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\begin{document}

\title{A pantropical analysis of fire impacts and post-fire recovery on
tropical plant diversity and species composition}



\author[1]{Dharma Sapkota}%
\author[1]{David Edwards}%
\author[1]{Mike Massam}%
\author[2]{Karl Evans}%
\affil[1]{The University of Sheffield}%
\affil[2]{Sheffield University}%


\vspace{-1em}



  
  \date{May 24, 2024}


\begingroup
\let\center\flushleft
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\selectlanguage{english}
\begin{abstract}
Fire is increasingly driving loss and degradation of tropical habitats,
but factors influencing biodiversity responses to fire are inadequately
understood. We conduct a pan-tropical analysis of systematically
collated data -- 5257 observations of 1705 plant species (trees and
shrubs, forbs, graminoids and climbers) in burnt and unburnt plots from
28 studies. We use model averaging of mixed effect models assessing how
plant species richness and turnover (comparing burnt and unburnt
communities) vary with time since fire, fire type, protected area status
and biome type. More long-term studies are needed, but our analyses
highlight three key findings. First, prescribed and non-prescribed burns
have contrasting impacts on plant communities, the direction of which
depends on focal life form and biome. Forb richness, for example,
increases following non-prescribed (but not prescribed) burns in
savannahs and flooded grasslands, but in moist broadleaved forest forb
richness increases strongly following prescribed (but not
non-prescribed) burns. Second, protected areas mitigate fire impacts on
plant communities. Species richness of trees/shrubs increased (by
\textasciitilde{}50\%) following fires in non-protected sites but tended
to remain similar in protected sites. Similarly, ten years after a fire
event graminoid community composition had recovered fully to resemble
non-burnt communities in protected areas, but remained highly divergent
in unprotected sites. Finally, this persistence in divergence of
community composition following fire events occurs across a number of
life forms. Composition of tree/shrub communities remained divergent
from unburnt communities ten years after a fire, and composition of forb
communities only returned to those of unburnt sites after ten years.
Fire intervals are already less than ten years in some tropical
locations, and future climate and land use change are predicted to
further shorten these intervals. Plant communities across much of the
tropics are thus likely to change substantially in response increased
exposure to fire.%
\end{abstract}%



\sloppy


\textbf{Introduction}

Globally, the distribution, seasonality, frequency and intensity of
fires has changed in recent decades due to anthropogenic global change
drivers including climate change, land-use change (with fire often used
to clear vegetation to facilitate land-use change) and, in some cases,
invasion by more flammable species (McLauchlan et al., 2020, Kelly et
al., 2020). These changes are predicted to accelerate over the next few
decades (Sheehan et al., 2019; Enright et al., 2015; Arag\selectlanguage{ngerman}ão et al.,
2008). There is particular concern regarding the impacts on
fire-sensitive tropical ecosystems, many of which are being rapidly lost
and degraded (Alroy, 2017; Busch \& Ferretti-Gallon, 2017), making the
tropics the epicentre of current and future extinction risk (Edwards et
al., 2019). Given these changing fire regimes, it is crucial to
understand how fire influences biodiversity, and the rate of recovery
following fire events (Kelly et al., 2020). This need is widely
recognised, for example, by the Intergovernmental Science-Policy
Platform on Biodiversity and Ecosystem Services (IPBES, 2019) and the
UNFCC REDD+ program (UNFCC, 2019).

The impacts of fire on biodiversity are, however, complex and
incompletely understood (Gill et al., 2013; McLauchlan et al., 2020;
Tingley et al., 2016), with positive and negative impacts reported
(Kelly et al., 2020; Giorgis et al., 2021). Some tropical biomes, such
as woody savannas and grasslands, are frequently exposed to fire, and
several species that are characteristic of these biomes require fires to
persist (Simon \& Pennington, 2012). In such biomes, fire positively
influences the diversity of photophilic floras and faunas (Pausas \&
Keeley, 2019), with a landscape mosaic of vegetation patches that vary
in the time since they were burnt typically maximising biodiversity
(Driscoll et al., 2010). Long-term suppression of fire in these systems
typically generates more homogenous vegetation patches that support
fewer species (Giorgis et al., 2021; Abreu et al., 2017), promotes woody
species and gradual shifts from grasslands to woody savannas, and then
shrublands and forests (Probert et al., 2019).

In other biomes, such as tropical moist forests, fire is historically
extremely rare and most plant species are highly sensitive to fire
(Cochrane \& Schulze, 1999, Giorgis et al., 2021). Consequently, recent
increases in the number of fires are a primary driver of tropical moist
forest degradation and biodiversity loss (Barlow et al., 2019; Lewis et
al., 2015), including tree (Galvao de Melo \& Durigan, 2010, Cochrane \&
Schulze, 1999) and forb communities (Gordijn et al., 2018). Increased
exposure to fire can also eventually convert moist tropical forest
ecosystems into open habitat and savannas (Flores \& Holmgren, 2021).

