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\title{Using~ the novel priority index in prioritizing the selection of inland
water bodies for site-based fish~ species conservation~~~~~~~~~~~}



\author[1]{Anthony  Basooma}%
\author[1]{Herbert Nakiyende}%
\author[1]{Mark Olokotum}%
\author[1]{Winnie Nkalubo}%
\author[1]{Laban Musinguzi}%
\author[1]{Vianny Natugonza}%
\affil[1]{National Fisheries Resources Research Institute }%


\vspace{-1em}



  
  \date{April 29, 2026}


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\selectlanguage{english}
\begin{abstract}
Freshwater ecosystems occupy \textless{}1\% of the Earth's total surface
area but provide an array of ecosystem services. However, these
ecosystems are threatened by multiple stressors, including
overexploitation, infrastructure developments, habitat alteration, and
alien species introductions. The magnitude of these threats varies in
different water bodies, requiring site-based conservation actions. In
this paper, we aimed at developing a priority index (CPIw) that can be
used to inform conservation managers in prioritizing the selection of a
waterbody for site-based fish conservation purposes. We used data on
distribution, diversity, and conservation status of fishes of Uganda,
which were retrieved from the Global Biodiversity Information Facility
(GBIF) and International Union Conservation for Nature (IUCN) databases.
In the index, we incorporated the species richness, surface area of a
waterbody, species rarity, and species IUCN status. A total of 288 fish
species were recorded in 81 waterbodies (7 large lakes, 37 small lakes,
and 37 rivers). Of these species, 110 were only found in large lakes,
followed by rivers (19) and small lakes (6). Despite the higher species
richness in large lakes relative to small lakes, the latter recorded
significantly higher CPIw compared with the former (t = -2.8, df = 30,
p-value = 0.008, d=0.7). This observation is consistent with the
expectation, given the low ecological substitutability for the species
and higher levels of exposure to human-induced threats in small water
bodies compared with large systems. Therefore, we suggest that in
situations where resources are limiting, small water bodies need to be
given much attention, although we do not suggest ignoring water bodies
with low CPIw values.%
\end{abstract}%



\sloppy




\section*{Introduction}

{\label{introduction}}

Freshwaters constitute \textasciitilde{}3\% of the Earth's water (WWF
2020) and occupy \textless{}1\% of the Earth's surface area
(Garcia-Moreno et al. 2014), but is habitat to about 40\% of all
described fish species (Lundberg et al. 2000). The benefits of these
freshwater resources to society are immense. For example, 21 largest
lakes in the world provide about 1.3 million tons of fish annually, 62.5
GW of hydropower, 5 billion m\textsuperscript{3} of potable drinking
water, and 815 million m\textsuperscript{3} of water for irrigation
(Sterner et al. 2020). Also, freshwater systems replenish estuarine,
oceans, and seas with nutrients and water (Matthews 2016); for example,
Nile River deposits 1318 and 212 kg
km\textsuperscript{-2}yr\textsuperscript{-1} of nitrogen and phosphorus,
respectively, into the Mediterranean Sea (Yasin et al. 2010).

Freshwater species are more susceptible to human-induced threats such as
climate change, pollution, habitat alteration, overexploitation, and
alien species introductions compared with marine and terrestrial species
(Darwall et al. 2018; WWF 2020). For example, while marine and
terrestrial species have declined by 39\% in the last 50 years,
freshwater counterparts have reduced by 76\% higher than the global
average of 52\% (WWF 2014). Freshwater fish species might be the most
threatened vertebrates assessed by IUCN (Reid et al. 2013). The species
are affected by the high levels of exploitation to support about 158
million people worldwide who derive animal protein from freshwater fish
species (McIntyre et al. 2016).

