; MF) than high severity and short post-fire time (dominance decay; DD). In contrast, long-term effects were less pronounced. Non-metric multidimensional scaling (NMDS) showed that there are different patterns of vegetation composition in low and high fire severities. The plant composition of the studied areas at low severity but with a longer time period since fire was more similar to the control area. In contrast, in the high fire severity, unburned and burned plots with different times since fire did not occupy overlapping areas in the ordination space. Severe fires also increased the alpha diversity at all spatial scales whereas the beta diversity only increased at the largest scale. Change of plant diversity pattern with time was more homogenous in low fire severity and more patchy in high fire severity. We concluded that the changes in the structure, composition and diversity of the post-fire vegetation were influenced both by fire severity and time since fire and that restoration actions to promote vegetation recovery should be adapted accordingly.

1. Introduction The study of species abundance distribution (SAD) can be defined as the abundance of all species recorded within an ecological community, assemblage or sample (Matthews and Whittaker, 2014). It has a long history in biogeography and community ecology (e.g. Preston, 1948; Tokeshi, 1993; McGill et al., 2007) and is considered as a very important initial step in investigating the ecology of populations and communities (Begon et al., 2006). SADs also provide the theoretical foundations for exploration of other macro-ecological and biogeographical patterns, such as the species–area relationship (‘SAR’, e.g. Preston, 1948; Hubbell, 2001; Whittaker and Fernández-Palacios,

⁎

2007). They are largely used in applied ecology and biogeography (Matthews and Whittaker, 2014) in particular, disturbance ecology (Gray and Mirza, 1979), hotspot selection (Dunstan et al., 2012), gradient analysis (Bazzaz, 1975), extrapolation (Hubbell, 2001; Zillio and He, 2010), and community structure analysis (Matthews et al., 2014). Many SAD models are available, for example McGill et al. (2007) listed 27 different models which can be classified in two main groups (Magurran, 2004; McGill et al., 2007; Matthews and Whittaker, 2014). One group with the statistical models (i.e. log series (Fisher et al., 1943) and log normal (Preston, 1948; May, 1975)) and the second group including biological (or theoretical) models (i.e. broken stick (MacArthur, 1957, 1960)), geometric or niche preemption models (Motomura,

Corresponding author at: Department of Forest Science, Faculty of Agriculture, Ilam University, Ilam, Iran. E-mail address: [email protected] (M. Heydari).

https://doi.org/10.1016/j.ecoleng.2019.105694 Received 30 July 2019; Received in revised form 26 November 2019; Accepted 27 November 2019 0925-8574/ © 2019 Published by Elsevier B.V.

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patterns and mechanisms for provision diversity which have always been considered as one of the main core goals of ecological research (Melo et al., 2009; Meynard et al., 2011). Changes in the structure and diversity of the post-fire vegetation depend on various factors in particular the characteristics of the fire regime (severity, history, repetition, season of occurrence), some main environmental regional conditions (e.g. climate and topography), and the functional characteristics of species in the existing communities (Kazanis and Arianoutsou, 2004; Coop et al., 2010; Russell-Smith et al., 2012; Power et al., 2016; Whitman et al., 2018). Diversity is one of the key features of ecosystems in particular in sustaining and maintaining their functions. However, diversity can be largely affected by fire whose impacts depend on fire characteristics such as severity and history (Keeley et al., 2003; Francos et al., 2016; Heydari et al., 2017). One of the important indicators of natural and human disturbances, such as fire, on diversity is the spatial diversity index (alpha and beta) (Erfanzadeh et al., 2015; Heydari et al., 2017). Although the impacts of various disturbances on the spatial and temporal components of the ecosystem biodiversity using spatial indices have been largely explored, there is still a lack of knowledge on how they are influenced by fire characteristics (e.g. Han et al., 2018). Several methods for measuring beta diversity (Koleff et al., 2003; Anderson et al., 2011) and beta diversity partitioning have been developed (Baselga, 2010; Schmera and Podani, 2011; Carvalho et al., 2012; Cardoso et al., 2014). In particular, beta diversity indices have proven effective in assessing different spatial and temporal patterns of ecological processes such as environmental filters (De Cáceres et al., 2012) like seed dispersal limitation (Scofield et al., 2012), or interspecific and intraspecific competition (Keller and Lau, 2018). Most studies analyze the short-term effects of fire on the characteristics of the understory vegetation whereas long-term studies are still lacking (Bataineh et al., 2006; Johnson et al., 2012). The speed at which the vegetation recovered is very variable and is largely influenced by site conditions, early vegetation composition and also fire severity (Kuuluvainen and Rouvinen, 2000; Pickup et al., 2013). Depending on these factors, the vegetation recovery can occur shortly or can be considerably delayed after the disturbance (Fernández-Abascal et al., 2004). Besides, fire severity also has a major impact (positive, negative or neutral) on the diversity, structure and functioning of the natural ecosystems (Burkle et al., 2015; Heydari et al., 2017). The present study aimed to investigate the effects of different fire severities in the short, intermediate and long term on the variation of diversity and structure of the vegetation communities in a semi-arid oak forest using SAD models based on ecological niches and spatial diversity indices. More specifically, the present study seeks to answer the following questions:

Table 1 A summary of Tokeshi's models (adapted from Magurran, 2004). Models

Selection of niche for division

Dominance decay MacArthur fraction

Largest niche always chosen Probability that niche is chosen is proportional to its size Niche chosen at weighted random Niche chosen at random No conventional niche apportionment assumed Smallest niche always chosen

Power fraction Random fraction Random assortment Dominance preemption

1932), and sequential fashion models (Sugihara, 1980). Statistical models were designed to produce empirical fit to observe data and to allow the investigator to objectively compare different assemblages (Magurran, 2004). On the other hand, biological models are related to how available niche space might be divided among the constituent species and whether the observed species abundance matches this expectation (Magurran, 2004). There are many different ways in which resources might be subdivided among species leading to various biological or theoretical models for niche apportionment. For example, Tokeshi (1990, 1993, 1996, 2009) introduced and developed six models (dominance decay (DD), MacArthur fraction (MF), power fraction (PF), random fraction (RF), random assortment (RA), and dominance preemption (DP)) that are based on the theoretical assumption that SADs are related to proportion of total resources/niches that are divided into smaller subunits by new species entering the community (Spatharis et al., 2009) (Table 1, Fig. 1). In addition, the various Tokeshi's models are different according to the way that this division occurs (Tokeshi, 2009). Tokeshi's niche apportionment models (hereafter niche-oriented) have been applied as a useful tool for quantifying structure of parasite community (Mouillot et al., 2003; Munoz et al., 2006), dragonfly community (Johansson et al., 2006), phytoplankton community (Spatharis et al., 2009), brachiopod community (Huang and Zhan, 2014), invertebrate communities (Tokeshi, 1990; Fromentin et al., 1997; Fesl, 2002; Barbone et al., 2007; Tokeshi, 2009), and plant community (Anderson and Mouillot, 2007; Yoko-o and Tokeshi, 2014). These models can also be helpful in analyzing the impacts of large disturbances on the communities' structures and resources. Fire is one of the most effective factors in the transformation of terrestrial ecosystems (Heydari et al., 2016; Miller et al., 2018; Guiterman et al., 2018). Although, at first fire seems to be a destructive factor in nature, many ecologists believe that fire should be considered as one of the inherent characteristics of terrestrial ecosystems and even a major management tool in the process of restoration and evolution of structural patterns and vegetation composition in natural ecosystems (Ramos-Neto and Pivello, 2000; González-Tagle et al., 2008; Ruprecht et al., 2013). Disruption of the environmental gradients by natural or anthropogenic disturbances, like fire, modifies the frequency distribution

a. Is the pattern of ecological niches apportionment between plant species altered by the interactions of fire severity and time since fire? Fig. 1. Illustration of Tokeshi's DD (dominance decay) model. In this model, total niche space (green bar) initially splits at random into two subunits (a). The smallest subunits (ashen color) is chosen by the first new species entering the community (b) and then remained niche split at random by the second species entering the community. The process is repeated in b) and c) until S species have been accommodated. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

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Fig. 2. Study area location in Iran and Darreh Shahr County. Patches selected in the study area are: OLS: One year after low severity fire; OHS: One year after high severity fire; FLS: Five years after low severity fire; FHS Five years after high severity fire; TLS: Ten years after low severity fire; THS: Ten years after high severity fire; UNB: Unburned.