Biodiversity will gradually recover following a fire event, and should
increasingly resemble the pre-fire community as time increases (Machida
et al., 2021). Frequent fire events can, however, prevent full recovery
by driving fire sensitive species to regional extinction (Gallagher et
al., 2021), and species recovery following fire can be much slower in
fire sensitive biomes than those that traditionally experience fire
(Nelson et al., 2014).~ Understanding of how biodiversity recovers
following fire events is, however, still insufficiently developed. In
part, this is because many studies of biodiversity responses to fire
focus exclusively on species richness, even though fires have strong
impacts on community composition and generate considerable turnover,
i.e. beta diversity (Gordijn et al., 2018; Durigan et al., 2020,
Peterson \& Reich, 2008). The influence of landscape context on
biodiversity recovery following fire is also insufficiently understood.
Recovery rates are likely to be faster within relatively intact
ecosystems (i.e. effectively protected from anthropogenic stressors) in
which a greater abundance of natural vegetation increases the
availability of propagules that can recolonise burnt sites. Well managed
protected areas may thus facilitate faster recovery from fires, although
tropical protected areas vary greatly in their effectiveness, including
at reducing fire risk (Laurance et al., 2012).

Most studies assessing fire impacts on plant biodiversity focus on
single study locations. Meta-analyses are scarce but have assessed the
relative fire sensitivity of native and exotic plant species (Jauni et
al., 2015; Alba et al., 2014; Aslan \& Dickson, 2020). Here, we build
upon a systematic compilation of data from published studies of tropical
and sub-tropical plant community responses to fire. We work on plants as
they comprise a wide range of life forms and life history strategies and
provide the habitat structure and resources that are exploited by other
taxonomic groups. We assess post-fire recovery of plant species richness
and composition following fire events. Specifically, we test whether
species richness and beta diversity (i.e., species turnover) between
burnt and unburnt plots respond differently to time since fire and fire
type (prescribed burns versus non-prescribed burns). We also assess if
protected area status (protected vs unprotected) moderates' responses of
species richness and species turnover to fire events. Our analyses take
biome identity into account and distinguishes between prescribed and
non-prescribed burns. We do so as prescribed burns are often used in
management programmes to reduce the amount of flammable material and
thus the size and intensity of subsequent fires. The practice has,
however, been criticised (Ryan et al., 2013), with some studies
suggesting that prescribed burns can alter plant communities in a manner
similar to non-prescribed burns (Ffolliott et al., 2012; Pastro et al.,
2011).

\textbf{Methods}

\textbf{Literature search}

A systematic literature search was conducted following the PRISMA
guidelines (Liberati et al., 2009; Moher et al., 2009) and completed in
March 2023. Three searches were carried out using `Web of Science', with
the search terms: i) fire* AND ``species richness'' AND plant*; ii)
burn* AND ``species richness'' AND plant*; and iii) fire*AND ``species
richness'' AND tree*. Our objective was to retain papers that were
empirical field-based studies, conducted in the tropics or sub-tropics,
i.e., 30\textsuperscript{o} north to 30\textsuperscript{o} south
(Corlett, 2013), and that provided complete species lists for control
(unburnt or sites sampled before a fire) and treatment sites (those with
fires). We only selected studies with equal sampling effort in control
and treatment sites, as such biases in study design can impact
conclusions regarding fire impacts on biodiversity (Kelly et al., 2017).

Data collection process took place in five stages (Table 1). After
collecting the papers from the initial search, titles were scanned to
identify papers that could be used to understand the impacts of fire on
plant diversity in tropical and sub-tropical locations. Duplicate papers
were removed, and abstracts were then scanned. Papers were only accepted
if the study met our criteria of being an empirical field-based study
located in the tropics or sub-tropics. We then read each paper in full
and removed those for which sampling effort was uneven across control
(unburnt) and treatment (burnt) sites or did not provide a complete
species list for each type of site. A list of retained papers is given
at \textbf{Appendix 1} .

