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\begin{document}

\title{Current and potential distribution of the invasive apple snail, Pomacea
canaliculata in Eastern Africa: Evidence from delimiting surveys and
modelling studies.}



\author[1]{Fernadis Makale}%
\author[2]{Alexander Muvea}%
\author[1]{Idah Mugambi}%
\author[1]{Duncan Chacha}%
\author[1]{Elizabeth Finch}%
\author[1]{Ivan Rwomushana}%
\affil[1]{CAB International}%
\affil[2]{Kenya Plant Health Inspectorate Services (KEPHIS)}%


\vspace{-1em}



  
  \date{June 15, 2024}


\begingroup
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\selectlanguage{english}
\begin{abstract}
Pomacea canaliculata, commonly known as Golden Apple Snail (GAS), an
invasive snail, has successfully invaded ecosystems outside its native
ranges with negative impacts being reported. GAS was reported in Kenya
in 2020 invading one of the largest rice-producing schemes, the Mwea
irrigation scheme. Delimiting surveys were conducted in five major rice
production schemes in Kenya to establish the boundary of spread since
its first report and to help in the management and development of
quarantine strategies to limit the spread of this pest within the scheme
and other risk areas. In addition, an Ensemble model approach was used
to model the potential distribution of GAS in Eastern Africa. Over 80\%
of the Mwea scheme's sections were infested with GAS along the river and
irrigation channel gradient from the initial infestation point (Ndekia).
The mean adult/m2 and egg clutches count was 8.4 and 7.7, respectively.
Significant difference was observed in number of adults/m2 and egg
masses among the sections; X2(7) = 138.69, p\textless{} 0.001and X 2(7)
= 114.17, p \textless{} 0.001, respectively. The survey did not find any
adults or eggs of GAS in the Ahero, Bura, Hola and West Kano rice
schemes. Most of the areas in Kenya were suitable for GAS invasion,
though with significant variations across the country. At a regional
level, the countries with the highest suitability for GAS were Malawi,
Madagascar and Uganda. Mozambique, Tanzania and Ethiopia also had areas
of high suitability, but these were more concentrated in specific areas
in each of the countries. In comparison, suitability across Sudan and
Somalia was very low. Strict quarantine measures should be instituted
and implemented to curb not just the spread of GAS in Kenya but entry
into uninvaded regions.%
\end{abstract}%



\sloppy


\textbf{Current and potential distribution of the invasive apple
snail,\emph{Pomacea canaliculata} in Eastern Africa: Evidence from
delimiting surveys and modelling studies} .

Fernadis Makale\textsuperscript{1*}, Alexander M.
Muvea\textsuperscript{2}, Idah Mugambi\textsuperscript{1}, Duncan
Chacha\textsuperscript{1}, Elizabeth A. Finch\textsuperscript{3}, Ivan
Rwomushana\textsuperscript{1}.

*Corresponding author: F.makale@cabi.org

\textsuperscript{1} CABI, Canary Bird, 673 Limuru Road, Muthaiga, P.O
Box 633-00621, Nairobi, Kenya

\textsuperscript{2} Kenya Plant Health Inspectorate Service, P.O. Box
49592-00100, Nairobi

\textsuperscript{3}CABI, Bakeham Lane, Egham, Surrey, TW20 9TY

\textbf{Abstract} .

\emph{Pomacea canaliculata,}commonly known as Golden Apple Snail (GAS),
an invasive snail, has successfully invaded ecosystems outside its
native ranges with negative impacts being reported. GAS was reported in
Kenya in 2020 invading one of the largest rice-producing schemes, the
Mwea irrigation scheme. Delimiting surveys were conducted in five major
rice production schemes in Kenya to establish the boundary of spread
since its first report and to help in the management and development of
quarantine strategies to limit the spread of this pest within the scheme
and other risk areas. In addition, an Ensemble model approach was used
to model the potential distribution of GAS in Eastern Africa. Over 80\%
of the Mwea scheme's sections were infested with GAS along the river and
irrigation channel gradient from the initial infestation point (Ndekia).
The mean adult/m\textsuperscript{2} and egg clutches count was 8.4 and
7.7, respectively. Significant difference was observed in number of
adults/m\textsuperscript{2} and egg masses among the sections;
X\textsuperscript{2}(7) = 138.69, p\textless{} 0.001and
X\textsuperscript{2}(7) = 114.17, p \textless{} 0.001, respectively. The
survey did not find any adults or eggs of GAS in the Ahero, Bura, Hola
and West Kano rice schemes. Most of the areas in Kenya were suitable for
GAS invasion, though with significant variations across the country. At
a regional level, the countries with the highest suitability for GAS
were Malawi, Madagascar and Uganda. Mozambique, Tanzania and Ethiopia
also had areas of high suitability, but these were more concentrated in
specific areas in each of the countries. In comparison, suitability
across Sudan and Somalia was very low. Strict quarantine measures should
be instituted and implemented to curb not just the spread of GAS in
Kenya but entry into uninvaded regions.

\textbf{Keywords} : \emph{Pomacea canaliculata,} delimiting survey,
invasion boundary, Ensemble modelling, invasive, golden apple snail.

\textbf{Introduction}

Biological invasions are major contributors to environmental or
ecosystem change with invasive alien species driving this change by
challenging the conservation of biodiversity and natural resources
(Simberloff \& Von Holle, 1999). This is achieved through significant
interactions e.g. interspecific competition which can influence habitat
selection, avoidance behaviour, and niche overlap (Davis, 2003; Martin
et al., 2010; Rosenzweig, 1987; Young, 2004). Indeed, the least
desirable outcomes of these interactions are habitat alteration (Burks
et al., 2011; Carlsson et al., 2004) and in extreme circumstances,
extinctions do occur (Clavero et al., 2009).

Among the notable invaders throughout the world are species from the
Pomacea genus, including \emph{Pomacea canaliculata} (Lamarck, 1829)
(channelled apple snail or Golden Apple Snail (GAS)) (Cowie \& Hayes,
2012; Hayes et al., 2012, 2015; Naylor, 1996). GAS is an invasive exotic
gastropod invading freshwater aquatic systems throughout the tropics and
is listed among `100 of the world's worst invasive species' (Lowe et
al., 2000). It is native to South, Central and North America (Hayes et
al., 2008) and has gained pest status outside its native ranges,
becoming a serious agricultural and ecological pest, that has caused
significant economic losses in wetland rice cultivation including
threatening biodiversity (Arfan et al., 2014; Correoso et al., 2017;
Cowie, 2009; Cowie \& Hayes, 2012; Halwart, 1994; Hayes et al., 2008;
Mart\selectlanguage{ngerman}ín et al., 2017; Seuffert \& Martín, 2013; Win, 2020; Yang et al.,
2018). As with many other successful invasive species, several factors
contribute to the invasiveness of GAS, especially in the invaded ranges.
They include high adaptability to stressful environmental conditions,
high reproductive rates and a lack of potential and effective natural
enemies (Cowie, 2009; Yusa, 2006) in the invaded ranges. Also,
sufficient irrigation water (Cowie, 2009; Salleh et al., 2012a; Teo,
2003) and food sources- rice and wild hosts e.g. Taro, (Sanico et al.,
2002) have facilitated the spread of this pest, especially in rice
fields. The amphibious respiration, capacity to aestivate during dry
periods, as well as cold acclimation and tolerance, increase the
resilience of GAS to rice farming practices, including agrochemical
applications, intermittent drainage and crop rotations -- under a wide
range of climatic conditions (Horgan, 2018). Damage and economic losses
continue to be reported in rice-producing areas as a result of
replanting to replace damaged crop and due to increased production costs
occasioned by the application of management practices (Halwart, 1994;
Ranamukhaarachchi \& Wickramasinghe, 2006; Salleh et al., 2012) in
addition to the non-crop impact to human health and natural ecosystems
(Joshi, 2007).