2.2. Experimental design and field data collection

b. How are the scale-based diversity indices (alpha and beta) modified over time according to the different fire severities? c. Are the post-fire vegetation pathways differently influenced by interaction of fire severity and time since fire?

Based on data recorded in Darreh Shahr Natural Resources office, twelve burned patches and two reference areas (unburned: UNB) were selected in the same physiognomic conditions and vegetation type (2 replicates × 7 patches). The patches (i.e. our true replicates) are large enough (1–2 ha) and separated by a distance ranging from 700 m to 1600 m. These burned patches corresponded to two fire severities (low and high: LS and HS) and three time periods since fire (one, five, and ten years after fire: O, F, T). Visible indicators such as ash cover, sprout mortality, litter depth, and vegetation reestablishment were used to determined fire severity (see Heydari et al., 2017). These fires were usually ignited intentionally (without scientific support) by native farmers and ranchers for different purposes such as to reduce the catastrophic damage of wildfire, to promote the regrowth of the vegetation for cattle grazing, to improve soil fertility and vegetation composition, and to facilitate soil tilling in the agricultural lands adjacent to the forest. They sometimes became out of control and caused severe fires in the forest (Fig. 3). The perimeter and center of these patches were recorded with a Global Positioning System (GPS). In each burned and unburned patch, two perpendicular transects of about 100 m in length were randomly placed along a North-South Azimuth. Along these two perpendicular transects, nine sample points were located with four sample points along each transect and one where transects met. In total, in each fire severity × time treatment, 18 sample points (9 sample points × 2 replicates) were located. During the vegetation growth peak (i.e. May and June 2016), four 1.5 m × 1.5 m micro-plots distributed around each sample point were randomly established for recording abundance-dominance of understory vegetation based on the BraunBlanquet scale. All plant species were identified and named according to the available literature (Ghahreman, 2000; Mozaffarian, 2008).

2. Materials and methods 2.1. Study sites description The study area is located in a deciduous mixed forest (named southern Zagros forest), in north-western Iran, encompasses ~ 467 ha in Darreh Shahr County (33° 5′ 12″ - 33° 6′ 7″ N and 47° 18′ 44″ - 47° 20′ 31″ E) (Fig. 2). This relatively limited area, was chosen with great care because it gave us the unique opportunity to study the impacts of different fire conditions on plant communities growing in similar site conditions. In fact, in our study we tried to minimize variations in soil, climate, topography and vegetation conditions. This area is characterized by uniform physiographic conditions. Elevations range from 1400 to 1550 m a.s.l., with a mean of 1350 m a.s.l and slope ranges from 5 to 15%. Brant's oak (Quercus brantii Lindl.) is the dominant tree species in the study area with 70 individuals per hectare and canopy cover < 50%. Lithosols are the most common soil type in Zagros and are characterized by shallow depth and low fertility (Fathizadeh et al., 2017). Other woody species such as Amygdalus scoparia Spach and Acer monspessulanum occur sporadically. According to meteorological station located at Darreh Shahr county, average annual precipitation and average annual temperature are 427 mm and 21.4 °C respectively, with minimum and maximum monthly temperatures averaging 8.2 °C (January) and 35.2 °C (August), respectively.

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Fig. 3. Fire with different severities in Zagros oak forest ecosystem: a) Low fire severity b) High fire severity c) Forest guard extinguishing a fire which was lit by native farmers and ranchers d) Fire in and around the oak forest (Photos from Ilam natural resources office, 2000–2015).

2.3. Alpha and beta diversity indices To calculate the alpha and beta diversity indices at different spatiotemporal scales, the additive partitioning diversity approach (Lande, 1996) was used based on species richness. The calculation was carried out at two local (within and among plots; alpha 1 and beta 1 respectively) and regional scales (within and among the transects; alpha 2 and beta 2 respectively). In the present study, the total plant species diversity was divided into within and among sample units of each scale (plot and transect) according to the following equation (Crist et al., 2003):

= 1+ 1+ 2 where γ is, the total number of species in the region, α1, the average number of species within plot, β1, the average number of species among plots, and the β2, the average number of species among transects. The average number of species within each transect was calculated as α2. By differentiation of α1 diversity from α2, the value of β1 diversity index was calculated for each plot. Finally, by using the difference of the α2 index from the total number of species in the region, the β2 diversity index was obtained. For each fire severity and time since fire these calculations were performed separately (see Fig 4).

