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\begin{document}

\title{Presence of the Eucalyptus snout beetle in Ecuador and potential
invasion risk in South America}



\author[1]{Verónica Crespo-Pérez}%
\author[2]{J. Angel Soto-Centeno}%
\author[3]{C. Miguel Pinto}%
\author[1]{Ana Avilés}%
\author[1]{Washington Pruna}%
\author[1]{Claudia Terán}%
\author[1]{Álvaro Barragán}%
\affil[1]{Pontificia Universidad Catolica del Ecuador}%
\affil[2]{Rutgers University}%
\affil[3]{Charles Darwin Research Station}%


\vspace{-1em}



  
  \date{June 14, 2024}


\begingroup
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\selectlanguage{english}
\begin{abstract}
\emph{Eucalyptus} snout beetles (Curculionidae: \emph{Gonipterus
scutellatus} complex), native to mainland Australia and Tasmania,
defoliate \emph{Eucalyptus} trees and are considered important pests.
Since the 19th century, species of the \emph{G. scutellatus} complex
have been introduced to other continents. Here, we document the presence
of \emph{Eucalyptus} snout beetles in Ecuador and use ecological niche
models to analyze their potential distribution in South America.
Phylogenetic analyses of DNA sequences unambiguously demonstrated that
the Ecuadorian specimens belong to the species \emph{G. platensis},
which has low genetic diversity compared with other species in the
complex. Ecological niche models revealed several areas of high to
intermediate climatic suitability for the pest in South America, even in
countries where it has not been registered, like Peru and Bolivia.
Accurate identification of species in the \emph{Gonipterus scutellatus}
complex and understanding of their potential distribution are essential
tools for improved management and prevention tactics.%
\end{abstract}%



\sloppy


\textbf{Presence of the \emph{Eucalyptus} snout beetle in Ecuador and
potential invasion risk in South America}

Ver\selectlanguage{ngerman}ónica Crespo-Pérez\textsuperscript{1}*, J. Angel
Soto-Centeno\textsuperscript{2,3}, C. Miguel Pinto\textsuperscript{4},
Ana Avilés\textsuperscript{1}, Washington Pruna\textsuperscript{1},
Claudia Terán\textsuperscript{5}, Álvaro Barragán\textsuperscript{1}*

\textsuperscript{1}Laboratorio de Entomología, Museo de Zoología QCAZ I,
Escuela de Ciencias Biológicas, Pontificia Universidad Católica del
Ecuador, Av. 12 de Octubre 1076 y Roca, Apartado: 17-01-2184, Quito,
Ecuador

\textsuperscript{2}Department of Earth \& Environmental Sciences,
Rutgers University, Newark, NJ 07102

\textsuperscript{3}Department of Mammalogy, American Museum of Natural
History, New York, NY 10024

\textsuperscript{4}Charles Darwin Research Station, Charles Darwin
Foundation, Puerto Ayora, Galápagos, Ecuador.

\textsuperscript{5}Laboratorio de Biología Molecular, Herpetología,
Escuela de Ciencias Biológicas, Pontificia Universidad Católica del
Ecuador, Av. 12 de Octubre 1076 y Roca, Apartado: 17-01-2184, Quito,
Ecuador

* Corresponding authors: Email: mvcrespo@puce.edu.ec,
arbarragan@puce.edu.ec. VC-P and AB should be considered joint senior
authors

\textbf{Abstract}

\emph{Eucalyptus} snout beetles (Curculionidae: \emph{Gonipterus
scutellatus} complex), native to mainland Australia and Tasmania,
defoliate \emph{Eucalyptus} trees and are considered important pests.
Since the 19th century, species of the \emph{G.}
\emph{scutellatus}complex have been introduced to other continents.
Here, we document the presence of \emph{Eucalyptus} snout beetles in
Ecuador and use ecological niche models to analyze their potential
distribution in South America. Phylogenetic analyses of DNA sequences
unambiguously demonstrated that the Ecuadorian specimens belong to the
species\emph{G. platensis} , which has low genetic diversity compared
with other species in the complex. Ecological niche models revealed
several areas of high to intermediate climatic suitability for the pest
in South America, even in countries where it has not been registered,
like Peru and Bolivia. Accurate identification of species in the
\emph{Gonipterusscutellatus} complex and understanding of their
potential distribution are essential tools for improved management and
prevention tactics.

\textbf{Key words}

Ecological Niche Model, Forest pest; \emph{Gonipterus platensis} ;
invasive species

\textbf{Introduction}

\emph{Eucalyptus} trees (Myrtaceae) were initially introduced in South
America in the early and mid-19th century to meet increasing demands of
wood, coal, and firewood in the region (FAO, 1981). Commercial
plantations of \emph{Eucalyptus} have rapidly expanded in the last three
decades and together with pine trees, today they constitute the base of
forestry development in many countries of South America (Estay, 2020).
In Ecuador, several species of \emph{Eucalyptus} (mostly \emph{E.
globulus} ) were first introduced in 1865 largely for firewood and
construction materials (FAO, 1981). They have also been extensively
planted for reforestation and erosion control programs, making them very
common and even dominant in many landscapes of the Ecuadorian Andes
(Granda, 2006). Although \emph{Eucalyptus} trees mainly occur in the
inter-Andean valleys of the country, a massive plantation program to
produce wood chips for paper pulp was established in the early 2000 in
the coastal province of Esmeraldas. Today, \emph{E. globulus} is one of
the most important tree species in the Ecuadorian forestry sector, which
directly employs more than 230 000 people (c.a. 5.5\% of the
economically active population) and contributes around 2 to 3\% to
Ecuador's GDP (Grijalva et al., 2015).