Rice is the third most important crop in Kenya and plays a critical role
in increasing household food security and farmers' incomes. It is mainly
produced under irrigation with production expected to increase with the
construction of Thiba dam in Mwea. In the past decade, rice consumption
has increased at a rate of 12\% annually, compared to 4\% for wheat and
1\% for maize (Atera et al., 2018). However, rice demand in the country
exceeds production (Kenya only produces 19\% of the rice consumed within
the country) with imports filling the gap between production and
consumption. In addition to production constraints related to water
shortage, pests and diseases (Watanabe et al., 2021), GAS has added to
the long list of challenges in rice farming threatening the entire value
chain. As such, further invasion of GAS in the already thin rice
industry would be detrimental to the country's economy and livelihoods.
Previous studies have found that GAS can reduce rice yields by as much
as 50\% with economic impacts running into millions of US dollars
(Djeddour et al., 2021; Halwart, 1994). Thus, working out where GAS has
spread to, and might spread to, could be used to give early warnings to
decision-makers to try and limit the effects of any potential invasion.
This will also help in contingency planning in case of invasion.

Since its first report in Kenya in 2020 (Buddie et al., 2021), GAS
continues to expand its range from the invasion point where damage to
rice crop continues to be reported by farmers. Its arrival or
introduction pathway in Kenya is however still contentious. Unconfirmed
media reports in Kenya suggest that the snail was introduced as a weed
biocontrol, but no import permit to introduce the species has been
issued by authorised organisations in the country. In Kenya, the
management of this pest has majorly relied on physical and cultural
practices with the desperate use of broad-spectrum synthetic chemicals.
Other available strategies employed by farmers include hand-picking of
adults and crushing of egg masses and water/flood management (alternate
wetting and drying). However, most of these practices have proved
ineffective to curb the spread of GAS. This is further exacerbated by
the fact that the country has no pesticides (Molluscicides) registered
specifically for the management of GAS.

Following a status survey in September 2020, samples of GAS were
collected and molecular work to barcode was carried out to ascertain the
true identity of this snail species. After confirmatory work of the
identity, CABI and Kenya Plant Health Inspectorate Services (KEPHIS)
undertook a delimiting survey in the five major rice production areas
i.e. Mwea, Ahero, Bura, Hola and West Kano irrigation schemes in
Kirinyaga, Tana River and Kisumu Counties to establish the boundary of
spread since its first report and to help in the management and
development of quarantine strategies to limit the spread of this pest.
In addition, species distribution modelling using the Ensemble model
approach was conducted to model the potential distribution of GAS in
East Africa.

\textbf{Materials and Methods}

\textbf{Study site}

The survey was carried out in Mwea, Bura, Hola, Ahero and West Kano rice
schemes. Mwea Irrigation Scheme is located in Kirinyaga County and falls
within two sub-counties (Mwea East and Mwea West). The scheme accounts
for over~70\% of the country's~rice~production and covers an approximate
area of 26,000 acres under paddy rice production while the remaining
area is utilized for settlement, public utilities and the growing of
subsistence crops. The scheme lies along the drainage basins of Rivers
Nyamindi and Thiba which supply the irrigation water. River Nyamindi
serves Tebere, Ndekia and Kiamanyeki sections while Thiba river serves
Mwea, Thiba, Wamumu, Karaba and Kandongu sections. The area receives a
mean annual rainfall and temperature of about 900- 1200 mm and 21.5 °C,
respectively. Ahero and West Kano Irrigation Schemes are located in
Kisumu County with a cumulative area of about 10,000 acres under
irrigation. They are served by two irrigation water sources i.e. River
Nyando and Lake Victoria, respectively. The area experiences a mean
annual temperature and rainfall of \textasciitilde{}
22.9\textsuperscript{o}C and \textasciitilde{}1300 mm, respectively.
Hola (Tana Delta irrigation scheme) and Bura Irrigation schemes are
located in Tana River County in the Lower Tana Basin. Bura scheme has a
tenant population of 2245 farmers settled in 11 villages with an
irrigatable area of 12000 acres. Green grams, maize, cowpeas, cotton,
watermelon, rice and onions are the primary crops cultivated. Tana Delta
scheme is the oldest National Irrigation Authority scheme in the
country. The scheme is about 12630 acres with 5000 acres under
irrigation. The main crops cultivated here include maize, rice, cotton,
watermelon, green grams, onions, pasture and other horticultural crops.
Tana River County has a hot and dry climate. Average annual temperatures
are about 30\textsuperscript{o}C. The highest midday temperature may be
above 40\textsuperscript{o}C around January-March with the lowest
temperature being around 20\textsuperscript{o}C in June-July.
Supplementary file 1 shows the survey locations including GPS
coordinates.

\textbf{Delimiting survey}

A delimiting survey (according to International Standards for
Phytosanitary Measures, ISPM No. 6) was conducted for GAS in the major
rice production areas of Kenya following its invasion of rice fields in
Mwea. Besides the Mwea irrigation scheme, other schemes including Ahero,
Hola, Bura and West Kano (Figure 2) were considered risk locations
because they correspond to the risk activities for \emph{Pomacea} spp.
based on the suitability of habitats. With the help of scheme managers,
field officers and lead farmers in these schemes and through trace-back
information, points of invasion were identified and defined to begin the
survey. The recommended approach was to survey the rice paddies, along
the watercourses and other suitable habitats in the vicinity of the
source of infestation within the potential spread distance estimated for
the apple snails. Field surveys were conducted in these schemes in
December 2020 and February 2022.

\textbf{Sampling procedure}

We sampled 3- 10 evenly distributed sampling sites in the rice paddy and
along the irrigation canals/feeder canals, depending on their length and
accessibility; the sites considered as inhabited by GAS were those where
eggs masses or live snails were found/observed after a preliminary
inspection from the shores. At sites with GAS populations, we sampled 3
points of \emph{ca} . 10 m located within less than 100 m; at sites
without GAS only one section was sampled. Sampling comprised a total of
401 points from 68 georeferenced sites distributed across the five
schemes (Table 1).

