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\begin{document}

\title{Local stochastics and ecoclimatic situation shape phytophagous chafer
assemblage composition}



\author[1]{Sasanka Ranasinghe}%
\author[2]{Jonas Eberle}%
\author[3]{Suresh Benjamin}%
\author[1]{Dirk Ahrens}%
\affil[1]{Zoological Reserch Museum Alexander Koenig}%
\affil[2]{Paris Lodron University of Salzburg}%
\affil[3]{National Institute of Fundamental Studies}%


\vspace{-1em}



  
  \date{May 24, 2024}


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\selectlanguage{english}
\begin{abstract}
Very little is known about factors determining the assemblage structure
of megadiverse polyphagous-herbivore scarab chafers in the tropics
(Coleoptera: Scarabaeidae). Here, we examined the composition of Sri
Lankan chafer assemblages and investigated whether it is influenced more
by the general ecoclimatic situation, macrohabitat, of indetermined
stochastic biotic and abiotic factors of each locality. We also explored
the influence of the latter on separate lineages and general body size.
Based on dedicated field surveys conducted during the dry and wet
seasons, we examined 4847 chafer individuals of 105 species sampled
using multiple UV-light traps in 11 localities covering different forest
types and altitudinal zones. Assemblages were assessed for compositional
similarity, species diversity, and abundance within four major
eco-spatial partitions: forest types, elevational zones, localities, and
macrohabitats. Our results revealed that assemblages were shaped mainly
by locality stochastics, and to a minor extent by ecoclimatic
conditions. Macrohabitat had little effect on the assemblage
composition. This was true for the entire chafer assemblage as well as
for all single lineages or different body size classes. However, in
medium and large specimens the contrasts between localities were less
pronounced, which was not the case for individual lineages of the
assemblage. Contrasts of assemblage similarity between localities were
much more evident than those for forest types and elevation zones.
Significant correlation between species composition and geographic
distance was found only for the assemblage of small-bodied specimens.
Seasonal change (dry-wet) in species composition was minor and only
measurable in a few localities.%
\end{abstract}%



\sloppy


\textbf{Introduction}

Analysing biodiversity patterns is fundamental to understanding the
underlying processes and causes of diversification (Holt et al. 2013).
In comparison to plants and vertebrates, arthropods are fragmentarily
known and lack comprehensive comparative data (Deca\selectlanguage{ngerman}ëns 2010; Beck and
Kitching 2007; Stork et al. 2015; Nielsen 2019). This is particularly
true for biodiversity hotspots (Myers et al. 2000). Restricted dispersal
capacities of arthropods (Gálvez-Reyes et al. 2020) and their occurrence
in micro-niches result in fine scale high endemism and still unknown
patterns (Buckley and Jets 2007; Daru et al. 2020; Baselga et al. 2022).
However, data on arthropod biodiversity rely on museum collections and
suffer largely from sampling bias (Santos and Quicke 2011; Echevarría
Ramos and Hulshof 2019). This is true for even relatively large-bodied
taxa such as phytophagous scarab beetles (Coleoptera: Scarabaeidae).

Diversity patterns of phytophagous beetles are known to be generally
linked to species turnover of their host plants (Ødegaard 2006; Kemp et
al. 2017; Luo et al. 2021) and their distribution is correlated with the
region and forest type (Yotkham et al. 2021). Other guilds, such as dung
feeding beetles, respond to shade cover rather than plant species
composition. Further, their occurrence and relative abundance vary
according as responses to microclimate (light intensity, temperature,
humidity) (Davis et al. 2013) or other factors (rainfall, temperature,
and host density/diversity) varying from regional to local scale in
relation to actual local functional ecological conditions (Tshikae et
al. 2013). Such correlation of occurrence and abundance with
environmental conditions, suggest that a strong role of lineage- or
species-specific traits such as dispersal capabilities or body size
determines local community composition (Murria et al. 2017). Insect body
size is modulated by many climatic factors along species ranges,
especially when they are distributed across climatic gradients at large
spatial scales (Lira et al. 2021; Romero et al. 2016; Brehm et al.
2019). Changes in body size may affect fertility, lifespan, population
dynamics, and species composition (Garcia-Robledo et al. 2020).