\emph{Eucalyptus} snout beetles are a complex of cryptic species
(\emph{Gonipterus scutellatus} complex) that feed on
\emph{Eucalyptus}leaves (Schröder et al., 2020). These beetles are
native to Eastern Australia and Tasmania. Species of the complex have
been accidentally introduced into Western Australia (at an unknown
date), New Zealand in 1890, Africa in 1916, South America in 1925,
Europe in 1975 and North America in 1994. In South America, they have
been introduced in Argentina in 1925, Uruguay in 1937, Brazil in 1955,
Chile in 1998 (Estay, 2020; González et al., 2010; Marelli, 1927), and
more recently, Colombia in 2016 (Madrigal-Cardeño, 2019; Schröder et
al., 2020). The morphological similarity between the species of the
\emph{Gonipterus scutellatus} complex has led to uncertainty and
confusion regarding the identity of introduced populations (Mapondera et
al., 2012; Schröder et al., 2020). According to the most recent taxonomy
(Mapondera et al., 2012), two species of the complex are invasive in
South America:\emph{G. platensis} in Argentina, Brazil, Chile, Colombia
and Uruguay, and \emph{G. pulverulentus} in Uruguay. Correctly
identifying newly introduced populations of \emph{Eucalyptus} snout
beetles is a first essential step towards effective management
strategies.

In areas outside of their native range, \emph{Eucalyptus} snout beetles
cause severe damage to \emph{Eucalyptus} trees, with both adults and
larvae feeding on leaves and producing important economic losses
(Mapondera et al., 2012). Damage includes crown defoliation, stag-horned
or witches' broom appearance, epicormic and stunted growth, reduced tree
vigor, and loss of apical dominance (CABI, 2021; Lanfranco \& Dungey,
2001), all of which make the trees more susceptible to attack by other
organisms (Fiorentino \& Medina, 1991). Indeed, \emph{Eucalyptus} snout
beetle infestations were projected to produce between 20 to 85\% losses
in wood production over a 10-year growth period (Reis et al., 2012).
These impacts have raised repeated concern and motivated the development
of control programs with the parasitoid \emph{Anaphes nitens} (de Souza,
2016; González et al., 2010; Hanks et al., 2000; Reis et al., 2012).
However, biological control strategies have not always been successful,
mainly due to geographical or seasonal mismatch between host and
parasite (Mapondera et al., 2012; Reis et al., 2012). This highlights
the need for further study of the ecology, taxonomy and life history of
the beetles and their natural enemies, such as \emph{A. tasmaniae}
,\emph{A. inexpectatus} (Hymenoptera: Mymaridae), \emph{Entedon
magnificus} (Hymenoptera: Eulophidae), and even entomopathogenic fungi
that could potentially be used in biological control programs (de Souza,
2016; Garcia et al., 2019; González et al., 2010; Gumovsky et al., 2015;
Lobos Peirano, 2018). Finally, given that these beetles are listed as
quarantine pests by several plant protection agencies (e.g., EPPO, CPCC,
NAPPO) (CABI, 2021), and the long-term presence and uses of this tree in
forestry in South America, countries that export
\emph{Eucalyptus}products should strive to detect, adequately manage or
prevent the introduction and spread of these insects.

Identifying potentially suitable environments where introduced species
may thrive can help in mitigation and conservation planning efforts.
Potential distributions of alien species into new geographic areas can
be estimated with ecological niche models. These predictive models
provide estimates of species' potentially occupied environmental niches
based on the relationship between their occurrences and the
environmental characteristics of the landscapes where they occur
(Jiménez-Valverde et al., 2011; Peterson et al., 2011; Soto-Centeno \&
Steadman, 2015). Such procedures enable the construction of risk maps
that identify areas suitable for invading or potentially invasive alien
species and may aid in the implementation of successful biosecurity
strategies (Pili et al., 2020).

In this study, we report for the first time the presence of
the\emph{Eucalyptus} snout beetle in the city of Quito, Ecuador (Fig.
1). We suspected that these beetles belong to the species \emph{G.
platensis} due to its presence in several other countries of South
America (as opposed to \emph{G. pulverulentus} that is only present in
Uruguay) and its recently reported presence in the neighboring country
of Colombia (Schröder et al., 2020). To confirm this identification, we
sequenced the COI gene of nine specimens collected in different sites in
Quito and inferred a phylogeny using additional published sequences.
Additionally, under an ecological niche modeling approach, we determined
areas of suitable habitat in South America, with a focus on Ecuador, to
set a baseline for possible areas at risk of invasion.

\textbf{Methods}

\emph{Specimen collection, DNA extraction, PCR and sequencing}

Specimens of the \emph{Eucalyptus} snout beetle were collected by hand
in various sites in the city of Quito. We removed three legs of the same
side of nine individuals for molecular analyses. Voucher specimens were
deposited in the invertebrate collection of the Zoology Museum (Quito
Católica Zoología Invertebrados, QCAZ I) of Pontificia Universidad
Católica del Ecuador (PUCE), Quito, Ecuador.