\textbf{Table 1:} The selection stages, procedure, and total no. of
papers obtained in the literature search.\selectlanguage{english}
\begin{longtable}[]{@{}ll@{}}
\toprule
\begin{minipage}[b]{0.47\columnwidth}\raggedright\strut
\textbf{Selection Procedure}\strut
\end{minipage} & \begin{minipage}[b]{0.47\columnwidth}\raggedright\strut
\textbf{Number of papers}\strut
\end{minipage}\tabularnewline
\midrule
\endhead
\begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
Papers yielded from initial search\strut
\end{minipage} & \begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
8970\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
Papers left after scanning titles\strut
\end{minipage} & \begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
1431\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
Papers left after removing duplicates\strut
\end{minipage} & \begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
1065\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
Papers left after reading the abstract\strut
\end{minipage} & \begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
460\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
Papers left after reading in full and checking selection criteria are
met\strut
\end{minipage} & \begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
28\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

\textbf{Data extraction \& quality control}

The final set of 28 studies contained 101 pairwise control (unburnt) and
treatment (burnt) plots and 5311 observations, where one observation
equates to a species being present in a burnt or unburnt plot
(\textbf{Appendix 2, Table S1} ). Some studies reported changes in tree
and shrub communities but used plot sizes that are widely considered too
small for accurate estimates of the species richness of these groups as
the plots could only contain one or two mature individuals of these life
forms. We thus did not include observations for calculating tree species
richness when plots were less than 100 m\textsuperscript{2}, or for
shrub species richness when plots were less than 16
m\textsuperscript{2}(see Mueller-Dombois et al., 2008). Most studies (n
= 24; 85 \%) provided their study site's latitude and longitude but when
these were not provided, they were obtained using the description of the
study site location and the online tool\emph{https://www.latlong.net/}.
Not all studies provided data on species' abundances (density or
percentage cover), so we converted data into a presence/absence matrix
for each burnt and unburnt site.

From each study, we extracted data on two fire metrics -- time since
fire (number of years between the most recent fire and sampling period),
and fire type (prescribed or non-prescribed burns). We defined each site
as protected if it was within the boundaries of a protected area (IUCN
categories I to VI) as defined by the World Database on Protected Areas
(WDPA) database (UNEP-WCMC \& IUCN, 2020); this was achieved using
the\emph{wdpar} R package version 1.3.2 (Hanson, 2020).

Biomes were classified according to Olson et al. (2001) as i) Tropical
and Subtropical Moist Broadleaf Forests (TSMBF; 5 papers \& 1846
observations) ii) Tropical and Subtropical Dry Broadleaf Forests (TSDBF,
6 papers \& 859 observations) iii) Tropical and Subtropical Coniferous
Forests (TSCF, 1 paper \& 31 observations), iv) Tropical and Subtropical
Grasslands, Shrublands \& Savannas (TSGSS, 13 papers and 2433
observations) and v) Flooded Grasslands and Savannas (FGS, 3 papers and
142 observations).

\textbf{Standardising taxonomy and life form classification}

Species, genus, and family names were standardised according to The
Plant List, R packages \emph{Taxonstand} version 2.4 (Cayuela et al.,
2012) and The World Flora, R package \emph{WorldFlora} version 1.10
(Kindt, 2020). Species that differ in their life-history strategies,
especially plants, can exhibit divergent recovery responses to fire
(Maginel et al., 2019; Foster et al., 2018, Peterson \& Reich, 2008).
Forbs and grasses, for example, often recover much quicker from fire
than tree species due to traits such as shorter generation times and
faster growth rates (Peterson \& Reich, 2008; Machida et al., 2021).
There is also much inter-specific variation in plant species' responses
to fire within life forms (Simpson et al., 2020; Trouv\selectlanguage{ngerman}é et al., 2021).
Hence, we then classified each species to one of nine life forms:
tree/shrub, forb, climber, graminoid, fern, succulent, lichen, and moss
using eight datasets from the TRY database (Kattge et al., 2020);
Botanical Information and Ecology Network (BIEN) database in R using the
package \emph{BIEN,} version 1.2.6 (Maitner, et al, 2017) and AusTraits,
a curated plant trait database for the Australian flora using the
package \emph{aurstraits} in R (Falster et al, 2021). This allowed us to
classify 88 \% of species; the remaining species were classified using
authenticated online sources or the life-form classification used in the
original study (\textbf{Appendix 3, Table S2} ).~ Ferns, succulents,
lichens, and mosses were excluded from further analysis as they were
recorded in too few studies (fewer than five). A list of plant groups
and the number of i) studies that recorded them, ii) observations and
iii) species recorded is presented at \textbf{Appendix 4, Table S3.}

\textbf{Biodiversity metrics}

We calculated two response variables (relative species richness -- alpha
diversity; beta-diversity - pairwise dissimilarity) for each of the four
analysed life forms, i.e., trees/shrubs, forbs, graminoids, and
climbers. Relative species richness was calculated following Burivalova
et al., (2014) as the total number of species in the burnt site divided
by the total number of species in the unburnt site. Consequently, values
of one represent situations where fire has no impact on species
richness, values less than one represent situations where fire reduces
species richness, and values greater than one represent situations where
fire increases species richness.