At each section, two people carefully inspected rice paddies, terrace
walls and irrigation canal shores while wading upstream in search for
egg masses on the emergent aquatic vegetation and other substrata; GAS
were searched for among the submerged vegetation, under stones or buried
in the substratum. Start and end points were marked as 10 x 10 m on rice
paddies and 10 m along canals for eggs count. A 1
m\textsuperscript{2}wooden frame quadrat was laid in three selected
sampling points of 10 m. All visible adults and eggs were counted and
recorded. At each sampling point, information on the number of adults,
egg masses, crop variety, other hosts infested, GPS points and
photographs, and time of sampling were recorded. Where the common white
garden snails were found, this was recorded as well. The starting points
for sampling were considered based on trace-back information that
confirmed that the upper sides were the invasion points. Therefore,
sampling was started from the lower (low infestation) to the upper ends
to avoid any chance of spreading the GAS to lower un-infested sides.

Special attention was given to drainage and irrigation canals as they
are significant for the dispersal of the pest. All the sections of the
Mwea irrigation scheme (including outgrowers) were surveyed i.e. Mwea,
Tebere, Kiamanyeki (outgrower), Ndekia (outgrower), Wamumu, Thiba,
Karaba, Kandongu/Ngothi (outgrower) and Mwea Irrigation Authority
Development (MIAD) centre area. A similar procedure was used in Ahero,
Bura, Hola and West Kano irrigation schemes. Sampling was done in 68
geo-referenced farms using a global positioning system device Garmin,
eTrek© 20x (Garmin, USA) across five major rice-cultivating locations of
Kenya.

\textbf{Data analysis}

Data analysis was carried out using Microsoft Excel (2019) and STATA
version 17 (StataCorp, College Station, Texas). Normality of variables
was tested using the Shapiro-Wilk test (Razali \& Wah, 2011), alongside
visual inspection of histograms. Homogeneity of variance was tested
using Levene's test (Nordstokke \& Zumbo, 2010; Nordstokke et al.,
2011). The Shapiro-Wilk and Levene's tests showed that the dependent
variables (adults/m\textsuperscript{2} and egg masses) did not follow a
normal distribution (p\textless{}.001) and had unequal variances
(p\textless{}.001). Therefore, the nonparametric Kruskal-Wallis H test
(Chan \& Walmsley, 1997) (Vargha \& Delaney, 1998) was used to determine
whether these variables differed across the different sections. A
pairwise post-hoc Dunn's test with Bonferroni adjustments was done to
identify the sections which were significantly different, with respect
to the number of adults/m\textsuperscript{2} and egg masses. Only data
from Mwea was included in the analysis since no GAS were observed in all
the other schemes.

\textbf{Species distribution modelling}

\textbf{Environmental data}

Based on the environmental requirements of GAS, the following climatic
variables were selected: annual precipitation; precipitation of driest
month, maximum temperature of warmest month and mean temperature of
coldest quarter. These were extracted from the 10 arc-minute resolution
WorldClim dataset (http://www.worldclim.org).

Precipitation data often does not reflect the amount of water input into
irrigated areas. As such, the chosen variables relating to
precipitation, annual precipitation and precipitation of driest quarter,
were modified to reflect irrigation patterns. The irrigation correction
was based on the general principle that irrigation compensates for water
loss through evapotranspiration (Brouwer \& Heibloem, 1986) and uses
methods fully described in Federman et al., (2013). In summary, the
difference between evapotranspiration and precipitation was calculated
for both annual data and for data representing the driest quarter. These
differences were then applied to annual precipitation and the
precipitation of the driest quarter in irrigated areas, to give
variables adjusted for irrigation. Irrigated areas were identified from
(Siebert et al., 2005). Evapotranspiration data was extracted from
ENVIREM (Title \& Bemmels, 2018), whilst the precipitation data was
extracted from the WorldClim dataset.

Multicollinearity amongst the four variables was tested for using the
Pearson correlation coefficients. A value of more than\selectlanguage{english} 0.7 is considered
to indicate variables which covary too much, however, this didn't apply
to any of the four variables.

\textbf{Distribution data}

Global distribution data for GAS was downloaded from GBIF (ww.gbif.org).
Only records which were labelled as ``human observation'' or
``occurrence'' were retained. These records were then filtered to remove
any coordinates with high levels of uncertainty. Data on the presence of
GAS collected during this current study were then added to the cleaned
GBIF records. This represented our overall dataset, which consisted of
3219 records. This dataset was filtered so that only one presence was
recorded in each climatic grid-cell, resulting in a working dataset of
730 distributional records.

Statistical Species Distribution Models (SDMs) require information on
where a species is absent. Often there is insufficient verified data,
and thus ``pseudo-absences'' must be used. To allow us to use, and test
the predictive accuracy, of statistical SDM methods, ten sets of
pseudo-absences were sampled. Each of the sets of pseudo-absences were
restricted so that they were always within 500km of a verified GAS
location, but were outside of a grid cell occupied by a presence
location. An upper distance for the pseudoabsences was specified as this
has been shown to prevent models from contrasting completely different
climate conditions, e.g. temperate vs. tropical (VanDerWal et al.,
2009). The prevalence of the pseudo-absences was always equal to the
number of presence points (i.e. 730)

\textbf{Modelling}

An Ensemble model approach was used to predict the distribution of GAS
in Kenya and Eastern Africa. Ensemble modelling can generate a more
robust model and has been shown to overcome the uncertainties involved
with interpreting results from individual models (Araujo \& New, 2007;
Hao et al., 2019). The ensemble used included ten different modelling
techniques: Surface Range Envelope (SRE), Generalized linear models
(GLM), Generalized additive models (GAM), Multivariate adaptive
regression spline (MARS), Classification tree analysis (CTA), Flexible
Discriminant Analysis (FDA), Artificial neural network (ANN), Random
forest (RF), Generalized boosting method (GBM) and maximum entropy
(MAXENT). All analyses were conducted in R (Team, 2013) using the
biomod2 package (Thuiller et al., 2013) and default SDM settings
(Appendix 1).

Occurrence data were split randomly and 70\% were used as training data
for model calibration and the remaining 30\% was used to evaluate the
model's predictive performance. The Area under the receiver operating
curve (AUC) and True Skill Statistic (TSS) were used to assess the
accuracy of the model predictions compared to the validation data.

Ensembles were created using all models for which the validation TSS \selectlanguage{english}[?]
0.4, a value which is considered to signify models with moderate
performance (Landis \& Koch, 1977). To construct the Ensemble, the mean
suitability predicted by all the retained models was calculated,
weighted by the accuracy (TSS) of each model.