For DNA extraction, we left the legs in 500 \selectlanguage{greek}µ\selectlanguage{english}L of TE buffer for 24 hours
at 4 \selectlanguage{ngerman}°C. After rehydration, each softened leg was crushed with a pestle
for 15 minutes, then 500 \selectlanguage{greek}µ\selectlanguage{english}L of CTAB-PVP and 16 \selectlanguage{greek}µ\selectlanguage{english}L of Proteinase K were
added to each sample and left shaking at 56 \selectlanguage{ngerman}°C for 24 hours. We then
added 650 \selectlanguage{greek}µ\selectlanguage{english}L of chloroform to each sample and mixed them to form an
emulsion; subsequently, samples were centrifuged for 10 minutes at 13000
rpm. We removed the supernatant from every sample and saved each in
different 1.5 \selectlanguage{greek}µ\selectlanguage{english}L tubes. Around 600 \selectlanguage{greek}µ\selectlanguage{english}L of 100\% Isopropanol were added to
every sample for DNA precipitation, and then centrifuged for 10 minutes
at 13000 rpm. The pellet was then washed with 70\% ethanol and
centrifuged again for 10 minutes at 13000 rpm. The pellet was left to
dry, and then resuspended in 25 \selectlanguage{greek}µ\selectlanguage{english}L of 0.1X TE Buffer. DNA concentration
was quantified using a Nanodrop 1000 from Thermo Scientific.

We amplified two fragments of the mitochondrial gene COI with the
following pairs of primers: the forward primer Jerry (5'- CAA CAT TTA
TTT TGA TTT TTT GG-3') and the reverse primer Pat (5'- TCC AAT GCA CTA
ATC TGC CAT ATT A-3') (Simon et al., 1994) and the forward primer
dgLCO1490 (5'- GGT CAA CAA ATC ATA AAG AYA TYG G-3') and the reverse
primer dgHCO2198 (5'- TAA ACT TCA GGG TGA CCA AAR AAY CA-3') (Meyer et
al., 2005). Each Polymerase Chain Reaction (PCR) contained 13 \selectlanguage{greek}µ\selectlanguage{english}L total
volume: 2.5 \selectlanguage{greek}µ\selectlanguage{english}L of 10x PCR Rxn Buffer, 3 mM MgCl2, 0.2 \selectlanguage{greek}µ\selectlanguage{english}M of dNTPs, 0.2 M
of forward primer, 0.2 M of reverse primer, 1 unit of Taq Platinum DNA
Polymerase 5000U Invitrogen and 20 ng of DNA. Conditions for PCR
amplification using the first pair of primers were the following:
initial denaturation at 95 \selectlanguage{ngerman}°C for 2 min; 40 cycles of 95 °C for 2 min,
annealing at 46 °C for 1 min, and extension at 72 °C for 1 min; final
extension at 72 °C for 5 min. Conditions for PCR amplification using the
second pair of primers were the following: initial denaturation at 94 °C
for 3 min; 35 cycles of 94 °C for 45 sec, annealing at 47 °C for 30 sec,
and extension at 72 °C for 1.5 min; final extension at 72 °C for 10 min.
The results were confirmed through an agarose gel electrophoresis. The
amplifications were then treated with ExoSAP before being sent to
Macrogen Inc. for sanger sequencing.

\emph{Phylogenetic analyses}

Phylogenetic analyses were conducted with the COI fragments amplified
with the Jerry and Pat primers---GenBank accession numbers:
MW041883-MW0441891. The sequences obtained with the primers dgLCO1490
and dgHCO2198 correspond to the COI fragment traditionally used in DNA
barcoding efforts (Waugh, 2007) and are available as references for
further comparisons---GenBank accession numbers: MW0441892-MW0441898.
Our newly generated DNA sequences were aligned with previously published
sequences (Garcia et al., 2019; Mapondera et al., 2012) and with other
sequences available in GenBank for the \emph{G. scutellatus} species
complex using the Geneious Alignment tool in Geneious Prime 2020.0.3
(https://www.geneious.com/). This alignment tool is versatile because it
can detect, and automatically transform, the direction of the sequences.
The final matrix consisted of 152 sequences, including two sequences
of\emph{Oxyops} sp. as outgroups, and it had a length of 420 bp. We used
IQ-Tree 1.6.12 (Nguyen et al., 2015) to determine in the same analysis
the best-fitting evolutionary model, the best maximum likelihood tree,
and three measures of nodal support. With the option ModelFinder
(Kalyaanamoorthy et al., 2017), we selected the best-fitting model of
codon substitution (MGK+F3X4+G4), because codon substitution models are
more realistic than other types of substitution models (i.e.,
nucleotide, amino acid) in protein-coding sequences (Gil et al., 2013;
Seo \& Kishino, 2009). Nodal support was estimated with the SH-like
approximate likelihood ratio test (SH-aLRT), using 1000 bootstrap
replicates (Guindon et al., 2010), the aBayes test, which is a
Bayesian-like transformation of aLRT (Anisimova et al., 2011), and the
ultrafast bootstrap approximation, using 1000 replicates (Hoang et al.,
2018; Minh et al., 2013). For the six \emph{Eucalyptus} snout beetle
species with most samples (n \selectlanguage{english}[?] 10), we calculated haplotype diversity
(H) and nucleotide diversity (\selectlanguage{greek}π\selectlanguage{english}) with the functions hap.div and nuc.div
from the package \emph{pegas} in R v3.6.3 (Paradis, 2010; R Core Team,
2022). H is the probability that two randomly drawn DNA sequences from
the sample would be different; thus, the values of H range from 0 to 1,
with 0 indicating that all sequences are identical and 1 corresponding
to a highly diverse sample because all sequences are different. \selectlanguage{greek}π \selectlanguage{english}is the
average number of differences per nucleotide site, among pairwise
comparisons of DNA sequences of the sample; thus, larger values indicate
greater nucleotide diversity (Nei, 1987).