Approximately 500 fish species are reported in all water bodies in
Uganda (NEMA 2007; Natugonza \& Musinguzi, 2020). However, several
non-native fishes, including Nile perch (\emph{Lates niloticus),} Nile
tilapia (\emph{Oreochromis niloticus} ), redbelly tilapia
(\emph{Coptodon zillii)} , blue-spotted tilapia (\emph{Oreochromis
leucostictus} ), and redbreast tilapia (\emph{Coptodon rendalii} ) were
introduced into various lakes and rivers within the Victoria and Kyoga
lake basins (Kishe-Machumu et al. 2018). These introductions especially
for Nile perch coincided with the collapse of most native fish species
(Ogutu-Ohwayo 1990). In Lake Victoria, \textasciitilde{}300+
haplochromine cichlids were extirpated (Kaufman 1992; Ogutu-Ochwayo
1990), and similar destructive ecological changes were observed in lakes
Kyoga and Nabugabo (Ogutu-Ohwayo 1990; Chapman et al. 1996). This loss
in haplochromine cichlids is believed to be the worst vertebrate species
extinction observed in recent times (Kaufman 1992), placing Nile perch
among the 100 top worst alien invasive species in the world (Lowe et al.
2000). Other native species such as Singidia tilapia (\emph{Oreochromis
esculentus} ) and Ningu (\emph{Labeo victorianus} ), which previously
dominated in fish catches from Lake Victoria and its affluent rivers
(Kudhongania et al. 1992) are currently classified as critically
endangered (IUCN 2020). The catfishes, including Semutundu (\emph{Bagrus
docmak} Forssk\selectlanguage{ngerman}ål, 1775), Lake Victoria deepwater catfish
(\emph{Xenoclaris eupogon} ), \emph{Clarias} spp\emph{.}
,\emph{Synodontis} spp., and Silver catfish (\emph{Schilbe intermedius}
) were also affected by Nile perch establishment in Lake Victoria
(Goudswaard \& Witte 1997; Balirwa 1998; Balirwa et al. 2003). Riverine
species were mostly affected by overexploitation and habitat
degradation, while the native tilapines declined mostly through
interspecific competition and hybridization (Cadwalladr 1965;
Kudhongania et al. 1992).

The reduction in fish species diversity in Uganda has led to numerous
studies focussing on the species diversity, abundances, distribution,
taxonomy, and biology of the remnant species to facilitate their
recovery and reduce further extinctions (Witte \& Van Oijen 1990;
Kaufman \& Ochumba 1993; Ogutu-Ohwayo 1993; Ogutu-Ohwayo et al. 1999;
Mbabazi et al. 2004). In particular, small lakes, swamps, rivers,
streams, and wetlands were documented as the main structural refugia for
these fishes (Ogutu-Ohwayo et al. 1999; Mwanja et al. 2001; Chapman et
al. 2002; Mbabazi et al. 2004; Balirwa et al., 2003; Wakwabi et al.
2006; Olwa et al., 2020). However, these studies were waterbody-specific
with limited information to rank the waterbodies for site-based
conservation given the limited available resources. Analysis of the
distribution of fishes at a broader scale has been limited in the past
due to the paucity of data, which have been scattered in many research
institutions in unusable formats. Recently, substantial amounts of data
on the occurrence of fishes for Uganda have been made available through
the Global Biodiversity Information Facility portal (GBIF) (GBIF, 2020).
This study aims to develop a Conservation Priority Index (CPIw) for
inland water bodies to prioritize their selection for site-based fish
conservation, especially when resources are limiting. We use data on the
distribution, diversity, and conservation status of the fish species in
Uganda, which are freely accessible through GBIF and IUCN databases.

\section*{Methods}

{\label{methods}}

\subsection*{Data acquisition and
processing}

{\label{data-acquisition-and-processing}}

We retrieved occurrence records from two fish classes (Actinopterygii
and Sarcopterygi) in Ugandan water bodies from GBIF online data
repository (GBIF 2020). We used the \emph{occ\_download\_get} function
in \emph{rgbif} package to retrieve data (Chamberlain et al. 2020).
Except for genera \emph{Astatoreochromis and Pseudocrenilabrus} , we
changed all other haplochromine cichlids genera to \emph{Haplochromis}to
conform with FishBase nomenclature (Froese \& Pauly 2019), which is
based on Oijen (1996). Thus, names such as \emph{Astatotilapia
nubila}were changed to \emph{Haplochromis nubilus} ; \emph{Schubotzia
eduardiana} to \emph{H. eduardianus} ; and \emph{Astatotilapia} pallida
to \emph{H. pallidus} . Occurrences that were outside the geographic
range described in FishBase, and whose identity could not be verified
based on recent survey data, were discarded. We also excluded all
occurrences without complete scientific names (genus and specific
epithet), e.g., \emph{Haplochromis} sp. and \emph{Oreochromis} sp.
Occurrences with unknown and incorrect water bodies were excluded, e.g.,
all records of \emph{H. eduardii} that were recorded in Lake Albert in
the GBIF datasets were discarded, as the species is endemic to Lake
Edward (Froese \& Pauly 2019). For occurrence records without a named
waterbody of origin but with coordinates were determined based on the
GPS coordinates. We used habitat descriptions, verbatim locality, and
location remarks to identify the waterbody of origin. We also discarded
all occurrences from manmade water bodies, such as ponds, tanks, and
aquarium. Lakes Salisbury and Kasudho were changed to Bisina and Kasodo,
respectively, to conform to the current names and avoid duplication of
records for the same lakes. We categorized lakes \textless{}200
km\textsuperscript{2} as small lakes and
\textgreater{}200km\textsuperscript{2} as large lakes, resulting in 7
large and 37 small lakes (Appendix S1 and S2). After preliminary
processing of the data, a total of 14,452 occurrences records were
retained for further analysis.

