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\begin{document}

\title{Distribution and predictive niche modeling of five endangered raptors
species in Kenya}



\author[1]{Peggy Ngila}%
\author[2]{David Chiawo}%
\author[1]{Margaret Owuor}%
\author[3]{Oliver Wasonga}%
\author[3]{Jane Mugo}%
\affil[1]{South Eastern Kenya University}%
\affil[2]{Center for Biodiversity Information Development}%
\affil[3]{University of Nairobi}%


\vspace{-1em}



  
  \date{May 24, 2024}


\begingroup
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\selectlanguage{english}
\begin{abstract}
Raptors are apex predators threatened globally by electrocution,
collisions, and habitat fragmentation. Most species of raptors are
understudied and largely unexplored. Top predators like raptors depend
on the sustainability of the ecosystems in which they live and migrate.
Knowing how endangered raptors are geographically dispersed, as well as
the factors that may influence how they use their habitat, is critical
for their protection. This research focuses on Kenya, where there are
gaps in knowledge on appropriate habitats and raptor dispersal patterns.
With several species of raptors endangered, it is crucial to determine
their distribution patterns for management and conservation. To evaluate
the size of the realized niches for five Kenyan raptor species at the
risk of extinction, we applied species distribution models (SDMs)
through an ensembling approach using occurrence data from the Global
Biodiversity Information Facility (GBIF) and environmental covariates.
These species were: Martial eagle, Secretarybird, Bateleur, Steppe
Eagle, and Southern ground hornbill. The five raptors' distribution
within and outside protected areas and the role of key environmental
predictors in predicting their distribution was estimated. Our findings
indicate raptor distribution in several areas in Kenya that is
predominantly in the south-western region, extending into the country's
central region. Martial eagle had the largest niche range amounting to
ca.49,169 km2 while the Southern ground hornbill had the smallest niche
range amounting to ca.4,145 km2. Secretarybird had the highest
distribution outside protected areas at 77.57\% followed by the Martial
eagle at 76.89\%. Significant predictors of raptor species distribution
in Kenya were; precipitation during the warmest quarter, precipitation
during the driest month, and precipitation during the coldest quarter.
Key areas for raptor conservation listed here could serve as foundation
for a number of additional Important Bird Areas (IBAs) in Kenya,
according to the A1 Global IBA Criterion for species that are globally
threatened.%
\end{abstract}%



\sloppy


\textbf{Distribution and predictive niche modeling of five endangered
raptors species in Kenya}

Ngila, Peggy\textsuperscript{1,2,3,*}, Chiawo David\textsuperscript{2},
Owuor, Margaret Awuor\textsuperscript{3,4}, Wasonga, Vivian
Oliver\textsuperscript{1}, Mugo Jane\textsuperscript{5}

\textsuperscript{1}Department of Land Resource Management and
Agricultural Technology, University of Nairobi, Box, 29053, 00625
Nairobi, Kenya

\textsuperscript{2} Center for Biodiversity Information Development
(BID-C), Strathmore University, Box 59857, 00200 Nairobi, Kenya

\textsuperscript{3} School of Environment Water and Natural Resources,
South Eastern Kenya University, Box 172, 90200 Kitui, Kenya

\textsuperscript{4} Wyss Academy for Nature, University of Bern,
Kochergasse 4, 3011 Bern, Switzerland

\textsuperscript{5}Department of Earth and Climate Science, University
of Nairobi, Box 30197, Nairobi, Kenya

* Corresponding author ngila.peggy@gmail.com

\textbf{Abstract}

Raptors are apex predators threatened globally by electrocution,
collisions, and habitat fragmentation. Most species of raptors are
understudied and largely unexplored. Top predators like raptors depend
on the sustainability of the ecosystems in which they live and migrate.
Knowing how endangered raptors are geographically dispersed, as well as
the factors that may influence how they use their habitat, is critical
for their protection. This research focuses on Kenya, where there are
gaps in knowledge on appropriate habitats and raptor dispersal patterns.
With several species of raptors endangered, it is crucial to determine
their distribution patterns for management and conservation. To
evaluate~the size of the realized niches for five Kenyan raptor species
at the~risk of extinction, we applied species distribution models (SDMs)
through an ensembling approach using occurrence data from the Global
Biodiversity Information Facility (GBIF) and environmental covariates.
These species were: Martial eagle, Secretarybird, Bateleur, Steppe
Eagle, and Southern ground hornbill. The five raptors' distribution
within and outside protected areas and the role of key environmental
predictors in predicting their distribution was estimated. Our findings
indicate raptor distribution in several areas in Kenya that is
predominantly in the south-western region, extending into the country's
central region. Martial eagle had the largest niche range amounting to
ca.49,169 km\textsuperscript{2} while the Southern ground hornbill had
the smallest niche range amounting to ca.4,145 km\textsuperscript{2}.
Secretarybird had the highest distribution outside protected areas at
77.57\% followed by the Martial eagle at 76.89\%. Significant predictors
of raptor species distribution in Kenya were; precipitation during the
warmest quarter, precipitation during the driest month, and
precipitation during the coldest quarter. Key~areas for raptor
conservation listed here could serve as foundation for a number of
additional Important Bird Areas (IBAs) in Kenya, according to the A1
Global IBA Criterion for species that are globally threatened.