In contrast to most other herbivore insects being rather host-specific,
phytophagous scarab chafers (Coleoptera: Scarabaeidae), with ca 30,000
extant species worldwide, feed unspecifically on leaves of a vast
variety of angiosperm plants as adults, on soil humus or roots as larvae
(Ritcher 1958). They have had a very successful follow-up evolution with
angiosperms (Ahrens et al. 2014). However, very little is known about
their actual assemblages responding to habitat differences (Eberle et
al. 2017). since only few studies have comparatively investigated their
quantitative composition (Ahrens et al. 2009; García Lopez et al. 2010,
2013), and often these studies include either only a part of the
assemblage (Ahrens et al. 2007), and/or consider separate localities
rather than habitats (Ahrens et al. 2009; García-Lopez et al. 2013).

To close this gap, we investigated here patterns of species diversity
and turnover in tropical phytophagous chafers in Sri Lanka, a global
biodiversity hotspot (Myers 2000) across different forest types,
elevation zones, localities, and habitats. We attempt to explore to
which extent each of these spatial components determines assemblage
composition. In this context, we also assessed their influence on
different lineages and the role of body size in shaping species
composition. If body size (as proxy of dispersal capability) had an
impact on assemblage composition, we would expect contrasting patterns
between entities of different spatial scales between smaller and larger
species assemblies, also in respect to phylogenetically partitioned
assemblages. This way, we expect to elucidate the dynamics of community
assembly and differentiation and to explain the high species richness
and endemism in tropical chafers.

\textbf{Materials and Methods}

\emph{Study area and sampling}

Four field expeditions were conducted during 2019 and 2020
(February-March/ October-November and June-July/ November-December,
respectively) during dry and rainy seasons. Specimens were sampled using
six UV-light traps (Ranasinghe et al. 2020) in 11 localities covering
different forest types and altitudinal zones of Sri Lanka (Figure S1).
Sites are in evergreen wet lowland forests (below 500m: L1, L8, L9; or
above 500m: L12, L13, L14), evergreen dry lowland forest (L3),
sub-montane forests (L2, L4), or montane forests (L5, L11) (Figure S1,
Table S1). Traps were placed in each locality at different sampling
sites (i.e., macrohabitats) at approximately 2 m above ground (Table
S1). They were positioned at the same location for 2--3 consecutive days
and operated from 6 pm to 11 pm. All traps (traps A-F) were separated by
at least 100--500m, to not influence each other. Beetles were trapped in
a sampling container with preservation liquid (96\% ethanol) (for trap
design, see Ranasinghe et al. 2020; 2022). Specimens were preserved in
96\% ethanol. In total, we performed 10-12 trapping events per
expedition/site, resulting in 60-72 trapping events in each location.

Phytophagous chafers (Coleoptera: Scarabaeidae) in Sri Lanka include
Dynastinae, Melolonthinae, and Rutelinae. Specimens were identified to
morphospecies based on external and genital morphology, some being
subsequently examined by a specialist. Specimens are deposited in the
Zoological Research Museum A. Koenig, Bonn (ZFMK) and in the National
Institute of Fundamental Studies, Kandy, Sri Lanka (NIFS).

\emph{Assemblage characterization}

Species richness and abundance was assessed by the mean number of
species or individuals respectively, per trapping event and site (i.e.,
total number of specimens per species of a particular trap divided by
number of used traps). Thus, abundances were corrected for sampling
biases due to trap failures because of weather or technical problems.
Species presence-absence data were used for the assessment of species
composition and assemblage similarity. Species accumulation curves were
plotted for each trap with the cumulative number of recorded species vs.
number of cumulative trapping events to assess sampling adequacy and
comparability of the results. Inventory completeness in each sampling
locality was measured by the number of observed species in respect to
the number of species predicted by the Chao1 richness estimator, i.e.,
the total number of species in each locality with lower and upper limits
(Chao and Lee 1992; Zou and Axmacher 2021). Sampling data (i.e.,
specimens per trap) were pooled for each trap from all four sampling
campaigns (2019 I, 2019 II, 2020 I, 2020 II) for total assemblage
analyses. A two-way cluster analysis (species vs locality) was performed
based on presence-absence data using the Jaccard similarity index
(Jaccard, 1912) in PAST v. 3.25 (Hammer et al. 2001).

The alpha diversity was measured using Shannon index, Simpson index and
Evenness for each locality (Shannon 1948; Simpson 1949; Hill 1973) being
calculated in PAST v. 3.25. Approximate confidence intervals for all
these indices were computed with a bootstrap procedure (number of random
samples (default 9999) with 95 \% confidence interval).