\subsection*{Conservation priority index
formulation}

{\label{conservation-priority-index-formulation}}

We retrieved the conservation status of each species from the
International Union for Conservation of Nature (IUCN) Redlist database
(www.iucnredlist.org/). The species are classified as data deficient
(DD), least concern (LC), critically endangered (CR), near threatened
(NT), endangered (EN), and not evaluated (NE) (IUCN 2012). We used
waterbody surface area, species richness, IUCN statuses, rarity, and
scaling constant to develop the conservation priority index (CPIw),
based on the formula:

\begin{equation}
\mathrm{CPIw\ =}\ \frac{\sum_{\mathrm{i\ =\ 1}}^{\mathrm{n}}{\mathrm{\text{Cwt}}_{\mathrm{i}}\mathrm{\text{.\ R}}_{\mathrm{i}}}}{\mathrm{(Aw\ .\ 7)}}\nonumber \\
 \end{equation}

where, for species \emph{i} per unit surface area of a waterbody,
Cwt\textsubscript{i} is the species weight based on its IUCN status
(i.e., CR (5), EN (4), VU (3), NT (2), LC (1)); n is the number of
species in a particular waterbody; Aw is the total surface area of the
lake, and Ri is the frequency of occurrence of a species in the water
bodies (i.e., if a species occurred in one waterbody, then a weight of 5
was assigned, 2-3 water bodies (weight 4), 4-5 water bodies (weight 3),
6-10 water bodies (weight 2), and \textgreater{}10 water bodies (weight
1)). The value 7 is a scaling constant, indicating the total number of
IUCN categories (IUCN, 2012). Note that fish species that were
registered as NE and DD in the IUCN database were assigned a
Cwt\textsubscript{i} of 5 on the basis that such species can go extinct
unnoticed and, therefore, should be considered in the same category as
CR species (IUCN 2012). The surface area for each lake was obtained from
the literature (Burgis \& Symoens 1987; Vanden Bossche \& Bernacsek
1990; Ogutu-Ohwayo et al. 1999; Schofield \& Chapman 1999; Olowo et al.
2004). We used Google Earth to approximate the surface area for lakes
Gawa, Kabaleka, Wamala, Nakabale, Owapet, Kirimira, and Kabaka because
it was not found in the literature. The surface area of Lakes Natuali,
Chankaranga, Okurachere, Kasunju, Nkuruba, and Mutabyo could not be
determined from both literature and Google Earth, and thus we could not
calculate their CPIw values.

\textbf{Data analysis}

For each water body category (small lakes, large lakes, and rivers), we
determined the species richness and the total number of species in IUCN
status categories. We used a non-parametric Kruskal Wallis test to
assess the mean differences in the species richness and IUCN categories
among waterbody categories. Post hoc multiple comparisons were conducted
with a Dunn's test to determine the statistical differences between the
waterbody categories. We generated a species accumulation curve for the
waterbody to assess if most of the species found in the data. We
determined the rarity of a particular species by summing the frequency
of occurrence in the water bodies where it was found. We used the Bray
Curtis dissimilarity measure to compute the ranks for water bodies and
species and later used a non-metric multidimensional scaling (nMDS) to
visualize the species and water bodies in 2-D ordination space. After,
we performed an analysis of similarity (ANOSIM) to determine the
statistical differences among waterbody categories. A similarity
percentage analysis (Simper) was used to evaluate the contribution of
the species to the dissimilarities between waterbody categories.