Fig. 4. An overview of the calculations of alpha and beta diversity indices at two scales (plot and transect).

(Random Assortment) were produced. The goodness-of-fit was evaluated using the Kolmogorov–Smirnov test at 0.05 level recommended as fitting tools for SAD model (Hill and Hamer, 1998). In this test, a not significant difference (p-value > .05) indicates that the actual data is fitted with the SAD model. To investigate the effect of fire severity and time since fire on the of alpha and beta diversity at two local and regional scales, we used a general linear model (GLM). In this model, the severity of fire (high and low) and time since fire (one, five and ten years) and their interaction were used as independent factors and, alpha and beta diversity indices

2.4. Statistical analysis To produce SAD models of ecological niche apportionment (Tokeshi, 1990; Tokeshi, 1996), the statistical package ‘nicheApport’ was used (Marques-Azevedo, 2017). In each studied patch, SAD models including DD (Dominance decay), MF (MacArthur Fraction), PF (Power Fraction), RF (Random Fraction), DP (Dominance Pre-emption) and RA 4

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severity treatment, while low fire severity reduced alpha 1 and increased alpha 2. Beta 1 diversity was not influenced by high fire severity after one or five years but decreased significantly after ten years. In contrast, in low fire intensity, beta 1 diversity was the highest after ten years and showed similar values after one or five years. β2 diversity showed an opposite trend: it was considerably reduced in low fire severity compared to high fire. In this later treatment, it peaked after one year and then significantly decreased with time (Fig. 6).

at two local (alpha 1 and beta 1) and regional scales (alpha 2 and beta 2) were used as dependent variables. Mean comparison of diversity components between different fire severities with different times since fire was performed by Duncan's multiple range test. The hypothesis that understory vegetation composition differs among plots with different time since fire was explored using nonmetric multidimensional scaling (NMDS) as part of the ‘vegan’ package in R (Oksanen et al., 2018). This ordination method which projects multivariate data into a space with fewer dimensions, was performed on each dataset using Bray–Curtis dissimilarity. In this method, plots with similar vegetation composition are close together. Normality and homoscedasticity assumptions were tested with Kolmogorov-Smirnov and Levene's tests, respectively. One-way analysis of variance (ANOVA) was applied to identify significant differences of the scores of the sample plots on the NMDS axis. All statistical analysis was performed using R 3.5.2 (R Core Team, 2018).

3.3. Understory vegetation composition Non-metric multidimensional scaling showed that there are different patterns of vegetation composition in low and high fire severity (Fig. 7 a, b) indicating that vegetation composition is clearly affected by fire severity and time since fire. ANOVA results confirmed that plant community composition significantly varied among the treatments in high fire severity (NMDS axis 1, F- value = 301.10 and Pvalue = 0.000; NMDS axis 2, F- value = 29.38 and P- value = 0.000). Differences in vegetation composition according to time since fire are particularly pronounced in the high fire severity conditions as there are no overlapping areas in the ordination space with unburned plots (Fig. 7 a, b). Even after one year after the fire, species composition separated significantly from that observed in the UNB, FLS and TLS. In contrast, vegetation composition of plots of low fire severity did not clearly differentiate at the different years after fire, except to some extent for OLS that separated from other burned and unburned plots (FLS, TLS and UNB) along axis 1 of the NMD (Fig. 7 a).

3. Results 3.1. Pattern of plant species abundance distribution The examination of the plant species abundance distribution with SAD models of ecological niche apportionment showed that only the dominance decay model (DD) and MacArthur Fraction model (MF) had the highest compliance with observed data (Table 2; and Fig. 5). Based on the results in the unburned area, the frequency distribution of species was the most consistent with DD model (p-value = .557). The severity of fire along with time since fire changed the pattern of species abundance distribution. For example, in OHS and OLS, observed data had the highest compliance with MF (p-value = .355) and DD (pvalue = .744) models respectively. Also, after five years of high fire severity (FHS) and low fire severity (FLS), DD (p-value = .582) and MF (p-value = .281) models respectively had better fitness with observed data (Table 2; and Fig. 5). After ten years DD and MF models had most compliance with field data in low (TLS) and high (THS) fire severity respectively (Table 2 and Fig. 5).