The importance of the environmental variables on the distribution of the
GAS was calculated using all models. For each variable, the variable was
randomised and model predictions were made with this shuffled dataset.
The Pearson's correlation (r) was then calculated between the model
predictions made with the original and those made with the shuffled
data. Importance was calculated as 1-r, so the higher the value, the
more influence the variable has on the model.

\textbf{Results}

\textbf{Delimiting Surveys}

Findings from this survey showed an expansion of invasion boundary from
the initial point of infestation in the Mwea rice scheme, with 80\% of
the scheme being infested with GAS. The survey did not find any adults
or eggs in Ahero, Bura, Hola and West Kano rice schemes and its out
growers in Kisumu and Tana River Counties.

The average number of adults/m\textsuperscript{2} and egg masses was 8.4
and 7.7, respectively. Out growers, Kiamanyeki and Ndekia sections in
the Mwea irrigation scheme recorded the highest average number of adults
(Figure 3). Table 2 shows the average number of
adults/m\textsuperscript{2} and egg masses recorded within each block
and section. Time had an impact on the density of adult GAS recorded
with sections sampled in the morning recording significantly higher
adult densities compared to those sampled in the afternoon (data not
shown). There were no adult or egg masses recorded in Karaba section.

Results showed that there was a statistically significant difference in
the number of adults/m\textsuperscript{2} and egg masses among the
sections; X\textsuperscript{2}(7) = 138.69, p\textless{} 0.001 and
X\textsuperscript{2}(7) = 114.17, p\textless{} 0.001, respectively. The
pairwise post-hoc Dunn's test with Bonferroni adjustments showed
significant differences in the number of adults/m\textsuperscript{2} and
egg masses between sections (Tables 3 \& 4). While rice was the main
economic host attacked there were no observed varietal differences in
the infestation and damage by GAS.

\textbf{Species distribution modelling}

Whilst all the individual SDM techniques yielded good results (AUC
\textgreater{} 0.8), RF had the best performance (AUC = 0.98) compared
to SRE which had the poorest performance (AUC= 0.84) (Table 5). All
models had TSS scores of above 0.4 and thus were all included in the
final Ensemble model.

The final Ensemble model had good predictive ability as indicated by TSS
scores of 0.80 and an AUC value of 0.95. Analysis into the importance of
the environmental variables showed that the driest quarter was the most
important environmental variable followed by mean temperature of the
coldest quarter and maximum temperature of the warmest month. Adjusted
annual precipitation was the least important variable (Table 6).

\textbf{Potential distribution of GAS}

Whilst there was nowhere in Kenya that was not suitable for GAS, the
extent to which areas were suitable varied significantly across the
country. Particularly high areas of suitability were found to the
South-East of the country, along the Tana river and the coast near
Mombasa (Figure 4). All three survey areas in this current study were
modelled as having high suitability for GAS. However, the occurrence can
only be realized in presence of rivers or irrigation water. Whilst no
GAS were found in Tana River and Kisumu counties, the high suitability
of these areas highlights the potential for further widespread
distribution of GAS into these areas.

Across the region, the countries with the highest suitability for GAS
were Malawi, Madagascar and Uganda. Mozambique, Tanzania and Ethiopia
also had areas of high suitability, but these were more concentrated in
specific areas of the country. In comparison, suitability across Sudan
and Somalia was very low (Figure 5).

\textbf{Discussion.}

Delimiting surveys are important in helping to establish the boundary of
the spread of a pest and to help in the management and development of
quarantine strategies to limit its spread to other risk areas (McMaugh,
2005). Knowledge of the boundary of invasion is important in appropriate
resource (financial, personnel, time etc.) allocation in the
surveillance and management of the pest.

GAS is listed among the world's worst invasive species' (Lowe et al.,
2000) and its impacts have been documented in various parts of the world
(Arfan et al., 2014; Correoso et al., 2017; Cowie, 2009; Cowie \& Hayes,
2012; Halwart, 1994; Hayes et al., 2008; Martin et al., 2017; Seuffert
\& Martin, 2013; Win, 2020; Yang et al, 2018). After its first report in
Mwea, Kenya in 2020, a delimiting survey to establish invasion boundary
was conducted in five leading rice-producing areas in Kenya. Results
from the survey showed an increase/expansion in the range from the
initial point of infestation (Ndekia) to other sections of the scheme.
Indeed, as reported in previous studies, several biotic, abiotic and
human activities have aided the spread of GAS and other invasive aquatic
snails around the world. The voracious appetite (Carlsson et al., 2004;
Qiu et al., 2011), high reproductive rate (Cowie, 2009; Cowie \& Hayes,
2012; Yusa, 2006), resistance to desiccation in dry down periods (Havel
et al., 2014), lack of effective natural enemies (Cowie, 2009; Yusa,
2006), and the presence of sufficient water (Cowie, 2009; Salleh et al.,
2012a; Teo, 2003) and food source- rice and wild host plants have been
confirmed to aid spread. In Mwea, as observed, the infestation has
followed or taken the route of slope and water flow i.e. along the river
and irrigation channels/canals gradient. The flow or movement of water
has the potential of spreading juvenile and adult snails downstream from
where they are able to begin a new infestation. Martin et al. (2017)
reported that the dispersal of GAS within streams depends on both
crawling and drifting. Indeed, the infested irrigation channels form
favourable habitats for the active and passive dispersal of snails,
acting as pathways for the spread of GAS due to the continuous presence
of water even during dry periods and supporting of secondary hosts for
GAS.

Knowledge of local risks is critical in instituting and implementing any
management practices for a pest (Finch et al., 2021). Species
distributional modelling for GAS highlighted areas in Kenya and Eastern
Africa which have the greatest potential for invasion. Of note, those
areas that were modelled as highly suitable, to the south-east of the
country and along coastland, harbour the major rice production areas in
the region. Besides, it's likely that these locations could serve as
high-risk areas because of the rich agricultural lands that have water
sources. While GAS has not been reported in other countries in Eastern
Africa, our results suggest large areas of high suitability across the
region. Of particular concern is Madagascar as it is one of the largest
rice producers in the region (FAO stat, 2020), and has high suitability
for GAS in the eastern and central parts of the country, where rice
production is at its highest. In addition, were GAS to enter and persist
in Lake Victoria, its spread into surrounding rice production in
Tanzania and Uganda could be rapid, and ongoing dissemination throughout
catchments and into major river systems such as the White Nile could be
disastrous for the region (Djeddour et al., 2021).