\emph{Occurrence data mining}

After confirming the records in Ecuador as \emph{Gonipterus
platensis,}we generated a database of 347 occurrence records of this
species available in the Global Biodiversity Information Facility
(GBIF.org, 2022; www.gbif.org; https://doi.org/10.15468/dl.8mwpht). The
occurrence dataset included few records from the native range of
\emph{G. platensis} in Australia (N = 9; GBIF.org, 2022; www.gbif.org;
https://doi.org/10.15468/dl.26tsqw). Therefore, we focused on
characterizing the potential distribution of the species only using
records from the invaded range in South America. Occurrence records were
then verified to ensure georeferencing accuracy, first, by excluding
localities without spatial reference and duplicates, and then, by visual
examination mapping using the packages \emph{sp} (Pebesma \& Bivand,
2005) and \emph{maptools} (Bivand \& Lewin-Koh, 2022) in R v3.6.3 (R
Core Team, 2022). Finally, occurrences were rarefied to an extent of
\textgreater{}5 km spatial distance from each other using the R
package\emph{raster} (Hijmans, 2023). The 5 km spatial buffer matched
the resolution of the environmental data (see below), and thinning
occurrence datasets helps reduce bias when modeling the distribution of
invasive species (Elith et al., 2010). The final species occurrence
dataset of \emph{G. platensis} included 52 unique localities that were
used for modeling. Data are available for download in Zenodo
(doi.org/10.5281/zenodo.7818068).

\emph{Predicted distribution of suitable habitat}

We used presence only data analyzed under a maximum entropy approach to
develop present day ecological niche models (ENMs). Our goal was to
evaluate the predicted distribution of \emph{Gonipterus platensis} in
its introduced range throughout South America, with a focus on Ecuador.
We used climate data from 19 WorldClim variables summarizing temperature
and precipitation features (Fick \& Hijmans, 2017) and elevation.
Environmental data was trimmed to the regional extent of South America
using the R package \emph{raster} (Hijmans, 2023). The choice of
environmental background can influence the predictive ability in ENM
(Elith et al., 2010). Therefore, we created a background extent to
calibrate the ENM by generating a buffer of 500 km around each observed
locality of \emph{G. platensis} , and sampling 10 thousand random points
within that environmental extent. Final models were then projected onto
the regional extent of South America.

ENMs were generated using Maxent v3.4.1; this method is widely used and
shows high predictive performance compared to other modeling methods
(Elith et al., 2006; Phillips et al., 2006). Species localities were
randomly partitioned into 75\% training and 25\% testing datasets, and
model calibration followed a cross-validation approach with k = 5. We
evaluated a range of regularization values from 1--5 and combinations of
up to four feature classes (i.e., L, Q, H, LQ, LQH, and LQHP) in the R
package \emph{ENMeval2.0} (Kass et al., 2021). The best tuning
parameters for modeling were then selected using Akaike Information
Criterion (AIC; Appendix, Table A1). Maxent uses regularization to
reduce model complexity and included variables contribute differentially
to the final model (Phillips \& Dud\selectlanguage{ngerman}ík, 2008). Thus, we included all 19
WorldClim variables and elevation in the model and allowed the algorithm
to converge onto the variables with the greatest contribution. The final
model was calibrated using the background extent and the best tuning
parameters (i.e., fc = LQH and rm = 2) and was projected on South
America and Ecuador. This approach allowed us to evaluate the predicted
distribution of \emph{G. platensis} across the introduced range.

Model performance was assessed using the area under the receiving
operating characteristic curve (AUC). AUC is a threshold-independent
measure that varies from 0 to 1, where a score of 1 represents perfect
discrimination and a score of 0.5 represents a model no better than
random (Peterson et al., 2011). We considered an AUC score greater than
0.7 to represent good model predictions (Peterson et al., 2011). Given
that AUC has been deemed unreliable for estimating performance of
presence-background models (e.g., Lobo et al., 2008) we separately
calculated the Boyce Index (BI) to assess model prediction in the R
package \emph{ecospat} (Di Cola et al., 2017; Hirzel et al., 2006). The
BI uses a Spearman rank correlation coefficient, which varies from -1 to
1 (Hirzel et al., 2006). A positive BI value approaching one indicates
that model predictions are consistent with the evaluation dataset, zero
indicates random performance, and negative values indicate a poor match
with the evaluation dataset (Hirzel et al., 2006).