\emph{Assemblage assessment partitioned by body size and lineages}

Differences in body size may reflect divergences in species ecology and
behaviour (Inward et al. 2011; Eberle et al. 2014; Lira et al. 2021).
Thus, size related differences in assemblage composition across
different spatial scales may provide insight to the causalities of these
patterns. Therefore, assemblages were analysed according to these three
body size groupings: 1) smaller 7 mm; 2) medium 7-15 mm; 3) larger 15
mm. The respective total body length was calculated using the sum of
pronotal and elytral length (PL+EL). The mean total body length of a
species was determined by taking the mean value of 3-5 individuals of
the same species. Alternatively, assemblage composition analyses were
partitioned according to phylogenetic lineages (following McKenna et al.
2021): Dynastinae, Rutelinae, Melolonthinae (excluding Sericini), and
Sericini to explore also phylogenetic patterns of differences in
assemblage composition (Smith et al. 2021).

\emph{Spatial turnover analysis}

Non-metric multidimensional scaling analyses (NMDS) based on
presence-absence data using the Jaccard similarity index were performed
for four major spatial components (i.e., forest types, elevational
zones, localities, and habitats). For this purpose, each single trap was
assigned for a particular spatial component (Table S1). Forest types
included four entities: a) evergreen wet lowland forests, b) evergreen
dry lowland forests, c) sub-montane forests, and d) montane forests.
Elevation had five units: EZ1: 0--500~m, EZ2: 501--1000~m, EZ3:
1001--1500~m, EZ4: 1501--2000~m, and EZ5: 2001--2500~m. The entity
`locality' included the 11 individual sampling localities. Habitat
comprised seven types: abandoned plantation, grassland, rock outcrop,
hilltop, forest edges, central forest, and disturbed forest. NMDS
ordination was performed on the full data set. Entities of spatial
components (i.e., forest types, elevational zones, localities, and
habitats) were subsequently mapped on the ordination results. Spatial
turnover analysis as well as a regression between qualitative species
composition similarity and geographic distances of sampling sites were
performed for the full assemblage and for assemblages partitioned by
body-size and phylogenetic lineage membership (see above).

\emph{Seasonal turnover analysis}

We assessed seasonal turnover for single traps and localities using NMDS
ordinations based on Jaccard indices from species presence-absence data.
The turnover of species composition in time was also evaluated for the
localities through ANOVA and Kruskal-Wallis tests as implemented in
PAST. Finally, we compared also seasonal turnover for lineage- and
body-size partitioned assemblage data.

\textbf{Results}

A total of 4847 specimens of 105 chafer species belonging to Rutelinae,
Melolonthinae, and Dynastinae were recorded (Table S2). Species richness
estimators suggested \textgreater{}89\% of total species inventory had
been captured. While 82\% of the individual locality assemblages showed
more than 84\% of sampling completeness (in terms of species
composition), in two cases sampling completeness was, with less than
50\%, quite low (L9, L14) (Table S3). Species accumulation curves for
individual localities showed that about 80\% of its species have been
captured in less than half of the total trapping events (before
34\textsuperscript{th} trapping event) (Figure S2). Similarly, species
accumulation curves for individual traps showed that about 80\% or
slightly more of the expected species has been captured before half of
the total trapping events.

Melolonthinae was the most speciose subfamily (n=79), with the highest
number of recorded individuals (n=2504). Dynastinae had the lowest
number of species (n=8) and individuals (n=38). For Rutelinae we
recorded 18 species in 531 exemplars. Among the Melolonthinae, Sericini
was the most speciose tribe accounting 44.7\% of all species (Figure 1).
Many species were geographically restricted, 67 species out of 105 (64\%
of total assemblage) were found exclusively at just one site. L3 showed
the highest alpha diversity and L13 the lowest (Table S3). These
patterns are also reflected by the results of the two-way cluster
analysis, one for the species occurring in different localities, and
another for the different localities in which certain species are
present (Figure 2) which linked faunal similarity with similar species
occurrence patterns.

\emph{Spatial turnover}

Ordination analysis on species presence/absence data (NMDS) of the full
chafer assemblage generally showed different patterns for the different
eco-spatial components (Figure 3A, F, K, P). The largest overlap of
entity clusters was observed for the macrohabitats. Overlap in forest
types, elevation zones, and localities were limited to a few entities.
Further, most entities were well-separated. The distances between the
entity clusters were almost similar within the same spatial component.
Similar patterns were also observed for separate lineages, however,
differences between the single entities (e.g., elevation zones or forest
types) were less pronounced with slightly larger overlaps. For
Dynastinae, patterns were not well pronounced due to low sample
representation (Figure 3). Species composition in localities of montane
forests (L5, L11) resulted generally more similar to each other (Figure
3), while assemblages of dry lowland forest were dissimilar for single
lineages. There was an overlap for the full assemblage analysis.