Sufficient food is an essential requirement for the establishment and
further dispersal of any species (invasive or otherwise) (Sanico et al.,
2002). Mwea, Ahero, Bura, Hola and West Kano schemes and indeed all the
rice schemes in Kenya, have majorly rice crop in the field at any
particular time. In instances where the rice crop is minimal or not
available and the paddies are dry, the canals hold water and suitable
hosts for the proliferation of GAS. Being its primary host, the GAS
population increase is inevitable. Time of sampling and as such, the
water temperature has an impact on the activity and density of adult GAS
recorded (Cameron, 1970; Winterbourn, 1969), and indeed the minimum
temperature was the second most important environmental variable for
predicting the potential distribution of GAS. During the survey, and
especially where sampling was done after midday (when the sun was
overhead), few to no adult numbers were recorded with sections sampled
in the morning (Kiamanyeki and Ndekia) on the other hand recorded
significantly higher adult densities compared to those sampled early
morning or late in the afternoon. This has implications for the timing
of management practices as efficacy will be achieved when GAS is active
as opposed to when they are submerged and inactive. However, on closer
observation, egg clutches were observed on plant stems an indication
that the adults were submerged below the water surface. Similarly,
Barnes et al. (2008) reported that the occurrence of egg clutches on
emergent substrates represents the first indicator of snail's presence
in an area, even at low adult density. Also, the high number observed in
these two sections could be because the first report of the GAS
infestation was confirmed in Ndekia. Taro and arrowroots stems were the
common egg laying substrates observed in the field. These findings
concur with those of other studies which reported the preference of GAS
to Taro (\emph{Colocasia esculanta} ), arrowroots (\emph{Maranta
arundinacea} ) and other broad-leaved aquatic weeds
e.g.\emph{Limnocharis flava} (Alismataceae), for oviposition (Arfan et
al., 2016; Joshi, 2007; Sanico et al., 2002).

\textbf{Management options}

Being a new pest in Kenya and indeed Africa, management of GAS has
mainly relied on cultural, physical and on a desperate scale, a
``trial-error approach'' on chemical practices. Similar results have
been reported around the world on the control of GAS in rice fields
(Joshi, 2007; Liang et al., 2014; Litsinger \& Estano, 1993;
Tangkoonboribun \& Sassanarakkit, 2009; Teo, 2003). Unfortunately, most
of these practices have proven to be either cumbersome, impractical,
expensive or ineffective. Worsening the situation is the unavailability
and inaccessibility of registered molluscicides in the country, which
has resulted in indiscriminate trial-error spraying further compounding
negative effects on environmental safety, human health and natural
enemies. Indeed, with the bio-ecology of GAS and its inevitable impacts
following the invasion, instituting an integrated approach that combines
cultural (water management, field hygiene etc.), physical (handpicking,
field screens etc.), biological (predators, snail parasitic
microorganisms, biopesticides etc.) and chemical control on an
areawide-scale is best for the management of this pest.

Material transfer, mechanisation and other human-mediated activities
have been found to aid the dispersal and introduction of pests in new
areas (Gippet et al., 2019; Litsinger \& Estano, 1993; Ranamukhaarachchi
\& Wickramasinghe, 2006). Whereas we did not find any adult GAS or their
eggs in Ahero, Bura, Hola or West Kano schemes, the authors learned of
the extensive material transfer and exchange (particularly seedlings of
the aromatic Pishori rice) and farm machinery (especially the combine
harvesters and rotavators) from Mwea- an infested area. While the
invasion boundary of GAS was limited to Mwea scheme, the transfer and
sharing of planting materials and machinery could exacerbate infestation
in lowly infested areas or lead to infestation in new areas. This could
pose a serious threat to rice farming in these areas should GAS invade
further compounding the already constrained rice industry. The
distribution model highlights these areas as highly suitable for
invasion. As such, and as the major purpose of a delimiting survey and
species distribution modelling, this information should aid in early
warning and contingency planning. Strict quarantine measures should be
developed and implemented to contain this invasive pest. Planting
material and machinery from Mwea should be thoroughly inspected and
disinfected before they are allowed into other schemes. Also having
records of the entry and exit of people, seedlings and machinery should
be implemented to have a traceable history and knowledge of
introduction.

While delimiting surveys are usually national, the one described in this
current study covered five rice-producing schemes, which represent over
80\% of the rice farming areas in Kenya. As such, while the findings are
representative, a further national survey incorporating time and space
variables of all the rice-producing and irrigated areas is proposed. To
manage and curb further spread especially to other risk areas, the
following containment measures are proposed: Undertake training and
awareness of and on GAS through National Irrigation Authority (NIA) with
support of other county teams; prevent field entry using a mesh as
physical barriers especially of the uninfested sections; practice
physical/mechanical control through hand picking of snails and crushing
of egg clutches; community-based snail management through synchronised
farm activities like land preparation and application of control
measures (an areawide management approach); introduce changes to the
cropping system by avoiding ratoon crops to limit host availability for
GAS; practice cultural practices like alternate wetting and drying (AWD)
of paddies; Change planting patterns by encouraging transplanted rice
compared to direct seeding; Manage water in canals by limiting water
levels less than 2 cm above the soil surface to reduce GAS movement and
dispersal; desilting of canals to minimize the habitable areas where the
snails lay their eggs and reduce their populations among other
practices.

\textbf{Conclusions}

This delimiting survey has established that GAS is spreading fast within
the Mwea Rice Scheme sections. The National Government through its
relevant agencies (particularly NIA, KEPHIS and the Plant Protection
Services Division (PPSD) of the Ministry of Agriculture and Livestock
Development), should bring together stakeholders in the rice value chain
to immediately create awareness and institute the control measures
proposed in this paper. This will also ensure that GAS does not spread
to other risk areas already identified in the rice-growing and indeed
other regions in the country and region. Rice is a crop grown in large
schemes in Kenya. This provides an ideal opportunity to institute
areawide GAS control measures, particularly community-based snail
management through synchronised farming activities including integrated
pest management strategies. Out growers should also be actively involved
in this process. National and International Research organisations
should be encouraged to come up with appropriate long-term control
measures that are sustainable and safe for the environment, particularly
the use of biological control methods e.g. snail parasitic
microorganisms in an IPM package. The establishment of pest-free areas
(PFAs) especially in Western Kenya and Tana River basin and other
regions where infestation has not been reported, is an important step
towards effective management of GAS. The development of a policy brief
for consideration by the National Government would kick-start this
process.

\textbf{Acknowledgement}

The authors wish to thank the management of the Mwea, Ahero, Bura, Hola
and West Kano irrigation schemes who facilitated access to the scheme
for the survey. All the extension officers and lead farmers who helped
in trace-back information and the survey are duly acknowledged. Finally,
anonymous reviewers are thanked for their constructive comments

\textbf{Authors' contributions}

FM, DC, AM and IR contributed to the study conception and design,
undertook the initial survey and provided detail on the survey
methodology. EF performed modelling studies and data analysis; IM
conducted data analysis. FM and EF wrote the first draft of the
manuscript. All authors revised and approved the final manuscript.