Because \emph{G. platensis} is invasive in South America, the final
projected model implemented a lowest presence threshold of 95\% (LPT95,
equivalent to the Minimum Training Presence threshold) obtained from the
model estimated by the Maxent cloglog output (Soto-Centeno \& Steadman,
2015). Under this rule, prediction pixels with equal or higher values
than the LPT95 were scored as suitable conditions where \emph{G.
platensis} could sustain viable populations in the introduced range. We
chose LPT95 to provide a conservative prediction where model datasets
contained at least 95\% of locality points within suitable habitat
(i.e., a theoretical expectation of 5\% omission rate of the training
data; Pearson et al., 2007). This threshold also helped us determine
visually if our ENMs allowed enough sensitivity to examine novel areas
of environmental suitability where \emph{G. platensis} could establish
populations in South America.

\emph{Characterization of climate envelopes}

We examined the range of conditions where \emph{G. platensis} is found
throughout South America (i.e., their ``climate envelope''; Hijmans \&
Graham, 2006). These ranges were compared to the conditions where the
species is found in Ecuador. This helped us define whether conditions in
Ecuador differ from those in the rest of the continent. Climate
envelopes were constructed using data from the top four environmental
variables with highest contributions (i.e., \textgreater{} 10\%) in the
Maxent models and elevation. In order of importance, the variables
included temperature seasonality (bio 4), annual mean temperature (bio
1), precipitation of the coldest quarter (bio 19), and mean temperature
of the coldest month (bio 6). While elevation did not have a high
contribution to the model, we used it here to broadly discuss areas of
predicted suitable habitat of \emph{G. platensis} . From these data, we
determined the climate and elevation profiles for all localities
where\emph{G. platensis} was documented by extracting environmental
information across all localities and directly comparing the range of
conditions individually in Ecuador vs. the rest of South America. These
data were not normally distributed; thus, Wilcoxon rank sum tests
followed by a Bonferroni correction for multiple comparisons were used
to examine the differences between conditions in Ecuador vs. the rest of
South America. This framework allowed us to better understand the
variation in environmental niche occupancy in the introduced range
of\emph{G. platensis} across the continent.

\textbf{Results}

\emph{Phylogeny and molecular identification}

The gene tree of the mitochondrial COI gene of species of
the\emph{Gonipterus scutellatus} complex (Fig. 2) showed that the
Ecuadorian samples are nested within the clade of \emph{Gonipterus
platensis} . The Ecuadorian samples were identical among them and showed
no variation with most of the other \emph{G. platensis} in the clade. In
fact, across the dataset, \emph{G. platensis} was one of the least
genetically diverse species (see nucleotide diversities, \selectlanguage{greek}π, \selectlanguage{english}in Fig. 2),
despite being the one with the most representation of DNA sequences. On
the other hand, \emph{G. notographus} and \emph{G} . sp. 2 were the most
genetically diverse species. Note that one sample, FJ888583\_co58, here
designated as \emph{G.} aff. \emph{notographus} , was placed outside
the\emph{G. notographus} clade.

\emph{Predicted distribution and climate envelope comparison}

The distribution of \emph{G. platensis} in South America could be driven
by an expansion into novel environments. Thus, our ENM approach focused
on evaluating areas of potentially suitable climate that the species
could invade. This model had good performance and obtained a true
positive fraction of 98\% across all known unique localities of the
invaded range (AUC = 0.956, BI = 0.883; Fig. 3). Notably, the ENM
predicted highly suitable climates for \emph{G. platensis} in the Andes
of Colombia, Ecuador, and Northern Peru. The coastal portion Chile, from
Santiago to Puerto Montt, also showed high suitability, whereas areas at
the southeast of Brazil and Uruguay, east Argentina and the Andes of
Bolivia showed intermediate suitability. In Ecuador, we found three
areas of high suitability in the northern (where we collected the
specimens analyzed in this study), central and southern parts of the
Ecuadorian Andes. Comparison of climate envelopes of the four top
contributing climatic factors and elevation for Ecuador and the rest of
South America highlighted some variation in the occupied niches (Table
1; Fig. 4). For example, the range of \emph{G. platensis} in Ecuador
revealed significant climate envelope differences related to seasonality
and elevation (Table 1; Fig. 4). Across South America, these beetles
occupy a wide breadth of climatic conditions and elevations. They exist
in elevational ranges spanning low and high altitudes, from 7 m to 3351
m above sea level (a.s.l.). In Ecuador, \emph{G. platensis} occupies the
highest elevational range with a mean of ca. 2600 (2233--3351) m a.s.l.
(Fig. 4). The lowest elevation ranges were found in Argentina, Chile,
and Uruguay, generally found from 7 to 327 m a.s.l.

\textbf{Discussion}

We aimed to document the introduction of the \emph{Eucalyptus} snout
beetle in Ecuador, determine its taxonomic placement, and model
potentially suitable habitat throughout South America. With this
framework, we provide the first genetic evidence for the presence
of\emph{Gonipterus platensis} in Ecuador. Furthermore, our ecological
niche model analysis suggests areas of suitable habitat throughout a
broad range of climatic and elevational regimes that may have played a
role in the establishment of populations of \emph{Eucalyptus} snout
beetles in South America and could promote its future expansion into new
areas.