For the index, we log-transformed the CPIw values and used a
Shapiro-Wilk and Levene tests to examine for normality and equality of
variance, respectively. After, we used a parametric Welch 2-Sample
t-test to evaluate the differences between the mean CPIw values for
large and small lakes. We processed data with predefined functions in R
(including \emph{specaccum,} \emph{diversity, metaNMDS, simper, anosim,}
) of the \emph{Vegan} package (Oksanen et al. 2019), \emph{dunn test} in
Dunn package (Dinno, 2017).

\section*{Results}

{\label{results}}

\subsection*{Species composition and
rarity}

{\label{species-composition-and-rarity}}

A total of 288 species were recorded from 81 water bodies (i.e., 7 large
lakes, 37, small lakes, and 37 rivers) (Fig 1 \& Appendix S2). Of the
288 species, 163 were haplochromine cichlids and 125 non-haplochromine.
The species accumulation curve increased at a low rate after 20 water
bodies (Fig 2). All haplochromine cichlids were recorded in 45 water
bodies compared with 75 for non-haplochromines. Species richness was
highest in large lakes dominated by Lake Victoria with 175 species
followed by Albert, Kyoga, and George (Fig 1, Appendix S2). Kruskal
Wallis test showed that waterbody categories differed significantly in
mean species richness, and the IUCN status categories (Table 1).
However, the nature of a waterbody had a weak effect on the variation in
species richness and IUCN categories (Table 1). Post hoc multiple
comparisons showed that large lakes were significantly different from
rivers and small lakes for all IUCN categories and species richness.
However, except for the vulnerable category, rivers and small lakes were
not significantly different (Table 1).

Of the 163 haplochromine cichlids, 85 species were found in utmost one
waterbody compared with 31 non-haplochromine species (Fig 3). Lake
Victoria had 71 rare species, where 11 were critically endangered
haplochromine cichlids and one non-haplochromine species
(\emph{Xenoclarias eupogon} ) (Appendix S3). Two near threatened species
(\emph{H. labiatus and H. oregosoma} ) were only found in the
interconnected system of lakes George, Edward, and Kazinga
Channel.\emph{Clarias gariepinus} was found in 42 water bodies followed
by\emph{Enteromius kerstenii} , \emph{O. niloticus} (33),
and\emph{Protopterus aethiopicus} (32). \emph{Astatoreochromis
alluaudi}was recorded in 30 water bodies, \emph{H. nubilus} (28),
\emph{H. lividus} (16), \emph{H. argenteus} (14), \emph{H. phytophagus}
(13),\emph{H. obesus} (12), and \emph{H. parvidens} in
(11).\emph{Oreochromis esculentus} was observed in 19 water bodies,
\emph{O. variabilis} (15), and \emph{L. victorianus} (8). Based on IUCN
categories, no vulnerable non-haplochromines was found in Lake Victoria;
however, 8 of the 25 vulnerable haplochromine cichlids were only
recorded in the lake (Appendix S3). \emph{Labeobarbus ruwenzorii}
and\emph{L. alluaudi} were only found to Lake Edward and rivers Ivi,
Rutushuru, Rwimi, Sebwe, Rwenzori, and Mubuku). \emph{Nothobranchius
taeniopygus} and \emph{Synodontis macrops} were only found in Aswa
River. \emph{H. melanopterus} was recorded in Lake Kasodo
whereas\emph{H. commutabilis} and \emph{H. ampullarostratus} were only
found in Lake Kachiira (Appendix S3). Of the endangered fish
species,\emph{Astatotilapia desfontainii} was only recorded in lakes
Bisina, Victoria, and Victoria Nile; \emph{H. simpsoni} was only found
in lakes Kyoga, Nabugabo, and Kayanja, while \emph{Lates macrophthalmus}
was only recorded in Lake Albert. \emph{H. beadlei} , a critically
endangered species, was found in lakes Nabugabo and Victoria, and
\emph{H. granti}in Lake Victoria and River Kagera.