Species turnover (beta diversity) was calculated as Sørensen pairwise
dissimilarity index (Sørensen, 1948), which is widely used to measure
the spatial turnover for presence/absence data in ecology and is
independent of species richness (Koleff et al., 2003; Socolar et al.,
2016). A value of 0 means the composition of two communities is
identical, and a value of 1 means the two communities do not share any
species in common.

\textbf{Data analysis}

All analyses were conducted in R 4.2.2 (R Core development team, 2022).
Continuous variables were centred prior to analysis, and we used the
equivalent sum to zero contrasts approach for categorical variables
(Schielzeth, 2010).~Centering variables reduces problems that otherwise
arise with model averaging when interaction terms are included as
predictors (Schielzeth, 2010; Cade, 2015; Tyre, 2017). We modelled
relative species richness and Sørensen index of i) trees/shrubs, ii)
forbs, iii) graminoids, and iv) climbers using linear mixed effects
methods with study ID as a random effect, using the lme4 package (Bates
et al., 2015).

In all cases models had Variance Inflation Factors (VIF) less than 10
indicating that results are not markedly impacted by collinearity
between predictors (Hair et al., 1992, Craney \& Surles, 2002). We also
checked for linearity of responses by including square terms and
comparing the model fit to equivalent models that only included a linear
term. The fit of all models was also checked using model diagnostic
plots.

We constructed all possible ecologically realistic models (n =
32;\textbf{Appendix 5, Table S4} ) given our suite of predictor
variables, i.e., time since fire (years; ln transformed), fire type
(fixed factor: prescribed/non-prescribed burns), biomes (fixed factor:
TSMBF, TSBDF, TSCF, TSGSS \& FGS), and protection status (fixed factor:
protected/non-protected). We included interaction terms between each of
our two fire metrics (time since fire, and fire type) and i) biomes, and
ii) protection status to test whether biome type or protected area
status moderated the relationships between each fire metric and our
outcome variables.

We used D\textsuperscript{2} as a measure of explanatory capacity;
D\textsuperscript{2} = (ND \selectlanguage{english}- RD)/ND where ND is the null deviance and RD
is the residual deviance, which cannot be explained by the model, thus
`ND--RD' is the explained deviance. D\textsuperscript{2} varies between
zero and one and equals one when the deviance can be explained
completely by the model (Guisan \& Zimmermann, 2000).

We used an information-theoretic criterion approach to obtain a set of
models whose \selectlanguage{greek}Δ \selectlanguage{english}AICc values were within two points of the best performing
model, i.e., that with the lowest AICc value, and then conducted model
averaging (Burnham \& Anderson, 2004).

\textbf{Results}

\textbf{Study Locations}

Our final set of 28 studies were located across the tropics (n = 22) and
sub-tropics (n = 6), although studies from South America (n=10) and
Australasia (n = 7) dominated (Fig 1). Five studies were in the Tropical
and Subtropical Moist Broadleaf Forests (TSMBF), six in the TSDBF
(Tropical and Subtropical Dry Broadleaf Forests), one in TSCF (Tropical
and Subtropical Coniferous Forests), 13 in the TSGSS (Tropical and
Subtropical Grasslands, Shrublands and Savannas), and three in the FGS
(Flooded Grasslands and Savannas) biomes (Fig 1).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

\textbf{Fig} \textbf{1} . Study locations coded by biomes. FGS (Flooded
Grasslands and Savannas); TSCF (Tropical and Sub-tropical Coniferous
Forests); TSDBF (Tropical and Sub-tropical Dry Broadleaf Forests); TSGSS
(Tropical and Sub-tropical Grasslands, Shrublands and Savannas); and
TSMBF (Tropical and Sub-tropical Moist Broadleaf Forests). The dotted
lines show the boundary of the sub-tropical zone\emph{.~}

\textbf{Relative Species Richness}

Models of the relative species richness of trees/shrubs in burnt and
unburnt plots had limited explanatory power (i.e., 5.78\%), and model
averaging revealed that the 95\% confidence intervals of most parameter
estimates overlapped zero (Table 2). Protection status did, however,
influence relative species richness of trees/shrubs -- with species
richness increasing at burnt sites relative to unburnt controls in
unprotected sites, whilst within protected sites species richness was
more similar in burnt and unburnt sites (Table 2; Fig. 2a). Models of
forb relative species richness in burnt and unburnt plots had much
higher explanatory power (i.e., 41.74\%) than models for other life
forms (Table 2). Model averaging revealed interactions between biome
type and type of fire. In flooded grasslands and savannahs forb species
richness increased in plots experiencing non-prescribed burns relative
to unburnt controls, with species richness being more similar in burnt
and unburnt controls following prescribed burns (Table 2; Fig. 2b). In
contrast, in moist broadleaved forest prescribed burns increased forb
species richness relative to unburnt controls, whilst non-prescribed
burns resulted in similar species richness in burnt and unburnt controls
(Table 2; Fig. 2). Models of the relative species richness of climbers
in burnt and unburnt sites also had relatively limited explanatory power
(i.e., 5.17\%) with model averaging revealing that prescribed burns
resulted in climber richness increasing in burnt plots relative to
unburnt ones, whilst non-prescribed burns resulted in species richness
declining in burnt sites relative to unburnt controls (Table 2; Fig.
2c). The best model of graminoid relative species richness had a higher
AICc (88.90) than that of the null model which lacked predictors (AICc
86.97).