4. Discussion 4.1. Pattern of plant species abundance distribution Species abundance distribution models are important ecological tools for analyzing the structure of different communities (Matthews and Whittaker, 2014), and their effectiveness in evaluating changes in plant communities' structure under the influence of environmental stresses has also been reported (Komonen and Elo, 2017). The niche apportionment models are a new group of abundance distribution models that, in addition to the abundance of plants, work under some assumptions of access and occupation of ecological niches (Tokeshi, 2009). Therefore, these process-oriented models provide more valuable information than common frequency-based models (i.e. geometric serie). The results of this study showed that the studied regions are highly homogenous (i.e. variation of evenness is low) as shown by the high compliance with pattern of dominance decay and MacArthur fraction models. In general, intense competition for limited resources, especially in environments with low production capability, will increase the evenness of plant composition (Stoll and Bergius, 2005; Rayburn and Monaco, 2011). In our study area which is part of Zagros forest ecosystem, environmental restrictions such as low precipitation, short growing season (Heydari et al., 2016; Fathizadeh et al., 2017), low soil depth (Heydari et al., 2016; Talebi et al., 2013) and low levels of soil nutrients (Hashemi et al., 2019) can be important factors causing intense competition between plants and increasing uniformity. The results indicated that the change in the evenness depends on the fire severity and time since fire as shown by the decrease of evenness one year following low fire severity. Also, the pattern of distribution in the UNB area and OHS was fitted by the DD model. In general, low fire severity decreased competition (Marozas et al., 2007; Laughlin and Fule, 2008) by promoting access to nutrients (Nelson et al., 2012) which led to increase the heterogeneity of the plant composition. The low severity fire, by increasing richness and diversity (Crawford et al., 2001), changed plant abundance and reduced the evenness. In contrast, fires of high severity generally result in the elimination of most plants (sensitive or not to fire) from the initial community composition and

3.2. The effect of fire severity and time since fire on the spatial diversity indices Alpha and beta diversity components were significantly affected by the severity of fire at both local and regional scales (Table 3). The negative effect of high fire severity on alpha 1 and alpha 2 components was observed only one year after fire (Fig. 6). Alpha 1 and alpha 2 diversity indices significantly increased with time in the high fire Table 2 Comparisons of the goodness-of-fit of SAD models of ecological niche apportionment to species abundance data under different fire severities and time since fire. OLS: One year after low severity fire; OHS: One year after high severity fire; FLS: Five years after low severity fire; FHS Five years after high severity fire; TLS: Ten years after low severity fire; THS: Ten years after high severity fire; UNB: Unburned. Models

Dominance decay (DD) MacArthur Fraction (MF) Random Fraction (RF) Power Fraction (PF) Random Assortment (RA) Dominance Pre-emption (DP)

Low severity fire

High severity fire

UNB

THS

FHS

OHS

TLS

FLS

OLS

0.557 0.281 0.003 0.000 0.000

0.213 0.087 0.000 0.000 0.000

0.186 0.281 0.000 0.000 0.000

0.744 0.050 0.000 0.000 0.000

0.441 0.594 0.000 0.000 0.000

0.582 0.132 0.004 0.000 0.000

0.152 0.355 0.000 0.000 0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

Underlined values (p-values) indicted that model statistically significantly agrees with observed data based on Kolmogorov–Smirnov test at 0.05 level. 5

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Fig. 5. Abundance distribution pattern curves in regions differing in fire severity and time since fire. OLS One year after low severity fire; OHS: One year after high severity fire; FLS: Five years after low severity fire; FHS Five years after high severity fire; TLS: Ten years after low severity fire; THS: Ten years after high severity fire; UNB: Unburned; DD: Dominance decay; MF: MacArthur Fraction; PF: Power Fraction; RF: Random Fraction; C: Control or Observed Data.

