Materials and methods
Study area
Tigray is one of the nine autonomous National Regional States of the
Federal Democratic Republic of Ethiopia (FDRE, Fig. 1A). It is divided
into seven administrative zones namely: Western zone, North-western zone
(NW), Central zone, Eastern zone, South-eastern zone (SE), Southern zone
and Mekelle special zone which are further divided into more 47
administrative districts (Fig. 1B). The region has several
agro-ecological zones that fall under three major biomes namely:
Sudan-Guinea Savanna biomes; Afrotropical Highlands biomes and
Somali-Masai biomes (Fishpool & Evans
2001; Haileselasie & Teferi 2012). It
has four traditional agro-ecological classifications: The “Qhola”
(below 1500 m a.s.l), “Weyna Degu’a” (1500-2300 m a.s.l), “Degu’a”
(2300-3200 m a.s.l), and “Wurch/Alelama” (above 3200 m a.s.l). The
highest mountain range in the region is the “Tsibet Sky Island” with
its highest peak reaching 3960 m a.s.l, located in the Southern Zone of
Tigray National Regional State and the lowest point is around 500 m
a.s.l in the Tekeze valley, western Tigray.
Here, 35 reservoirs of varying age were included in a study of avifauna
of the limnetic aquatic ecosystem of the region (see Appendix S1 in
supporting information). These reservoirs were purposively selected
because of previous experience to each site
(Haileselasie & Teferi 2012;
Teferi et al. 2013;
Haileselasie et al. 2018). The reservoirs
have different age from the youngest Mihtsab Azmati reservoir (MAZ; 5
years) to oldest Bokoro reservoir (BOK; 44 years). They also vary in
elevation (range: 1512– 2747), water surface (range: 1.78– 45.41 ha)
and water depth (range: 1– 13.7 m). To evaluate the relationship
between limnological characteristics of reservoirs and patterns of bird
species richness and distribution, we selected nine variables: reservoir
area, water depth, elevation, pH, nutrient concentration (Total Nitrogen
& Total Phosphate), water turbidity, water temperature and electrical
conductivity that are known to influence ecological processes and
species abundance in freshwater wetlands and lakes
(Hoyer & Canfield 1994;
Seymour & Simmons 2008;
Rajpar & Zakaria 2010;
van der Valk 2012). Ecological variables
such as: presence and absence of fish, emergent vegetation, presence and
absence of forest edge and/or downstream wetland were recorded and coded
into an appropriate format for analysis. Limnological characteristics of
each reservoir: reservoir’s morphometry (altitude, area and maximum
depth) and water chemistry variables: water temperature (°C), pH,
electrical conductivity (EC, µS/cm) were measured in the field using
portable pH/EC/ multi-meters. Whereas integrated water samples were
collected and brought to Aquatic Ecology Laboratory (Mekelle University,
Tigray) for determination of Total Phosphorus (TP, μg/l) and Total
Nitrogen (TN, μg/l) in the laboratory using standard methods stated in
Nelson and Sommers (1975).
Bird surveys
Data for this study were obtained by counting birds that were observed
during a survey of 35 reservoirs (see Fig. 1). Birds observed utilizing
limnetic ecosystems were recorded by observers who motored around each
reservoir in a small boat and/or by walking along the perimeters of the
reservoir depending on the size of the water body. Birds were identified
to a species level based on bird’s field guide for East Africa; birds of
sub-Sahara and country specific checklists
(Urban & Brown 1971;
Sinclair & Ryan 2003;
Ash & Atkins 2009). Species richness in
this article’s context is defined as the total number of bird species
observed throughout the entire sampling period in the region (as gamma
diversity) and number of bird species recorded in each reservoir (as
alpha diversity). Here no attempt has been made to calculate annual bird
abundance (birds/area) for each reservoir. English names and Taxonomy of
birds reported here, follows the International Ornithological Congress
(IOC) standard format (Gill & Donsker
2020).
Data analysis
Differences in limnological variables between reservoirs are visualized
by Principal Component Analyses on residuals of full limnological data
set. Principal components were extracted from covariance matrices using
the function rda in vegan package of the R software
(R Development Core 2014). The
Eigenvalues and % of variance for each axis were used to retain number
of significant PC axis for further analysis. And the Euclidean distance
after standardizing the variables, followed by Ward clustering is used
to display plot of the first two PC axis of limnological components.
To partition gamma diversity into its alpha (α) and beta (β) components;
gamma (γ) diversity of birds as species richness with q=0, is equated as
multiplicative (i.e. α*β= γ) relationship. As a result beta (β)
diversity is calculated as gamma diversity divided by mean alpha
diversity, with all samples being equally weighted as applied in the
R-software package vegetarian (Jurasinski,
Retzer & Beierkuhnlein 2009).
To explore relationships among environmental variables (ENV) and
geographic location (SPACE) of the reservoirs and bird species richness,
redundancy analysis ordination (RDA) is performed. Species richness data
was submitted to a multiple regression analysis at limnological
variables (ENV), biological variables (BV) and age of the reservoirs in
order to investigate the most important explanatory factors influencing
avian species richness and their distribution. The Monte Carlo
Permutation test of 999 permutations is used to test statistical
significance of the relationship. Pearson’s correlation coefficients is
used to examine correlations between the variables and to reduce the
number of explanatory factors.
Besides, the scores of species (alpha diversity) and environmental
variables resulting from the ordination is used to build a bi-plot that
illustrates the relationships between environment and bird species
richness. To describe the environmental preferences of particular
species, Redundancy Analysis ordination (RDA) in R software
(R Development Core 2014) was applied.
The function partitions the variation-varpart in vegan R package
(Oksanen et al. 2013) using
adjusted R-squared (R2adj) in
redundancy analysis ordination (RDA) is used to disentangle the effect
of these variables: in species - environment - space - age variation
partitioning by partial regression.