\textbf{Table 2:~} Results from model averaging across multiple
regression models of relative species richness in burnt sites relative
to control (unburnt) sites for trees/shrubs, forbs, and climbers.
Results for graminoids are not included as no model had a lower AICc
than the null model (i.e., one that lacked predictors). Parameter
estimates are provided with 95\% confidence intervals in brackets. FGS
(Flooded Grasslands and Savannas); TSCF (Tropical and Sub-tropical
Coniferous Forests); TSDBF (Tropical and Sub-tropical Dry Broadleaf
Forests); TSGSS (Tropical and Sub-tropical Grasslands, Shrublands and
Savannas); and TSMBF (Tropical and Sub-tropical Moist Broadleaf
Forests).\selectlanguage{english}
\begin{longtable}[]{@{}llll@{}}
\toprule
\begin{minipage}[b]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[b]{0.22\columnwidth}\raggedright\strut
\textbf{Life Forms}\strut
\end{minipage} & \begin{minipage}[b]{0.22\columnwidth}\raggedright\strut
\textbf{Life Forms}\strut
\end{minipage} & \begin{minipage}[b]{0.22\columnwidth}\raggedright\strut
\textbf{Life Forms}\strut
\end{minipage}\tabularnewline
\midrule
\endhead
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{Trees/Shrubs}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{Forbs}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{Climbers}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{Predictors}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{Parameter estimate (95 \% CI)}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{Parameter estimate (95 \% CI)}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{Parameter estimate (95 \% CI)}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
\emph{Fire Type (Non-prescribed)}\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
-0.051 (-0.264, 0.162)\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
-0.071 (-0.267, 0.141)\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
-0.365 (-0.672, -0.057)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
\emph{Protection Status (Non-protected)}\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
0.245 (0.008, 0.482)\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
0.077 (-0.188, 0.344)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
\emph{Biomes (FGS)}\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
-0.046 (-0.446, 0.354)\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
0.959 (0.528, 1.392)\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
--\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
\emph{Biomes (TSCF)}\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
1.010 (-0.330, 2.352)\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
-0.417 (-1.051, 0.232)\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
--\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
\emph{Biomes (TSDBF)}\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
-0.195 (-0.552, 0.161)\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
-1.272 (-1.667, -0.871)\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
--\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
\emph{Biomes (TSGSS)}\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
-0.370 (-0.875, 0.134)\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
-1.252 (-1.574, -0.921)\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
--\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
\emph{Fire Type (Non-prescribed): Biomes (FGS)}\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
--\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
2.821 (2.268, 3.362)\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
--\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
\emph{Fire Type (Non-prescribed): Biomes (TSDBF)}\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
--\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
0.380 (-0.020, 0.769)\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
--\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
\emph{Fire Type (Non-prescribed): Protection Status
(Non-protected)}\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
0.023 (-0.123, 0.171)\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
--\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
--\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{Model Explanatory Power (D\textsuperscript{2})}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{5.78 \%}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{41. 74 \%}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{5.17\%}\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image5/image5}

\end{center}
\end{figure}

\textbf{Fig} \textbf{2} .~ Impact of fire on relative species richness
(species richness in burnt sites divided by richness in the control
sites a) Trees/Shrubs (Non-Protected (n =34), Protected (n = 46), b)
Forbs: FGS (n = 5), TSCF (n = 2), TSDBF (n =12), TSGSS (n = 40), TSMBF
(n = 2) and c) Climbers (Non-prescribed (n =33), Prescribed (n =27). The
error bars represent the 95\% confidence interval. The dotted lines
represent a relative species richness of 1, i.e., equal species richness
in both burnt and unburnt plots.

\textbf{Species Turnover}

Models of turnover in species composition of tree/shrub, forb and
graminoid communities between burnt and unburnt plots consistently had
good explanatory power, which (except for forbs) was higher than
equivalent models of relative species richness (trees/shrubs: 32.99 \%,
forbs: 27.60\%, and graminoids: 64.95\%). Dissimilarity in species
composition of tree/shrub and forb communities was initially marked
(Table 3; Fig3a, 3b). Composition of forb communities had returned to
that of pre-fire communities approximately ten years after a fire (Fig.
3b), the composition of tree and shrub communities remained dissimilar
to that of pre-fire communities ten years after the fire event (Fig.
3a).