destruction of soil seed banks (Heydari et al., 2017). Besides, they can also increase the competition ability of some plant species and favor the dominance of fast-colonizing species (Kelly et al., 2016). These modifications result in the removal of rare and common species to the benefit of stress-tolerant plants which increase the evenness of the composition (Pourreza et al., 2014). In areas submitted to a fire of high severity, large gaps and unoccupied ecological niches were likely to be created immediately. These conditions were favorable to the migration and establishment of new species with time, leading to a more heterogeneous vegetation. This interpretation was consistent with the shift observed in the distribution pattern from dominance decay to MacArthur fraction in the present study, five years following the high severity fire. However, with time, the vegetation heterogeneity tends to decrease (Knight and Holt, 2005) suggesting a progressive return to pre-fire conditions. In contrast to these latter results, we found that the diversity of plant communities was reduced after a low fire severity due to the loss of most sensitive and non-sensitive plants of the initial community. Along with previous studies (e.g. Komonen and Elo, 2017), we found that species abundance distribution models can be used as key tools for assessing and monitoring changes in the structure of plant communities after disturbances such as fire.

fire-sensitive plants did not recover with time or at a very slow rate. For instance, species such as Echinops kotschyi Boiss, Thymus daenensis Celak, Oliveria decumbens Vent were present only in the unburned areas. At each time step (1, 5 and 10 years after fire), we noted a significant and higher increase in beta-2 diversity after high severity fire than after low severity fire. The severe fire, by the elimination of a wide range of sensitive and non-sensitive species in different regions, deeply modifies the floristic composition and tends to create a certain degree of homogeneity at a small scale but leads to heterogeneity at a large scale (Myers et al., 2015). In this regard, Laughlin and Fule (2008) reported that the low severity fire increased the species richness, while a fire of higher intensity limited species richness. However, severe fires do not totally eliminate some perennial species, as these species have developed resistant vegetative reproduction systems such as rhizomes allowing their recovery (Gonzalez and Ghermandi, 2008). For instance, in our study species such as Lolium rigidum Gaudin and Hordeum bulbosum L. were not affected by fire thanks to their perennial rhizome. We concluded that the severe fires caused the fragmentation of plant communities which increased with time, leading to a higher beta-2 diversity.

4.2. Alpha and beta diversity

Our results showed that the occurrence of fire modified the plant community composition and that the rate of change varied according to fire severity as shown by numerous studies (Gibson et al., 2016; Bachinger et al., 2016; Scherer et al., 2017; Tsafrir et al., 2019). More precisely, we showed that the vegetation composition affected by low severity fire had higher similarity (overlap in NMDS) with the unburned area after 5 and 10 years (Fig. 7 a) than the high severity fire. In fact, severe fires, by eliminating or reducing the overstory canopy cover, may increase the vulnerability of soil, reduce soil organic matter, destroy soil structure, and favor the loss of nutrients (Alexander et al., 2006; Fernández et al., 2008; Mataix-Solera et al., 2011; Pereira et al., 2013). These alterations, as well as the direct damage to seed storage and seed producer's plants (Santana et al., 2010), deeply modify the vegetation composition whereas changes are limited after a low intensity fire (Úbeda and Outeiro, 2009). In fact, we showed that severe fire resulted in high dispersion (low similarity) among the studied regions, indicating that the vegetation composition could not return to the pre-fire stage (UNB) even after 10 years (Fig. 7 b). Because habitat conditions were deeply altered, the recovery of the vegetation composition after a severe fire will require a long time and even cannot occur in the absence of restoration actions. In this regard, Heydari et al. (2016) also showed that a high severity fire could lead to contrasted plant composition among habitats thereby extending the recovery time. From these results, we concluded that the effects of low severity fire on the vegetation composition were temporary, while the