\textbf{Funding}

The study was conducted as part of CABI's PlantwisePlus programme, which
is funded by the UK Foreign, Commonwealth and Development Office (FCDO),
Netherlands Directorate-General for International Cooperation (DGIS),
Swiss Agency for Development and Cooperation (SDC) and the European
Commission (DG INTPA). CABI is an international intergovernmental
organisation and gratefully acknowledges the core financial support from
our member countries (and lead organisations) including the United
Kingdom (Department for International Development) (Foreign,
Commonwealth and Development Office), China (Chinese Ministry of
Agriculture and Rural Affairs), Australia (Australian Centre for
International Agricultural Research), Canada (Agriculture and Agri-Food
Canada), the Netherlands (Directorate-General for International
Cooperation), Switzerland (Swiss Agency for Development and
Cooperation), and Ireland (Irish Aid, International Fund for
Agricultural Development-IFAD). See
http://www.cabi.org/about-cabi/who-we-work-with/key-donors/ for full
details

\textbf{Data availability}

The data that support the findings of this study are available from the
current study (presented in the article); the survey which first
discovered GAS in Kenya (Buddie et al., 2021), and from GBIF, which
archives the data open access. All the data has been published
previously.

\textbf{Declarations}

\textbf{Conflict of interest}

The authors have no relevant financial or non-financial interests to
disclose.

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\textbf{Tables}

\textbf{Table 1} : Survey site locations showing (left-to-right): name
of county, name of irrigation scheme, section name, block name,
latitude, longitude and altitude\selectlanguage{english}
\begin{longtable}[]{@{}lllllll@{}}
\toprule
\textbf{County} & \textbf{Scheme} & \textbf{Section} & \textbf{Block} &
\textbf{Latitude} & \textbf{Longitude} &
\textbf{Altitude}\tabularnewline
\midrule
\endhead
Kirinyaga & Mwea & Kiamanyeki & Kiamanyeki & -0.69598 & 37.38399 &
1130.964\tabularnewline
Kirinyaga & Mwea & Tebere & T22 & -0.6826 & 37.41321 &
1130.789\tabularnewline
Kirinyaga & Mwea & Tebere & T25 & -0.68013 & 37.41974 &
1127.933\tabularnewline
Kirinyaga & Mwea & Tebere & T11 & -0.67137 & 37.41266 &
1136.626\tabularnewline
Kirinyaga & Mwea & Tebere & T8 & -0.65554 & 37.40119 &
1148.359\tabularnewline
Kirinyaga & Mwea & Tebere & T6/7 & -0.65648 & 37.39282 &
1156.951\tabularnewline
Kirinyaga & Mwea & Tebere & T67 & -0.65394 & 37.39147 &
1156.443\tabularnewline
Kirinyaga & Mwea & Tebere & T15 & -0.67471 & 37.39326 &
1144.631\tabularnewline
Kirinyaga & Mwea & Tebere & T18 & -0.67598 & 37.39271 &
1141.957\tabularnewline
Kirinyaga & Mwea & Tebere & T21 & -0.68456 & 37.39308 &
1135.785\tabularnewline
Kirinyaga & Mwea & Tebere & T17 & -0.67916 & 37.39133 &
1135.878\tabularnewline
Kirinyaga & Mwea & Tebere & T20 & -0.67643 & 37.37517 &
1146.468\tabularnewline
Kirinyaga & Mwea & Tebere & T16/13 & -0.67643 & 37.37522 &
1145.216\tabularnewline
Kirinyaga & Mwea & Tebere & T13 & -0.65388 & 37.37233 &
1172.261\tabularnewline
Kirinyaga & Mwea & Tebere & T5 & -0.64891 & 37.37055 &
1178.281\tabularnewline
Kirinyaga & Mwea & Tebere & T2 & -0.635 & 37.36634 &
1190.863\tabularnewline
Kirinyaga & Mwea & Tebere & T3 & -0.63667 & 37.37067 &
1187.551\tabularnewline
Kirinyaga & Mwea & Ndekia & 2B & -0.65557 & 37.3596 &
1156.595\tabularnewline
Kirinyaga & Mwea & Ndekia & 3 & -0.64652 & 37.3462 &
1189.205\tabularnewline
Kirinyaga & Mwea & Ndekia & 4 & -0.62942 & 37.33787 &
1193.442\tabularnewline
Kirinyaga & Mwea & Ndekia & 1 & -0.62793 & 37.35605 &
1191.451\tabularnewline
Kirinyaga & Mwea & Mwea & M4 & -0.65947 & 37.34339 &
1181.241\tabularnewline
Kirinyaga & Mwea & Mwea & M6/7 & -0.65274 & 37.31306 &
1189.667\tabularnewline
Kirinyaga & Mwea & Mwea & MIAD & -0.64865 & 37.30164 &
1193.927\tabularnewline
Kirinyaga & Mwea & Mwea & M17 & -0.66471 & 37.31534 &
1178.342\tabularnewline
Kirinyaga & Mwea & Mwea & M14 & -0.68421 & 37.31312 &
1166.108\tabularnewline
Kirinyaga & Mwea & Thiba & H20 & -0.68795 & 37.33216 &
1163.23\tabularnewline
Kirinyaga & Mwea & Thiba & H5 & -0.71707 & 37.34196 &
1151.891\tabularnewline
Kirinyaga & Mwea & Wamumu & W7 & -0.73445 & 37.33957 &
1142.041\tabularnewline
Kirinyaga & Mwea & Wamumu & W5 & -0.74663 & 37.34327 &
1136.672\tabularnewline
Kirinyaga & Mwea & Wamumu & W3 & -0.73131 & 37.37017 &
1128.687\tabularnewline
Kirinyaga & Mwea & Thiba & H7 & -0.70129 & 37.35384 &
1121.972\tabularnewline
Kirinyaga & Mwea & Outgrower & Ngothi/ Mugaa & -0.68567 & 37.28479 &
1145.008\tabularnewline
Kirinyaga & Mwea & Outgrower & Kandongu Upper & -0.67544 & 37.30454 &
1176.011\tabularnewline
Kirinyaga & Mwea & Karaba & K2 & -0.7401 & 37.36328 &
1130.389\tabularnewline
Kirinyaga & Mwea & Karaba & K4 & -0.74651 & 37.38684 &
1136.36\tabularnewline
Kirinyaga & Mwea & Karaba & K7 & -0.73796 & 37.41393 &
1114.962\tabularnewline
Kirinyaga & Mwea & Kimbimbi & Kimbimbi & -0.61966 & 37.36361 &
1172.615\tabularnewline
Kisumu & West Kano & West Kano F & F2 & -0.18069 & 34.80642 &
1135.034\tabularnewline
Kisumu & West Kano & West Kano E & E1 & -0.18243 & 34.8114 &
1137.53\tabularnewline
Kisumu & West Kano & West Kano E & H & -0.187556 & 34.81456 &
1130.178\tabularnewline
Kisumu & West Kano & West Kano J & J & -0.195628 & 34.82079 &
1131.656\tabularnewline
Kisumu & West Kano & West Kano C & C3 & -0.202834 & 34.82034 &
1130.011\tabularnewline
Kisumu & West Kano & West Kano C & C4 & -0.209996 & 34.82514 &
1135.788\tabularnewline
Kisumu & West Kano & West Kano B & B2 & -0.212803 & 34.83126 &
1128.359\tabularnewline
Kisumu & West Kano & West Kano A & A & -0.219084 & 34.83573 &
1134.457\tabularnewline
Kisumu & West Kano & West Kano & West Kano Outgrower1 & -0.205708 &
34.85217 & 1139.142\tabularnewline
Kisumu & Ahero & Ahero & Ahero A & -0.1361154 & 34.96645 &
1167\tabularnewline
Kisumu & Ahero & Ahero & Ahero C & -0.1403756 & 34.94051 &
1161\tabularnewline
Kisumu & Ahero & Ahero & Ahero F & -0.127424 & 34.95957 &
1165\tabularnewline
Kisumu & Ahero & Ahero & Ahero K & -0.1453987 & 34.92973 &
1155\tabularnewline
Kisumu & Ahero & Ahero & Ahero L & -0.1453987 & 34.92973 &
1155\tabularnewline
Kisumu & Ahero & Ahero & Ahero N & -0.1629546 & 34.91958 &
1137\tabularnewline
Kisumu & Ahero & Ahero & Ahero Outgrower & -0.1276082 & 34.93182 &
1163\tabularnewline
Tana River & Hola & Hola & Hola Area 6 & -1.49819 & 40.03611 &
62\tabularnewline
Tana River & Hola & Hola & Hola Commercial farm1 & -1.421 & 40.00595 &
71\tabularnewline
Tana River & Hola & Hola & Hola Commercial farm2 & -1.40727 & 40.00875 &
74\tabularnewline
Tana River & Hola & Hola & Hola Area 4-5 & -1.39731 & 39.99625 &
75\tabularnewline
Tana River & Hola & Hola & Hola Commercial farm3 & -1.39927 & 39.99839 &
73\tabularnewline
Tana River & Bura & Bura & Hola Area 1 & -1.48108 & 39.99122 &
72\tabularnewline
Tana River & Bura & Bura & Chewele Branch & -1.14718 & 39.82289 &
102\tabularnewline
Tana River & Bura & Bura & Village 6 & -1.12421 & 39.84693 &
98\tabularnewline
Tana River & Bura & Bura & Village 6a & -1.12436 & 39.85062 &
99\tabularnewline
Tana River & Bura & Bura & Village 4-5 & -1.1147 & 39.86814 &
93\tabularnewline
Tana River & Bura & Bura & BCF- Bura Commercial Farm & -1.14595 &
39.86618 & 96\tabularnewline
Tana River & Bura & Bura & Village 8 & -1.14593 & 39.86628 &
96\tabularnewline
Tana River & Bura & Bura & Village 10 & -1.188393 & 39.85943 &
98\tabularnewline
\bottomrule
\end{longtable}