The phylogenetic analysis we showed herein highlighted large genetic
variation across the \emph{Gonipterus} \emph{scutellatus} species
complex. However, most of the samples analyzed in this study came from
Australia and Tasmania, with few samples coming from Portugal, Spain and
South Africa (Mapondera et al., 2012), and only the nine samples we
collected coming from South America. DNA barcoding data (mitochondrial
COI sequences) clearly resolved the identity of the beetles found in
Ecuador as \emph{G. platensis} , which has also been documented in other
South American countries (i.e., Argentina, Brazil, Colombia, Chile, and
Uruguay) (Garcia et al., 2019; Mapondera et al., 2012; Schr\selectlanguage{ngerman}öder et al.,
2020). The genetic diversity of \emph{G. platensis} was one of the
lowest among the species analyzed in this study (see values of \selectlanguage{greek}π \selectlanguage{english}for
each clade in Fig. 2), which coincides with the results found by
Mapondera et al. (2012) for this species in Western Australia. In fact,
the Ecuadorian samples share the same haplotypes with individuals from
Tasmania (Garcia et al., 2019), which suggests that the Ecuadorian
population could have originated from a Tasmanian source.
Notwithstanding, an analysis including genetic samples from Colombia and
other South American countries is needed to thoroughly examine whether
the Ecuadorian samples were a product of a direct introduction from the
native range or if they were secondarily introduced from another South
American country. An introduction from Colombia seems plausible given
geographical proximity and the continuity of suitable climatic habitat
predicted by our model (Fig. 3). Moreover, our phylogeny suggested that
there are at least three different geographical origins of the \emph{G.
platensis} specimens found outside Australia. Indeed, two different
sequences of this species from Spain and Portugal (JN391479\_ESP and
JN391480\_POR) are the most different compared to the sequences of South
America and Tasmania, as evidenced by their long branches in the
phylogeny (see \emph{Gonipterus platensis} clade in Fig. 2). Additional
sampling and genetic analysis are necessary from \emph{Eucalyptus} snout
beetle populations from Southeastern Australia and from other South
American countries to truly pinpoint the origin of invasive populations.

Two main challenges have been identified when producing ecological niche
models (ENM) for invasive species (Lake et al., 2020). First, invasive
species are often in disequilibrium with the novel environment they
occupy, and second, generating ENM projections from native to invaded
ranges may be problematic for correlative model approaches (Elith et
al., 2010). We followed an ENM approach at the species level, including
all unique localities of \emph{G. platensis} in South America available
in GBIF (GBIF.org, 2022; www.gbif.org;
https://doi.org/10.15468/dl.8mwpht). This approach is justifiable
because few records were available in open-source databases from the
native range of Australia (N = 9). Such a low number of occurrences
would bias ENM predictions, particularly when projected from native
ranges into novel environments where the species was introduced.
Furthermore, using only the native range to estimate the potential
suitable habitat of a species may result in misrepresentation of
predictions, particularly if the colonization into the invaded range is
characterized by niche expansion (Broennimann et al., 2007). To mitigate
the spatial data bias, we rarefied records and used a background extent
that included only potentially reachable areas (following Elith et al.,
2010). This choice of background would reduce the degree of model
extrapolation (Elith et al., 2010). Despite the uneven sampling, our
approach allowed us to produce estimates of the areas where \emph{G.
platensis} occurs, but also assess regions of suitable habitat where
these beetles could potentially be found or invade (Fig. 3).

The ENM predictions presented herein attained a high true positive
fraction for the currently known observations (Appendix, Fig. A1).
Notably, the westernmost locality in the Buenos Aires province of
Argentina showed the lowest predicted distribution values and was
excluded by the LPT95 threshold. This inland locality of Argentina was
characterized by a narrow range of high temperatures (i.e., little
variation in temperature seasonality) and generally dry conditions
(i.e., lowest precipitation of coldest quarter) that were unique
compared to all other occurrences. Despite the lack of observation
records from GBIF, model predictions showed that suitable habitat
for\emph{G. platensis} exists in southeastern Brazil in the city of
Curitiba, PR, where the beetle was first documented as invasive for the
country (Fig. 3; op. cit. \emph{G. scutellatus} in Freitas, 1979). This
region of suitability in Brazil spans from the state of Esp\selectlanguage{ngerman}írito Santo,
south through Rio Grande do Sul, and west into Mato Grosso do Sul, in
which \emph{G. platensis} is widely recognized as a pest
of\emph{Eucalyptus} forests (de Souza, 2016; Wilcken \& Oliveira, 2015).

In the northwestern parts of South America, ENM predicts suitable
habitat across the Andes from Colombia to Bolivia at high elevations
(Fig. 3). In Ecuador, \emph{G. platensis} was predicted to have
significantly higher elevations than elsewhere in the continent (Fig. 4;
Table 1), with highly suitable habitat existing in and around the cities
of Quito, Ambato, and Cuenca and with at least moderately suitable
habitat occurring all along the Ecuadorian Andes (Fig. 3). Suitable
habitat at high elevations was also predicted throughout Perú, which may
represent potential areas for invasion (Fig. 3). In the southern
countries, the model resulted in high to moderate values of suitability
at lower elevations in Chile, Argentina, and Uruguay, respectively (Fig.
3). Examination of the climate envelopes revealed that \emph{G.
platensis} in Ecuador occupies significantly less seasonal and drier
conditions than in other parts of the invaded range at similar
elevations (Fig. 4; Table 1). Nonetheless, when considering the entire
distribution, this beetle occurs across a wide elevational range and a
variety of suitable environmental conditions where it could potentially
sustain long term populations. The sampling bias, which likely excluded
areas occupied by \emph{G. platensis} but not included in GBIF or not
yet documented across South America, renders our ENM predictions
somewhat exploratory. Nonetheless, these results combined with climate
envelope analysis highlights the potential of \emph{G. platensis} to
occupy a broad range of environmental conditions, which may allow it to
become highly invasive in different parts of South America.