NMDS on Jaccard indices from species presence-absence data for the three
different body size classes showed similar overall patterns: large
overlap for all partitions in macrohabitats, and moderate to clear
distinction for ecoclimatic zones (elevation, forest type) and
localities. Small and medium-sized assemblages showed somewhat
contrasting patterns for assemblages of large-bodied specimens for
forest types, elevation zones and localities (Figure 4). Again,
eco-spatial entities (e.g., elevation zones, or forest types) in
partitioned analyses were less different than for the full assemblage
data (Figure 4).

A linear regression analysis showed no significant correlation (r=
-0.029, p= 0.831) between species composition similarity and geographic
distance among localities (Figure 5). We also tested for this
correlation for the assemblages partitioned by body size and lineages
(Table S4); a significant dependence between species composition
similarity and geographic distance was found only for the assemblage of
small-bodied specimens (r= -0.344, p= 0.02).

\emph{Seasonal turnover}

Species number and abundance varied significantly between the four field
campaigns (ANOVA, p\textless{}0.01) (Figure 1B). Patterns of species
turnover among single traps between the sampling campaigns were not
homogeneous for different localities. Some localities showed very little
difference between all campaigns, some had strong differences between
all, and in some cases only one or two differed in species composition
(Figure 6). For pooled data for a single locality and field campaign,
seasonal species turnover of localities varied between 19--61\% (Table
S5). Kruskal-Wallis test for individual localities showed that L1, L2,
L3, L9 had significant seasonal species turnover, while other localities
did not show any significant species turnover between the seasons (Table
S5). Among our four field campaigns, February (2019, I) and December
(2020, II) campaigns showed the highest faunal similarity (i.e., 49.2\%)
and lowest similarity (17\%) was found between campaigns of October
(2019II) and June (2020I) (Table S6). Faunal similarity among campaigns
varied for lineage and body size partitioned assemblage data, which
showed higher similarity for Melolonthinae and Sericini as well as
small-sized specimens compared to the complete assemblage, in all other
less faunal similarity compared to the latter (Table S6).

\textbf{Discussion}

We investigated here for the first time components determining the
chafer assemblage composition, comparing the impact of ecoclimatic
influences with macrohabitat and locality stochastics on the similarity
of investigated entities. Locality stochastics represent a not further
investigated multi-factor ensemble that includes all biotic and abiotic
environmental conditions at local scale such as macrohabitat,
biogeography, edaphic conditions, land use, predation, local climate,
rainfall, radiation, and others. We also explored the patterns of
lineage membership and body size resulting from assemblage composition
across larger scale entities (forest type, elevation) versus smaller
scale entities (localities, macrohabitats).

The comparison of chafer assemblage composition at different eco-spatial
scales revealed that assemblages were shaped mainly by locality
stochastics, to minor extent to the ecoclimatic conditions, and not by
macrohabitat. This was true for the entire chafer assemblage, as well as
single lineages or different body size classes. NMDS plots of faunal
similarity showed the largest overlap among macrohabitat entities. In
contrast to that, overlap for clusters of forest types, elevation zones,
and localities were limited. However, contrasts between localities were
less pronounced in medium-sized and large specimens.

Investigated macrohabitats were quite different (e.g., forest,
grassland, abandoned plantations). They are known to provide multiple
niches (Bosc et al. 2019) for chafer species, however, only a few
species were recorded that were specific to these habitats. Most species
and resulting assemblages sorted rather by locality rather than by
macrohabitat. This could be partly explained by the trapping method
(light traps) used, as fully winged chafer beetles may be attracted from
other habitats over certain distances within the same locality. However,
the fact that we found no correlation between species composition (for
total assemblage) and geographic distance (Figure 5), even for adjacent
localities situated in the same forest type also in the same mountain
range (e.g., L2, L4), may indicate either that species generally might
tend to disperse also over moderate to longer distances or that species
disperse very little. Limited dispersal is supported further by
molecular evidence (Ranasinghe et al. in review), since different, the
same here investigated localities shared almost no haplotypes. This
latter conclusion would be not surprising as previous studies have also
shown high turnover rates of assemblage composition at higher elevations
independently from geographical distance (García Lopez et al. 2010).
However, the resulting significant correlation for the assemblage of
small-bodied specimens, which is definitively linked to their limited
dispersal capacity and mirrored by their higher endemism (Fabrizi and
Ahrens 2014), might indicate that lacking significance on our study
might be a result of an insufficient number of samples and species.
Larger species were generally less common and are also less represented
in higher altitudes. Influence from palaeogeographical and
biogeographical factors should also be considered in this context (Kemp
et al. 2017) as several sampling localities are situated in the central
highlands within complex mountain systems (escarpments, ridges, or
peaks) which can act as geographical barriers. The latter can
particularly triggered geography-driven speciation, as shown by
diversification of Sericini in Asian mountains (Ahrens 2007; Eberle et
al. 2016).