4.3. Post fire secondary succession

Alpha and beta diversity indices are highly sensitive to environmental modifications and can therefore be used to reflect changes in plant community following various disturbances such as fire (Mattingly et al., 2015; Heydari et al., 2017; Mahood and Balch, 2019). Our results indicated that plant diversity indices varied with the spatial scales considered and were influenced by the fire severity and time since fire. For example, alpha 1 and alpha 2 indices increased with time for the high severity fire whereas only the alpha 2 diversity was increased in the low severity fire. These variations at both scales (plot and transect) can be explained by the increasing species migration to unoccupied areas caused by fire. The increase in the number of species after a certain period following disturbance, in particular fire, has been frequently reported by other researchers (e.g. Laughlin and Fule, 2008). This result is also consistent with the hypothesis of the secondary succession of plant communities stating that their diversity increases over time (Capers et al., 2005; Sun et al., 2017). Therefore, it can be concluded that the increase of alpha diversity is scale-dependent after low fire severity but not after high fire severity. We also found that time since low fire severity reduced the beta diversity (homogeneity) at both scales. In other words, the similarity of the plant community compositions had increased with time. After a fire of low severity, fire-sensitive species were likely to be eliminated while the competitiveness of fire-resistant species increased. Besides, these

Table 3 Results of the GLM with time since fire (Year), fire severity and their interaction as fixed factors and diversity indices as dependent variables. The statistics are mean square (MS), F-value (F) and levels of significance (*** P < .001, ** P < .01, * P < .05). Alpha1: plant species diversity within plots; Alpha2: plant species diversity within transects; Beta1: plant species diversity among plots; Beta2: plant species diversity among transects. R2 indicate the percentage of variation explained by the GLM model. Source of variation

Year Severity Year × Severity R2

Alpha 1

Alpha 2

Beta 1

Beta 2

MS

F

MS

F

MS

F

MS

F

83.73 23.15 349 0.656

18.3*** 5.06* 76.28***

162.75 330.75 177.75 0.615

175.6*** 357*** 191.86***

15.81 178.89 98.26 0.468

3.49* 39.48*** 21.69***

162.75 5676 177.75 0.685

175.6*** 6127*** 191.8***

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Fig. 6. Comparison of the diversity components (mean ± SD) between the different fire severity and time since fire treatments. Different letters indicate significant differences among the treatments (Duncan's multiple range test).

Fig. 7. Non-metric multidimensional scaling (NMDS) ordination based on Bray-Curtis similarity matrix, exploring differences in the understory vegetation community composition between plots with different time since fire (one, five and ten years) in low (a) and high (b) fire severities; OLS: One year after low severity fire, FLS: Five years after low severity fire, TLS: Ten years after low severity fire, OHS: One year after high severity fire, FHS: Five years after high severity fire, THS: Ten years after high severity fire. UNB = Unburned area.

severe fire caused a substantial change in the composition of the plant communities. Vegetation succession pathways are therefore deeply affected by severe fires: post-fire plant community composition largely differs from its pre-fire composition and its recovery can only occur after a long period (> 10 years) and probably with the help of restoration actions.

dependent on the fire severity and time since fire. Time since low severity fire led to a more homogeneous vegetation (decreased beta 1) while time since severe fire created a patchiness pattern on the site. Secondary post-fire successions are therefore modified. In particular, severe fire will cause radical changes in plant communities especially through the elimination of a wide range of fire-sensitive species and these modifications are long-lasting. The return to a pre-fire vegetation composition is time-demanding and restoration operations are often needed to speed up the process. In contrast, the low severity fire, by reducing competition and increasing access to resources, has a beneficial effect on species richness and can even be used as a valuable management tool for biodiversity conservation.

5. Conclusion Fires of natural or human origins are largely inevitable in dry ecosystems submitted to anthropogenic disturbances. In the absence of adapted restoration actions, they threaten in the long term the services and functions of these ecosystems. However, choosing the most suitable rehabilitation approaches for the recovery of plant communities after fire requests a precise knowledge of the effects of fire on the vegetation composition and habitat characteristics. In this study, we showed that a low severity fire generated a pattern with a higher heterogeneity (MacArthur fraction; MF) than a high severity fire (dominance decay; DD), although the effects decreased with time. The pattern of frequency distribution as well as plant diversity and composition were also

Authors' contribution Mehdi Heydari and Reza Omidipour contributed in designing the experiment, analyzing the data, and writing the manuscript. Hadieh Moradizadeh and Arash Mezbani contributed in collecting the data; Mehdi Heydari, Reza Omidipour and Bernard Prévosto were involved in revising the manuscript. 8

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Declaration of Competing Interest

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