Finally, we make a call to study the ecological and economic impacts of
the invasion of \emph{Eucalyptus} snout beetle and its hosts
(e.g.,\emph{Eucalyptus globulus} ) in South America, particularly in
Colombia and Ecuador. In northern South America little research has been
conducted on forestry of \emph{Eucalyptus} species, whereas in Brazil
and Chile, forestry research is quite active. In the particular case of
Ecuador, \emph{Eucalyptus} trees were planted in the late 19th century,
mainly in volcanic soils, known as `cangagua', in the inter-Andean
valleys (FAO, 1981). These valleys have had human influence for
millennia (Bush et al., 2022; Young, 2009), so perhaps these degraded
environments have lost natural enemies that could exploit these insects
as new hosts. We thus recommend extensive sampling of
\emph{Eucalyptus}snout beetles and potential controllers in Ecuador to
test this hypothesis. Also, as far as we know and according to records
in GBIF,\emph{Gonipterus platensis} remains concentrated in a couple of
areas of the Ecuadorian Andes (around the city of Quito in the North and
in the city of Loja in the South, Appendix, Fig. A1), but our model
predicts the whole range as climatically suitable for invasion. In fact,
climatic suitability expands across the border to the Peruvian Andes,
within the area of distribution of \emph{Eucalyptus} spp. plantations
(Castillo Vera et al., 2019; Luzar, 2007), where no reports of the
insects have been made so far. All this highlights the urgency of
conducting more studies on the invasion of this pest to design effective
control and/or prevention measures.

\textbf{Competing interests}

The authors have declared that no competing interests exist.

\textbf{Data accessibility}

DNA sequences: Genbank accession numbers MW041883-MW0441898

Files and scripts used to run phylogenetic analyses are publicly
available at Zenodo: doi.org/10.5281/zenodo.7823745

Records of \emph{Gonipterus platensis} used for our models and R scripts
for Ecological Niche Modeling are publicly available at Zenodo:
doi.org/10.5281/zenodo.7818068

\textbf{Author contributions}

\textbf{Verónica Crespo-Pérez:} conception and design (lead), data
curation (equal), writing original draft (lead), project administration
(lead)

\textbf{J. Angel Soto-Centeno:} conception and design (supporting), data
curation (equal), data analysis (equal), methodology (equal), writing
original draft (supporting)

\textbf{C. Miguel Pinto:} conception and design (supporting), data
analysis (equal), methodology (equal), writing original draft
(supporting)

\textbf{Ana Avilés:} data curation (supporting), writing - review and
editing (equal)

\textbf{Washington Pruna:} data curation (supporting), writing - review
and editing (equal)

\textbf{Claudia Terán:} methodology (supporting), writing - review and
editing (equal)

\textbf{Álvaro Barragán:} conception and design (lead), data curation
(equal), writing - review and editing (equal), project administration
(supporting)

\textbf{Acknowledgments}

This work was supported by PUCE's Research Directorate, project
``Presencia del gorgojo del eucalipto en Ecuador y riesgo potencial para
América del Sur''. Work by JAS-C was partly supported by a National
Science Foundation grant DEB:2135257. Collection of specimens and
obtention of DNA sequence data were performed under permit
MAE-DNB-CM-2015-0030 granted by the Ministerio del Ambiente of Ecuador.
DNA sequencing was possible thanks to the support by the grant Arca de
Noé Initiative, PIs. S. Ron and O. Torres Carvajal, funded by Secretaría
de Educación Superior, Ciencia, Tecnología e Innovación del Ecuador.

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\textbf{Table 1.} Environmental comparison of climate envelopes
for\emph{Gonipterus platensis} in South America. All values represent
means (range) for the following environmental features: Temperature
seasonality (st.dev. x 100; bio 4), annual mean temperature (bio 1),
precipitation of the coldest quarter (bio 19), mean temperature of
coldest month (bio 6), and elevation (in m a.s.l.). \emph{P} values
adjusted using a Bonferroni correction; values with an asterisk (*)
indicate statistical significance at the 0.05 level.\selectlanguage{english}
\begin{longtable}[]{@{}lllllll@{}}
\toprule
\textbf{Locality} & \textbf{n} & \textbf{bio 4} & \textbf{bio 1} &
\textbf{bio 19} & \textbf{bio 6} & \textbf{Elevation}\tabularnewline
\textbf{Ecuador} & 23 & 18.3 (11--44) & 14.2 (10--16) & 288 (103--363) &
7.8 (4--10) & 2666 (2233--3351)\tabularnewline
\textbf{South America} & 29 & 310 (31--581) & 14.1 (10--21) & 473
(76--993) & 5.5 (2--13) & 566 (7--2657)\tabularnewline
& \emph{P} & \textless{} 0.005* & \textgreater{} 0.5 & 0.265 &
\textless{} 0.005* & \textless{} 0.005*\tabularnewline
\bottomrule
\end{longtable}

\textbf{Figure legends}

\textbf{Figure 1.} Adult of \emph{Gonipterus platensis} collected by
VC-P and CMP in the city of Quito. Photo by VC-P with an Olympus Tough
TG6 digital camera.