Some of the divergent composition patterns retrieved for the full
assemblage (Figure 3A, F), which are in turn not encountered for any of
the single lineages, reveal that occurrences of entire lineage members
may also impact on the apparent differentiation (e.g., wet lowland
forest vs. submontane forest, EZ 1 vs EZ 2). The latter case is caused
by the more poorly sampling/ absence of larger-bodied species (e.g.,
Dynastinae), in higher elevations, since low temperatures obviously
might not favour larger species with long larval development (Danks
1992). In fact, even in mountain ranges with larger amplitudes of
elevations, the altitudinal differentiation of the fauna in phytophagous
chafers is rather poor (Ahrens 2004) compared to other insects (Mani
1968).

The strong turnover for localities is in line with the rather high
degree of endemism in many phytophagous scarabs (Ahrens and Fabrizi
2016), despite their considerable size. Their assemblage patterns across
local spatial scales can be explained not only by poor dispersal
capacities, but also by short emergence times compared to the length of
their life span: their root feeding, endogenous larvae do not disperse.
Their emergence during early night-time often falls together with heavy
monsoon rains which narrows down the time window for potential dispersal
flights.

Other lineages composed of larger species, such as Dynastinae, have
greater dispersal ability compared to smaller Rutelinae and
Melolonthinae (García Lopez et al. 2013), and this has an impact on the
faunal divergence pattern of assemblages as revealed by pronounced
larger cluster overlaps across different spatial scales.

Seasonality and weather fluctuation may strongly impact the expressed
patterns of assemblage composition in ecofaunistic analyses (De Oliveira
et al. 2021). In tropical climate, rainy seasons and dry seasons are
alternating in shorter intervals with quite constant temperature and
humidity throughout the year and food resources being continuously
available. Thus, minor fluctuations to species' presence and numbers may
occur even in the tropical ecosystems. Many of our localities (except
L1-L3, L9) did not show a significant seasonal species turnover, while
those which did experience generally stronger dry-wet fluctuations than
other localities according to their position in the island.

In final conclusion, we need to remember that at local level all
ecological, climatical, and spatial components sum up in their effect
increasing the complexity of influences on the assemblages. This points
the way for future, more detailed studies, in which localities of
similar eco-spatial situations shall be addressed. Yet, since
phytophagous chafers are for many tropical crops common pests, and
damage can often also be caused by a multispecies autochthonous
community with endemic species, (Ahrens et al. (2009), we need more
knowledge here, as this might positively affect simultaneously pest and
biodiversity management.

\textbf{Conflict of Interest}

We have no conflicts of interest to declare.

\textbf{Data Availability Statement}

All supporting data is made freely available in Dryad and as
supplementary information.

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\textbf{Figure Legends}

\textbf{Figure 1} : Total number of species (species richness) in
different locations and in four field campaigns; \textbf{A, B} . based
on subfamily level/separate lineages; \textbf{C, D.} assemblage sorted
for body size.

\textbf{Figure 2} : Dendrogram from species presence data. Results of a
UPGMA clustering of localities is shown at the lower left of the figure,
with 1000 bootstraps. Black square: Presence; White square: Absence.

\textbf{Figure 3} : NMDS analyses of assemblages from single trapping
events separated by lineages and different spatial and eco-spatial
partitions; forest types (\textbf{A-E} ), elevation zones (\textbf{F-J}
), localities (\textbf{K-O} ) and habitats (\textbf{P-T} ). Partitions
are enclosed by convex hulls. Multiple traps from one locality have the
same colour and colours correspond to Figure S1.

\textbf{Figure 4} : NMDS analyses assemblages separated by body size
classes and different spatial and eco-spatial partitions; forest types
(\textbf{A-C} ), elevation zones (\textbf{D-F} ), localities
(\textbf{G-I} ) and habitats (\textbf{J-L} ). Partitions are enclosed by
convex hulls. Multiple traps from one locality have the same colour and
colours correspond to Figure S1.

\textbf{Figure 5:} Correlation between species compositional similarity
(and pairwise geographic distance. \textbf{A} . assemblage sorted for
body size; \textbf{B} . assemblage sorted for lineages.

\textbf{Figure 6:} NMDS analyses of assemblages from single trapping
events separated by sampling locality in the course of four field
campaigns (2019~I, 2019 II, 2020~I, 2020~II).

\textbf{Hosted file}

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