Within protected areas graminoid communities exhibited marked turnover
immediately following fire events, but these communities resembled
pre-fire communities ten tears after the fire event (Table 3; Fig. 3c).
Turnover in graminoid species composition in non-protected areas was
much lower immediately after a fire than in non-protected areas, but
this dissimilarity increased slightly over the ten years following a
fire (Fig. 3c). Species turnover in the composition of climber
communities did not appear to be associated with any of our predictor
variables as the best model had a higher AICc (79.27) than the null
model that lacked predictors (AICc 78.14).

\textbf{Table 3:~} Results from model averaging across multiple
regression models of species turnover for trees/shrubs, forbs, and
graminoids. Results for climbers are not included as no model had a
lower AICc than the null model (i.e., one that lacked predictors).
Parameter estimates are provided with 95\% confidence intervals in
brackets.\selectlanguage{english}
\begin{longtable}[]{@{}llll@{}}
\toprule
\begin{minipage}[b]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[b]{0.22\columnwidth}\raggedright\strut
\textbf{Life Forms}\strut
\end{minipage} & \begin{minipage}[b]{0.22\columnwidth}\raggedright\strut
\textbf{Life Forms}\strut
\end{minipage} & \begin{minipage}[b]{0.22\columnwidth}\raggedright\strut
\textbf{Life Forms}\strut
\end{minipage}\tabularnewline
\midrule
\endhead
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{Trees/Shrubs}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{Forbs}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{Graminoids}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{Predictors}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{Parameter estimate (95 \% CI)}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{Parameter estimate (95 \% CI)}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{Parameter estimate (95 \% CI)}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
\emph{Time Since Fire (ln transformed)}\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
-0.059 (-0.117, -0.001)\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
-0.155 (-0.235, -0.076)\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
-0.102 (-0.160, -0.044)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
\emph{Protection Status (Non-protected)}\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
--\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
--\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
-0.027 (-0.141, 0.084)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
\emph{Time Since Fire (ln): Protection Status (Non-Protected)}\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
--\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
--\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
0.115 (0.051, 0.176)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{Model Explanatory Power (D\textsuperscript{2})}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{32.99 \%}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{27.60 \%}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{64.95 \%}\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image7/image7}

\end{center}
\end{figure}

\textbf{Fig 3} .~ Impact of fire on species turnover between burnt and
unburnt sites on a) Trees/Shrubs, (n = 84) b) Forbs (n = 61), and c)
Graminoids: Non-protected (n = 40), Protected (n =16). Each points
represents the no. of sites. The X axis is plotted on a log scale. The
shaded area represents the 95\% confidence interval.

\textbf{Discussion~}

Fire has played an important role in shaping tropical biodiversity for
millennia (Kelly et al., 2020), with studies reporting contrasting
impacts on biodiversity (Kelly et al., 2020; He et al., 2019). To
explain this heterogeneity, we analysed a systematic compilation of data
quantifying species richness and community composition responses to fire
in tropical communities of major plant life forms. Our analyses account
for variation across biomes and quantify responses to time since fire
and fire type (prescribed or non-prescribed burns) and assess if
protected area status modifies plant community responses.

Despite conducting a comprehensive literature search, we only found 28
studies that met our data analysis requirements. There is thus a clear
need for additional empirical fieldwork that assesses plant community
responses to fire; such studies should follow the open science
principles of making underlying datasets freely available to facilitate
meta-analyses. Our focal studies included ones that assessed
biodiversity recovery up to twenty-nine years following fire events, but
most studies were conducted within ten years of a fire event. Given that
we find plant community composition can remain impacted by fires ten
years after they occur (see below discussion) there is a particular need
for long-term (\textgreater{} ten years) longitudinal studies. Our
results indicate that changes in species richness and recovery of
community composition following fire events vary across plant life
forms. This heterogeneity across life forms suggests that apparent
contradictions in the published literature regarding the impacts of fire
on plant communities may arise from variations in which life forms
dominate the focal plant communities. We thus encourage future studies
to take this into account in their study design and interpretation.
Despite the limitations of data availability and duration of studies our
analyses provide important novel preliminary insights regarding
biodiversity responses and recovery from fire events.

Variation across biomes

Some biome types are poorly represented within our dataset, further
underlining the need for additional field studies. Our analyses,
however, reveal divergent responses of forb species richness to fire
events depending on the biome in which they are located, with these
relationships being moderated by fire type (prescribed burn or
non-prescribed burn) that may be related to the biome's historical
exposure to fire (see below discussion of fire type effects). Species
richness and community composition of other life forms exhibited similar
responses to fire across our focal biome types. Our results thus support
evidence that the increasing frequency of fire events across the tropics
can influence plant communities and other taxonomic groups across a wide
range of habitat types (Feng et al., 2021; Kodandapani et al., 2004),
including ones which traditionally experience fire (Andersen et al.,
2005, Corey et al., 2019).