\textbf{Figure 2.} Maximum likelihood tree of a fragment of the COI gene
of species of the \emph{Gonipterus scutellatus} complex. Numbers at the
nodes indicate support values from the SH-like approximate likelihood
ratio test (SH-aLRT), the Bayesian-like transformation of aLRT (aBayes
test), and the ultrafast bootstrap method. Terminal labels indicate the
GenBank accessions, followed by sample codes. Clade classification
follows Mapondera et al. (2012). Values of nucleotide (\selectlanguage{greek}π\selectlanguage{english}) and haplotype
(\emph{H} ) diversity for each of the six species examined are indicated
below the species names.

\textbf{Figure 3.} Predicted distribution of \emph{Gonipterus
platensis}in its introduced range of South America. The inset shows a
focus on Ecuador. Each model was calibrated to the local environmental
conditions of the introduced range. Warmer colors of predicted
distribution (i.e., red) indicate higher environmental suitability.
Niche model predictions including occurrences are included in the
Appendix, Fig. A1.

\textbf{Figure 4.} Boxplots showing the distribution for environmental
features characterizing the introduced ranges of \emph{Gonipterus
platensis} . Brown = Ecuador and teal = South America. Median: heavy
line; 25\% and 75\% quartiles: edges of box; 5\% and 95\% quartiles:
whiskers; observed values included as points to help visualize density.

\textbf{Appendix}

\textbf{Table A1:} Parameter tuning results from ENMeval (Kass et al.
2021) for \emph{Gonipterus platensis} throughout South America. fc =
feature classes (i.e. L = linear, Q = quadratic, H = hinge, P =
product), rm = regularization (beta) multiplier, tune.args = parameter
tuning to implement in Maxent, AICc = corrected Akaike Information
Criterion, delta.AICc = deviation from the smallest AICc value (set at
zero and arranged in ascending order), ncoef = number of coefficients.\selectlanguage{english}
\begin{longtable}[]{@{}llllll@{}}
\toprule
fc & rm & tune.args & AICc & delta.AICc & ncoef\tabularnewline
LQH & 2 & fc.LQH\_rm.2 & 1083.09907 & 0 & 19\tabularnewline
LQHP & 3 & fc.LQHP\_rm.3 & 1086.08195 & 2.98288597 & 16\tabularnewline
H & 2 & fc.H\_rm.2 & 1094.32451 & 11.2254442 & 19\tabularnewline
LQHP & 2 & fc.LQHP\_rm.2 & 1095.95112 & 12.8520564 & 22\tabularnewline
H & 3 & fc.H\_rm.3 & 1096.50915 & 13.4100852 & 14\tabularnewline
LQHP & 4 & fc.LQHP\_rm.4 & 1100.32847 & 17.2294029 & 15\tabularnewline
LQHP & 1 & fc.LQHP\_rm.1 & 1103.75876 & 20.6596915 & 28\tabularnewline
LQH & 1 & fc.LQH\_rm.1 & 1107.2177 & 24.1186367 & 28\tabularnewline
LQHP & 5 & fc.LQHP\_rm.5 & 1107.68961 & 24.5905466 & 13\tabularnewline
H & 4 & fc.H\_rm.4 & 1110.39672 & 27.2976525 & 13\tabularnewline
H & 1 & fc.H\_rm.1 & 1112.79687 & 29.6978034 & 27\tabularnewline
LQH & 3 & fc.LQH\_rm.3 & 1113.34805 & 30.2489781 & 19\tabularnewline
LQ & 1 & fc.LQ\_rm.1 & 1119.77007 & 36.6710077 & 16\tabularnewline
LQH & 5 & fc.LQH\_rm.5 & 1121.3233 & 38.2242347 & 12\tabularnewline
LQH & 4 & fc.LQH\_rm.4 & 1125.6363 & 42.537234 & 17\tabularnewline
H & 5 & fc.H\_rm.5 & 1126.60636 & 43.5072975 & 12\tabularnewline
LQ & 2 & fc.LQ\_rm.2 & 1134.2499 & 51.1508365 & 14\tabularnewline
LQ & 3 & fc.LQ\_rm.3 & 1151.56173 & 68.4626582 & 12\tabularnewline
LQ & 4 & fc.LQ\_rm.4 & 1172.08359 & 88.9845189 & 12\tabularnewline
LQ & 5 & fc.LQ\_rm.5 & 1194.0281 & 110.929032 & 12\tabularnewline
L & 1 & fc.L\_rm.1 & 1314.40165 & 231.302579 & 15\tabularnewline
L & 2 & fc.L\_rm.2 & 1315.73578 & 232.636713 & 10\tabularnewline
L & 3 & fc.L\_rm.3 & 1331.31237 & 248.213307 & 9\tabularnewline
L & 4 & fc.L\_rm.4 & 1351.63307 & 268.534007 & 10\tabularnewline
L & 5 & fc.L\_rm.5 & 1356.33024 & 273.231173 & 8\tabularnewline
\bottomrule
\end{longtable}

\textbf{Figure A1.} Predicted distribution of \emph{Gonipterus
platensis} in its introduced range of South America with a focus on
Ecuador (inset). Each model was calibrated to the local environmental
conditions of the introduced range. Warmer colors of predicted
distribution (i.e., red) indicate higher environmental suitability.
Occurrences used for modeling are represented by green circles.\selectlanguage{english}
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