Among the 3 waterbody categories, large lakes recorded the highest
number of non-shared species (110), followed by rivers (19), and the
small lakes (6) (Fig 4). The species richness in waterbody categories
differed significantly (Kruskal Wallis: \selectlanguage{greek}χ\selectlanguage{english}\textsuperscript{2}= 18.6, df
=2, p=0.001, \selectlanguage{greek}η\selectlanguage{english}\textsuperscript{2} = 0.2). The nMDS had a stress level of
0.14, with large and small lakes closely clustered except rivers
(Appendix S4). Haplochromine cichlids were closely clustered,
except\emph{H. latifasciatus, H. victoriae, A. alluaudi, H. nubilus, and
H. schubotzi} , while non-haplochromine species were widely distributed
(Appendix S5). Analysis of similarity (ANOSIM) among waterbody
categories showed significant differences (R=0.24, p\textless{}0.001).
Similarity percentage (simper) analysis showed that large lakes differed
from small lakes (93.5\%) and rivers (97.5\%), while small lakes
differed from rivers by 96.2\%. \emph{Lates niloticus} and \emph{O.
niloticus} contributed the highest average percentage variation between
large lakes and rivers. Similarly, \emph{L. niloticus} had the highest
contribution between large and small lakes (Appendix S6). In
contrast,\emph{A. alluaudi} and \emph{C. gariepinus} had the highest
contribution to the variation between rivers and small lakes.

\subsection*{Conservation Priority Index
(CPIw)}

{\label{conservation-priority-index-cpiw}}

CPIw was computed for 38 lakes, where 7 were large lakes and 31 small
lakes. Small lakes had a mean CPIw of 2.4 (SD 4.6) compared with 0.05
(SD 0.04) for large lakes. A significant difference between the mean
CPIw value for large and small lakes was observed (Welch 2-Sample
t-test: t = -2.8, df = 30, p-value = 0.008, d=0.7). The surface area of
the lakes had a large effect on the CPIw values. Highest CPIw values
were recorded for lakes Manywa followed by Kayanja, Gigate, Agu,
Naragaga, Kawi, and Nabugabo (Fig 5 \& Appendix S7). Low values were
mostly recorded for large lakes and highest for small lakes.

\section*{Discussion}

{\label{discussion}}

\subsection*{Species composition, rareness, and
distribution}

{\label{species-composition-rareness-and-distribution}}

The total species count recorded was comparable with the FishBase
overall estimate of 278 described fish species in Uganda (Froese \&
Pauly 2019). Also, the species accumulation curve approached the
asymptote, suggesting that most of the species were considered in the
analysis (Gotelli \& Colwell 2001). Large lakes had higher species
richness and more rare species compared with rivers and small lakes.
Differences in species richness and rarity among water bodies may be
attributed to geomorphological, abiotic, and biotic factors (Brown et
al. 2007). Also, isolated inland water bodies favor rapid allopatric
speciation and adaptive radiation (Basiita et al. 2018), which exposes
the species to different evolutionary pressures. For example, along the
Victoria Nile, Murchison Falls hinders fish migration to Lake Kyoga from
Albert (Basiita et al. 2018). The falls along River Semiliki prevents
fish passage to Lake Albert from Edward (Acere \& Mwene-Beyanga 1990).
Similarly, between Lake Victoria and Kyoga have been separated by the
Owen and Bujagali falls (Basiita et al. 2018), and a sandbar between
Lake Nabugabo and Victoria (Stager et al. 2005). These biogeographical
barriers may have led to allopatric speciation; for instance, Lake
Nabugabo which was once connected to Lake Victoria had five endemic
species (Ogutu-Ohwayo 1993; van Alphen et al. 2004). At the species
level, \emph{L. niloticus} from lakes Kyoga and Victoria were
genetically different (Basiita et al., 2018) possibly due to manmade
barriers between the two lakes, which has impeded gene flow (Basiita et
al. 2018).