\textbf{Table 2} : Average number of adults and egg masses recorded, by
section and block in Mwea scheme\selectlanguage{english}
\begin{longtable}[]{@{}llll@{}}
\toprule
\begin{minipage}[b]{0.22\columnwidth}\raggedright\strut
Section\strut
\end{minipage} & \begin{minipage}[b]{0.22\columnwidth}\raggedright\strut
Block\strut
\end{minipage} & \begin{minipage}[b]{0.22\columnwidth}\raggedright\strut
Average number of Adults/m\textsuperscript{2}\strut
\end{minipage} & \begin{minipage}[b]{0.22\columnwidth}\raggedright\strut
Average number of Egg masses\strut
\end{minipage}\tabularnewline
\midrule
\endhead
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Kiamanyeki\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Kiamanyeki\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
21\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
5.5\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Mwea\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Mwea\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
0\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
0.4\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
M14\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
0\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
0\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
M17\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
0\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
0\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
M4\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
0\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
1.2\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Ndekia\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
1\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
32.3\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
12.1\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
3\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
11.9\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
41\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
4\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
15.8\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
19.4\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
2b\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
7.5\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
2.9\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Out grower\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Kandongu\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
39\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
8.9\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Tebere\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
T11\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
0\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
6.3\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
T13\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
14.1\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
1.1\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
T14\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
0\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
4.7\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
T15\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
0\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
7.7\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
T16\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
0.8\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
5.6\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
T17\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
0\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
0.4\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
T18\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
4.7\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
1.7\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
T2\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
2\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
11.4\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
T20\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
8.2\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
97\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
T21\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
6.5\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
6.9\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
T22\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
0\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
0\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
T3\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
59\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
0.5\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
T5\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
18.9\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
2.9\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
T6/T7\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
0\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
10.3\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
T8\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
0\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
0\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Thiba\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
H20\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
0\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
0\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
H5\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
0\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
0\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
H6/H7\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
2.7\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
0\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
H6/H8\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
4\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
0\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
Wamumu\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
W3\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
1.4\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
0\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
W5\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
2.8\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
0\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
W7\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
0\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
0\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

\textbf{Table 3} : Dunn's Pairwise Comparison of egg masses by section\selectlanguage{english}
\begin{longtable}[]{@{}llllll@{}}
\toprule
& Karaba & Mwea & Ndekia & Outgrower & Tebere\tabularnewline
\midrule
\endhead
Kiamanyeki & -1.67 & & & &\tabularnewline
Mwea & -1.06 & & & &\tabularnewline
Ndekia & -7.29* & -5.67* & & &\tabularnewline
Outgrower & -5.42* & -4.55* & -0.88 & &\tabularnewline
Tebere & -5.41* & -3.63* & 3.24* & 2.73 &\tabularnewline
Thiba & 0 & 0.99 & 6.73* & 5.25* & 4.86*\tabularnewline
Wamumu & 0 & 0.99 & 6.73* & 5.25* & 4.86*\tabularnewline
\bottomrule
\end{longtable}

*denotes significance at the 0.05 level

\textbf{Table 4} : Dunn's Pairwise Comparison of
adults/m\textsuperscript{2} by section\selectlanguage{english}
\begin{longtable}[]{@{}lllllll@{}}
\toprule
& Karaba & Kiamanyeki & Mwea & Ndekia & Outgrower &
Tebere\tabularnewline
\midrule
\endhead
Kiamanyeki & -4.8* & & & & &\tabularnewline
Mwea & 0 & 4.7* & & & &\tabularnewline
Ndekia & -8.67* & 0.43 & -8.02* & & &\tabularnewline
Outgrower & -6.83* & -0.6 & -6.61* & -1.41 & &\tabularnewline
Tebere & -4.96* & 2.79 & -4.46* & 5.36* & 4.48* &\tabularnewline
Thiba & -1.81 & 3.72* & -1.69 & 6.19* & 5.41* & 2.36\tabularnewline
Wamumu & -2.85 & 3.16* & -2.67 & 5.14* & 4.72* & 1.15\tabularnewline
\bottomrule
\end{longtable}