Protection Status

The role of protected areas in limiting adverse impacts of fire on
biodiversity is often considered to focus on reducing fire risk rather
than mitigating impacts once fire occurs (Eklund et al., 2022; Kearney
et al., 2020). Indeed, there is concern that fire suppression in
protected areas can result in substantial accumulation of flammable
material that increases adverse ecological consequences of fires when
they arise and encourage the formation of communities that are more
sensitive to fire than areas lacking protection (De Groot et al., 2009;
Pereira et al., 2012). We find, however, that protected areas have a
stablishing influence that can limit the magnitude of fire induced
changes in plant communities. Species richness of trees/shrubs increased
(by \textasciitilde{}50\%) following fires in non-protected sites but
tended to remain similar in protected sites. Similarly, graminoid
community composition resembled that occurring at unburnt control sites
approximately ten years after a fire event in protected sites, yet in
unprotected sites graminoid communities remained highly divergent from
those in control sites ten years after a fire. Whilst tropical protected
areas are not always managed as effectively as possible (Laurence et
al., 2012), our results suggest that protected areas can reduce the
impacts of fire on tropical plant communities and promote more rapid
recovery due to having low anthropogenic pressure (Geldmann et al.,
2019) and suitable ecological condition for diverse community (Gray et
al., 2016). Accelerated recovery will be expected if unburnt areas
within protected sites enable faster re-colonisation of burnt patches
than in unprotected landscapes in which the distance to large intact
habitat patches is greater (Gray et al., 2016). Faster recovery in
protected areas may also arise due to protection from subsequent human
activity following fire events, such as increased grazing, hunting,
logging, collecting firewood etc. (Andam et al., 2008), that enables
faster recovery.

Effects of fire type

Previous studies reported that non-prescribed burns, specifically
wildfires, and prescribed burns have equivalent effects on plant
communities (Ffolliott et al., 2012; Pastro et al., 2011) and survival
(Nesmith et al., 2011). Our analyses provide rare evidence that
prescribed and non-prescribed fires can have divergent impacts on plant
communities.

Prescribed burns increased climber species richness, whilst
non-prescribed burns tended to reduce climber species richness. These
results extend previous work suggesting reduced species richness of
climbers in burnt compared to unburnt plots, irrespective of the fire
type (e.g., Addo-Fordjour et al., 2020; Balch et al., 2011). Climbers
can play a key role in vegetation dynamics; for example, fire-resilient
lianas can protect trees from further fire (Uhl et al., 1988);
conversely, lianas are often associated with reduced tree growth rates
and higher subsequent mortality (Becknell et al., 2022; Finlayson et
al., 2022). Given these roles of climbers in vegetation dynamics, and
the use of prescribed burns as a conservation tool to reduce the
probability of larger, more intense fires, it is important to understand
the mechanisms driving the difference in impacts of prescribed and
non-prescribed burns. Climbers proliferate in tropical habitats when
disturbance events increase light levels or soil nutrients (Magnago et
al., 2017), which will happen following a fire. It is plausible that
prescribed burns enable this proliferation to occur, increasing climber
richness, but the greater intensity of non-prescribed fires (Marshall et
al., 2020) limits such proliferation. We also found evidence that in
moist broad-leaved forest, prescribed burns increased forb species
richness whilst non-prescribed burns had negligible impact on it. Such
patterns may also be driven by prescribed burns beneficially altering
abiotic conditions for forbs, but greater intensity of non-prescribed
burns preventing forb communities from benefitting from these
conditions. It is unclear why the opposite pattern, increased forb
richness following non-prescribed burns and negligible impact of
prescribed burns, occurs in flooded grasslands and savannas - although
it may be linked to such biomes having greater historical exposure to
fire.