High species richness and rarity are expected in habitats of long
geological age, geographically isolated, and where species are prone to
speciation (Strayer 2013). Also, according to the biogeographical
principle, large areas have more species (Rosenzweig 1995). In this
study, the high species richness and rareness observed in Lake Victoria
is not surprising, given its surface area, explosive speciation and
adaptive radiation of haplochromine cichlids because of hybridization,
allopatric, microallopatric and sympatric mechanisms (van Alphen et al.
2004; Meier et al. 2017). A similar trend of high species richness with
increased lake area was observed (Amarasinghe \& Welcomme 2002). In
large water bodies, species can shift to favorable habitats, for
example, \emph{Squalius lucumonis} and \emph{Telestes muticellus} that
migrated upstream in the Mediterranean Rivers due to climate warming
(Carosi et al. 2019). The level of rarity of recently described species
could not be determined in the available data. For example, the
distributions of \emph{H. akika} in Lake George (Lippitsch 2003)
and\emph{H. katonga} in River Katonga (Schraml \& Tichy 2010) are not
well understood except where the specimen was obtained. Also,
haplochromine cichlids are mostly lumped as haplochromines (Marshall
2018), which may have accounted for the absence of certain species in
particular water bodies.

For rivers, habitat degradations and manmade obstructions mostly damming
have affected fish species migration, feeding, and recruitment patterns
(FAO 2001). For instance, only 70 of the 177 large rivers in the world
are free from damning (WWF, 2020). In Uganda, along the Upper Victoria
Nile, three dams were constructed. These obstructions affected species
richness and gene flow along the river (Basiita et al., 2018). For
example, the stocks of \emph{H. simotes} and \emph{L. victorianus} have
been affected by dams along the Upper Victoria Nile (Sayer et al. 2018).
Similar effects of damming have been reported along Yangtze and Mekong
(Dugan et al. 2010; Yi et al. 2010).

\subsection*{Conservation Priority Index (CPIw) for inland water
bodies}

{\label{conservation-priority-index-cpiw-for-inland-water-bodies}}

Biological metrics including indices, indicators, or targets have been
used by managers and decision-makers to prioritize areas for
conservation (Tognelli et al. 2019; Linke et al. 2011). These metrics
may include prioritizing areas with threatened species (Kirkpatrick
1983), threatened species affected by climate change (Tognelli et al.
2019), or fixed percentages of an area (Linke et al. 2011). However,
some indices such as species richness do not incorporate the component
of complementarity of the areas in question, thus, highly-ranking areas
with the same species (Kirkpatrick, 1983). Shannon Weaver diversity
index does not distinguish habitats with the same species evenness and
richness (Omayio \& Mzungu, 2019). Most priority indices are computed
for terrestrial ecosystems or specific for a particular region, thus,
cannot be extrapolated to other systems (Brum et al. 2017). For example,
the Forest Conservation Priority index (FCPI) (de Mello et al. (2016)
used the area and shape of the forest without considering the species
conservation status. The Cave Conservation Priority Index (CCPi) (Souza
Silva et al. 2014) did not consider the species or their conservation
status but species richness, distribution, and impact weights. In the
species conservation importance index, the species conservation status
and rarity were considered (Halmy \& Salem, 2015), but the index applied
to terrestrial plants but not an aquatic ecosystem. Thus, these priority
indices cannot be used to rank water bodies for site-based priority
conservation.

In most instances, biodiversity measures such as species richness are
usually higher for large water bodies, which would mean they are
prioritized for conservation. Indeed, this study also showed higher
species richness for large lakes compared to small ones. However, the
novel conservation priority index (CPIw) was significantly higher for
small lakes compared with large lakes. This observation is consistent
with expectation, given the low ecological substitutability for the
species and higher levels of exposure to human-induced threats in small
water bodies compared to large systems. For a system such as Lake
Victoria, with a vast habitat heterogeneity, fish species can easily
seek refugia in other habitats (Seehausen et al. 1997; Chapman et al.
2003), which may not be possible in a small water body. The index showed
that, for example, Lake Gigate with a surface area of 1.7
km\textsuperscript{2}, but with 4 critically engendered haplochromine
cichlids (\emph{H. latifasciatus, H. obesus, H. parvidens,} and \emph{H.
argenteus} ) could be prioritized for conservation ahead of a large
system with higher species richness. The likelihood of the species
getting extinct in Lake Gigate is eminent if a similar magnitude of
stress is applied to both lakes. However, the index does not imply that
other water bodies with low CPI should not be monitored, but it may
allow a conservation manager or decision makers to rank water bodies for
urgent intervention, especially if the resources are limiting. Because
conservation interventions should address social needs (Linke et al.,
2011), large waterbodies, which are usually productive would be
difficult to fully conserve. The index should, therefore, be adopted as
a rapid metric measure to rank water bodies to enable prioritizing them
for conservation. Further, if the size and species in habitats in the
waterbody are known, the index can be downscaled to a habitat level.
However, index could not be applied on rivers because most of them are
dammed or obstructed, creating distinctive habitats along the river.