*denotes significance at the 0.05 level

\textbf{Table 5:} Summary statistics for all the models made, along with
standard interpretations of the values in parentheses\selectlanguage{english}
\begin{longtable}[]{@{}lll@{}}
\toprule
Model technique & Mean AUC (+SD) & Mean TSS (+SD)\tabularnewline
SRE & 0.84 (+0.01) & 0.68 (+0.02)\tabularnewline
GLM & 0.91 (+0.01) & 0.74 (+0.01)\tabularnewline
GAM & 0.93 (+0.02) & 0.79 (+0.03)\tabularnewline
MARS & 0.93 (+0.01) & 0.77 (+0.02)\tabularnewline
CTA & 0.92 (+0.03) & 0.81 (+0.03)\tabularnewline
FDA & 0.93 (+0.01) & 0.77 (+0.02)\tabularnewline
ANN & 0.88 (+0.03) & 0.74 (+0.04)\tabularnewline
GBM & 0.97 (+0.01) & 0.84 (+0.02)\tabularnewline
RF & 0.98 (+0.01) & 0.98 (+0.01)\tabularnewline
MAXENT & 0.95 (+0.01) & 0.68 (+0.02)\tabularnewline
\bottomrule
\end{longtable}

\textbf{Table 6:} Importance of variables for Species Distribution
Models (SDMs) of GAS. Values are the mean (+ standard deviation) of the
results across all SDM techniques\selectlanguage{english}
\begin{longtable}[]{@{}ll@{}}
\toprule
Variable & Importance\tabularnewline
\midrule
\endhead
Adjusted precipitation in the driest quarter & 0.37
(\selectlanguage{ngerman}±0.16)\tabularnewline
Adjusted annual precipitation & 0.23 (\selectlanguage{ngerman}±0.16)\tabularnewline
Maximum temperature of the warmest month & 0.25 (\selectlanguage{ngerman}±0.11)\tabularnewline
Minimum Temperature of Coldest Month & 0.31 (\selectlanguage{ngerman}±0.13)\tabularnewline
\bottomrule
\end{longtable}\selectlanguage{ngerman}

\textbf{Figure Legends}

\textbf{Figure 1} : Adult Golden apple snail, \emph{Pomacea
canaliculata}

\textbf{Figure 2} : Golden Apple Snail (\emph{P. canaliculata} ) survey
locations in Mwea (Kirinyaga County), Bura and Hola (Tana River County)
and Ahero and West Kano (Kisumu County)

\textbf{Figure 3} : Average number of adults/m\textsuperscript{2} and
egg masses, by section in Mwea irrigation scheme

\textbf{Figure 4} : Environmental suitability for Golden Apple Snail
(\emph{P. canaliculata} ) across Kenya as predicted by an ensemble of
SDMs. SDMs were permitted into the ensemble if the TSS from internal
cross-validation was \selectlanguage{english}[?]0.4. The ensemble was calculated as the mean of
projections from all permitted SDMs, each model weighted by the
cross-validated TSS. Points represent survey locations described in this
paper

\textbf{Figure 5} : Environmental suitability for Golden Apple Snail
(\emph{P. canaliculata} ) across Eastern Africa as predicted by an
ensemble of SDMs. SDMs were permitted into the ensemble if the TSS from
internal cross-validation was \selectlanguage{english}[?]0.4. The ensemble was calculated as the
mean of projections from all permitted SDMs, each model weighted by the
cross-validated TSS. Points represent survey locations described in this
paper

\emph{Appendix 1}

GLM = list( type = 'quadratic',

interaction.level = 0,

myFormula = NULL,

test = 'AIC',

family = binomial(link = 'logit'),

mustart = 0.5,

control = glm.control(epsilon = 1e-08, maxit = 50, trace = FALSE) ),

GBM = list( distribution = 'bernoulli',

n.trees = 2500,

interaction.depth = 7,

n.minobsinnode = 5,

shrinkage = 0.001,

bag.fraction = 0.5,

train.fraction = 1,

cv.folds = 3,

keep.data = FALSE,

verbose = FALSE,

perf.method = 'cv',

n.cores = 1),

GAM = list( algo = 'GAM\_mgcv',

type = 's\_smoother',

k = -1,

interaction.level = 0,

myFormula = NULL,

family = binomial(link = 'logit'),

method = 'GCV.Cp',

optimizer = c('outer','newton'),

select = FALSE,

knots = NULL,

paraPen = NULL,

control = list(nthreads = 1, ncv.threads = 1, irls.reg = 0, epsilon =
1e-07, maxit = 200

, trace = FALSE, mgcv.tol = 1e-07, mgcv.half = 15, rank.tol =
1.49011611938477e-08

, nlm = list(ndigit=7, gradtol=1e-06, stepmax=2, steptol=1e-04,
iterlim=200, check.analyticals=0)

, optim = list(factr=1e+07)

, newton = list(conv.tol=1e-06, maxNstep=5, maxSstep=2, maxHalf=30,
use.svd=0), outerPIsteps = 0

, idLinksBases = TRUE, scalePenalty = TRUE, efs.lspmax = 15, efs.tol =
0.1, keepData = FALSE

, scale.est = fletcher, edge.correct = FALSE) ),

CTA = list( method = 'class',

parms = 'default',

cost = NULL,

control = list(xval = 5, minbucket = 5, minsplit = 5, cp = 0.001,
maxdepth = 25) ),

ANN = list( NbCV = 5,

size = NULL,

decay = NULL,

rang = 0.1,

maxit = 200),

SRE = list( quant = 0.025),

FDA = list( method = 'mars',

add\_args = NULL),

MARS = list( type = 'simple',

interaction.level = 0,

myFormula = NULL,

nk = NULL,

penalty = 2,

thresh = 0.001,

nprune = NULL,

pmethod = 'backward'),

RF = list( do.classif = TRUE,

ntree = 500,

mtry = 'default',

sampsize = NULL,

nodesize = 5,

maxnodes = NULL),

MAXENT = list( path\_to\_maxent.jar = 'C:/Users/finche/OneDrive -
CABI/Projects/Other/RSW',

memory\_allocated = 512,

initial heap size = NULL,

maximum heap size = NULL,

background\_data\_dir = 'default',

maximumbackground = 'default',

maximumiterations = 200,

visible = FALSE,

linear = TRUE,

quadratic = TRUE,

product = TRUE,

threshold = TRUE,

hinge = TRUE,

lq2lqptthreshold = 80,

l2lqthreshold = 10,

hingethreshold = 15,

beta\_threshold = -1,

beta\_categorical = -1,

beta\_lqp = -1,

beta\_hinge = -1,

betamultiplier = 1,

defaultprevalence = 0.5),

MAXNET = list( myFormula = NULL,

regmult = 1,

regfun = \textless{}function\textgreater{} )

)\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-1/Figure-1}

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

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

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-4/Figure-4}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-5/Figure-5}

\end{center}
\end{figure}

\selectlanguage{english}
\FloatBarrier
\end{document}