Effects of time since fire

Whilst we find no effects of time since fire on species richness, the
species composition of tree/shrub, forb and graminoid communities
changes markedly following a fire. Increased species turnover
immediately after the fire is likely to be due to loss of species due to
mortality or their inability to tolerate novel environmental conditions
(including micro-climates, nutrient and light levels), and the
recruitment of new species that favour the altered environmental
conditions (Doherty et al., 2017; Kaewsong et al., 2022; Keeley et al.,
2005). Biodiversity should gradually recover after a fire event and
increasingly resemble the pre-fire community (Machida et al., 2021). In
forb communities, recovery tends to be close to completion ten years
after a fire event, but recovery of tree/shrub community composition is
incomplete after ten years. Moreover, there is no evidence for any
recovery in graminoids communities outside protected sites after ten
years. The long-term persistence of these compositional changes is
probably driven by multiple factors including the long-term legacy of
altered nutrient availability post-fire (Verma et al., 2019), fire
induced reductions in tree growth rates (Bucini \& Hanan, 2007), and
(especially outside protected areas) altered land-use patterns following
fire events (Butsic et al. 2015). Our results underline the need to
avoid fire for at least ten years to allow forb communities to recover,
and longer for trees/shrubs -- and to protect burnt locations from human
activities that could disturb regenerating vegetation. It is thus
notable that fire return rates in many tropical areas are already
shorter than ten years (Archibald et al. 2013), and in some locations,
such as central highlands of Vietnam, have increased in recent decades
primarily due to changes in human activity (Nguyen et al. 2023). Indeed,
the number of fires in the tropics has increased at \textasciitilde{}5\%
per annum since 2001 (Tyukavina et al., 2022), and are projected to
increase further due to both climate change and human activity across a
wide range of tropical regions and biomes (Wu et al., 2021; Li et al.,
2023). There is thus increasing probability that fires will fire return
intervals will decrease to the extent that plant communities will be
unable to recover community composition before the next fire occurs,
especially in the drier tropical biomes that currently have the shortest
fire return intervals (Archibald et al. 2013).

\textbf{Conclusions}

Our data compilation and analysis of tropical/sub-tropical plant
community responses to fire generates important findings that inform
knowledge of fire impacts and mitigation strategies and help shape
future research agendas. Despite increasing awareness of changing
tropical fire regimes, limited studies address plant community responses
to key fire features, and long-term longitudinal studies that can
quantify recovery times are particularly scarce. More focused research
is needed to assess how recovery rates are influenced by landscape
composition and configuration. We uncover considerable heterogeneity
across plant life forms in their responses to fire metrics and encourage
researchers to consider this when reporting fire impact studies. Our
research makes four important contributions. First, we uncover evidence
that fire impacts on species richness and recovery of community
composition can vary with protection status, with protected areas
appearing to be able to buffer some plant communities from fire-induced
changes. Second, we find that prescribed and non-prescribed burns can
vary in their impacts on plant communities, and this should be
considered when designing prescribed burning strategies to reduce the
risk of larger, more intense non-prescribed fires. Third, there were no
differences in fire impacts between fire-adapted and fire-sensitive
biomes regarding species richness and community composition of life
forms, except in the forb's community. Finally, and most importantly, we
find major shifts in species composition of plant communities, which are
often detectable ten years after a fire. Tropical/sub-tropical plant
communities thus appear particularly vulnerable to compositional changes
from the observed and projected future increases in fire frequency that
reduce recovery time between fire events.

\textbf{Acknowledgements:}

Oscar Morton and Jocelyne Sze helped with R coding.

\textbf{Conflict of interest disclosure:}

We have no conflicts of interest to disclose.

\textbf{Data availability statement:} All data included in analyses are
available in the Supporting Information.

\textbf{Author contributions:}

\textbf{Dharma Sapkota:} Conceptualization (equal); Data curation
(lead); Formal analysis (lead); Methodology (equal); Visualization
(lead); Writing -- original draft (lead). \textbf{David P. Edwards} :
Conceptualization (equal); Data curation (supporting); Formal analysis
(supporting); Methodology (supporting); Supervision (equal);
Visualization (supporting); Writing -- review \& editing
(equal).\textbf{Mike Massam} : Formal analysis (supporting); Methodology
(supporting); Visualization (supporting); Writing -- review \& editing
(supporting). \textbf{Karl Evans} : Conceptualization (equal); Data
curation (supporting); Formal analysis (supporting); Methodology
(equal); Supervision (equal); Visualization (supporting); Writing --
review \& editing (equal).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Fig-1/Fig-1}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Fig-2/Fig-2}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Fig-3/Fig-3}

\end{center}
\end{figure}

\textbf{Hosted file}

\verb`Table 1.docx`  available at \url{https://authorea.com/users/641884/articles/655934-a-pantropical-analysis-of-fire-impacts-and-post-fire-recovery-on-tropical-plant-diversity-and-species-composition}

\textbf{Hosted file}

\verb`Table 2.docx`  available at \url{https://authorea.com/users/641884/articles/655934-a-pantropical-analysis-of-fire-impacts-and-post-fire-recovery-on-tropical-plant-diversity-and-species-composition}

\textbf{Hosted file}

\verb`Table 3.docx`  available at \url{https://authorea.com/users/641884/articles/655934-a-pantropical-analysis-of-fire-impacts-and-post-fire-recovery-on-tropical-plant-diversity-and-species-composition}

\selectlanguage{english}
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\end{document}