\subsection*{Supporting Information}

{\label{supporting-information}}

The map large lakes in Uganda (Appendix S1), Waterbody species richness
and IUCN status (Appendix S2), Rare species (Appendix S3), Ordination
plot for waterbodies (Appendix S4). Ordination plot for species
(Appendix S5), Simper analysis plot (Appendix S6), Waterbody
Conservation priority index values (Appendix S7)

\textbf{Literature cited}

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Table 1. Kruskal Wallis analysis of variances for IUCN categories and
species richness followed by Dunn's multiple comparison test between
waterbody categories\selectlanguage{english}
\begin{longtable}[]{@{}llllllll@{}}
\toprule
& & & & & Dunn's test multiple comparisons \emph{z (p-value)} & Dunn's
test multiple comparisons \emph{z (p-value)} & Dunn's test multiple
comparisons \emph{z (p-value)}\tabularnewline
\midrule
\endhead
Variable & \selectlanguage{greek}χ\selectlanguage{english}\textsuperscript{2} & df & \emph{p}-value & Effect size &
Lakes & Rivers & Small lakes\tabularnewline
Critically endangered & 24.6 & 2 & \textless{}0.001 & 0.27 & Large & 4.5
(\textless{}0.001) & 2.7 (0.009)\tabularnewline
& & & & & Small & 3.2 (0.002) &\tabularnewline
Endangered & 17.1 & 2 & \textless{}0.001 & 0.19 & Large & 4.1
(\textless{}0.001) & 3.5 (0.001)\tabularnewline
& & & & & Small & 1.1 (0.38) &\tabularnewline
Vulnerable & 24.4 & 2 & \textless{}0.001 & 0.26 & Large & 4.8
(\textless{}0.001) & 3.3 (0.002)\tabularnewline
& & & & & Small & 2.6 (0.013) &\tabularnewline
Near threatened & 13.5 & 2 & 0.001 & 0.16 & Large & 3.3 (0.001) & 3.6
(0.0004)\tabularnewline
& & & & & Small & 0.6 (0.811) &\tabularnewline
Least concern & 16.4 & 2 & 0.0002 & 0.18 & Large & 3.9 (0.001) & 3.7
(0.0003)\tabularnewline
& & & & & Small & 0.4 (0.1) &\tabularnewline
Data deficient & 19.8 & 2 & \textless{}0.001 & 0.22 & Large & 4.4
(\textless{}0.001) & 3.9 (\textless{}0.001)\tabularnewline
& & & & & Small & 0.9 (0.532) &\tabularnewline
Not evaluated & 25.8 & 2 & \textless{}0.001 & 0.28 & Large & 4.4
(\textless{}0.001) & 5.1v(\textless{}0.001)\tabularnewline
& & & & & Small & 1.2 (0.367) &\tabularnewline
Species richness & 18.6 & 2 & 0.001 & 0.20 & Large & 4.3
(\textless{}0.001) & 3.7 (0.0004)\tabularnewline
& & & & & Small & 1.2 (0.385) &\tabularnewline
\bottomrule
\end{longtable}



\textbf{Figure legends}

Figure 1. Species composition in water bodies according to IUCN status
and richness.

Figure 2. Species accumulation curve for fish species from 81 water
bodies in Uganda

Fig 3. Rarity of species in the different water bodies in Uganda

Figure 4. Fish species composition in the three water body categories

Figure 5. Conservation Priority Index (CPIw) for evaluated water bodies
in Uganda.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

Fig 1. Species composition in water bodies according to IUCN status and
richness.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

Fig 2. Species accumulation curve for fish species from 81 water bodies
in Uganda\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

Fig 3. Rarity of species in the different water bodies in Uganda\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image4/image4}

\end{center}
\end{figure}

Fig 4. Fish species composition in the three water body categories\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image5/image5}

\end{center}
\end{figure}

Fig 5. Conservation Priority Index (CPIw) for evaluated inland water
bodies in Uganda.

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