\textbf{Keywords:}

Raptors, Species Distribution Modeling, Protected Areas, Raptors-habitat
suitability, Raptor conservation



\textbf{INTRODUCTION}

Raptors such as eagles, hawks, falcons, harriers, buzzards, and vultures
are predatory birds found throughout the globe except the Antarctica
region . Globally, 37\% of raptor species are thought to be on the verge
of extinction. The countries with the most threatened species are
Indonesia, Tanzania, Sudan, and Kenya . 102 different species of raptors
can be found in Kenya, with 14\% of them threatened globally . Raptors
have been chosen with respect to being a potentially useful substitute
species on behalf of all biodiversity ; because they are found at the
apex of environmental food chains, they serve as custodians of climate
change by warning of potential human-induced~effects on biodiversity .
Along with their highly dynamic habitats, the selection of raptors as an
umbrella species for species diversity is because of their variety of
prey across the food chain . With the exception of vultures, raptors are
generally well-liked by the overall population and extensively studied
by scientists due to their appeal and recognition functioning as
flagship species for harnessing resources to safeguard and conserve
biodiversity in totality

Historical trends and recent assessments of raptor populations in Kenya
show a plummet of more than 50\% over 40 years . Fourteen of Kenya's 102
raptor species are listed as Critically Endangered, Endangered, or
Vulnerable on the IUCN Red List . These species are highlighted in table
1.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

\textbf{Fig. 1} . Examples of raptors species in Kenya. There are
declines in some of these species that have not caught the attention of
IUCN as a result of worrying national declines.
1\textsuperscript{st}image top row, Wahlberg's eagle (Hieraaetus
wahlbergi), image credit by copper (licensed under
http://creativecommons.org/licenses/by-nc/4.0/). 2\textsuperscript{nd}
image top row Lappet faced vulture (Torgos tracheliotos) by tjeerd
(licensed under http://creativecommons.org/licenses/by-nc/4.0/).
3\textsuperscript{rd} image top row Augur buzzard (Buteo augur) by David
Bygott (licensed under http://creativecommons.org/licenses/by-nc/4.0/).
4\textsuperscript{th} image top row, Montagu's harrier (Circus
pygargus), by Nik Borrow (licensed under
http://creativecommons.org/licenses/by-nc/4.0/). 1\textsuperscript{st}
image bottom row, Long-crested eagle (Lophaetus occipitalis) by Bram ter
Keurs (licensed under http://creativecommons.org/licenses/by/4.0/).
2\textsuperscript{nd} image bottom row Bateleur (Terathopius ecaudatus),
image credit by thelandlover (licensed under
http://creativecommons.org/licenses/by-nc/4.0/) .
3\textsuperscript{rd}image bottom row; Hooded vulture (Necrosyrtes
monachus), image credit by Nik Borrow (licensed under
http://creativecommons.org/licenses/by-nc/4.0/)

Table 1: List of raptor species of raptors in Kenya and their IUCN
status. Eight of the species have recorded a worrying decline which have
not been captured by global estimates.\selectlanguage{english}
\begin{longtable}[]{@{}lll@{}}
\toprule
Common name & Scientific name & IUCN status\tabularnewline
\midrule
\endhead
Steppe eagle & \emph{Aquila nipalensis} & Endangered\tabularnewline
Long crested eagle & \emph{Lophaetus occipitalis} & Least
concern\tabularnewline
Augur buzzard & \emph{Buteo augur} & Least concern\tabularnewline
Hooded vulture & \emph{Necrosyrtes monachus} & Critically
endangered\tabularnewline
White backed vulture & \emph{Gyps africanus} & Critically
endangered\tabularnewline
Common kestrel & \emph{Falco tinnunculus} & Least concern\tabularnewline
Lappet faced vulture & \emph{Torgos tracheliotos} &
Endangered\tabularnewline
Montagu's harrier & \emph{Circus pygargus} & Least conern\tabularnewline
Bateleur & \emph{Terathopius ecaudatus} & Endangered\tabularnewline
Black Kite & \emph{Milvus migrans} & Least concern\tabularnewline
Wahlberg's eagle & \emph{Hieraaetus wahlbergi} & Least
concern\tabularnewline
Black-chested snake eagle & \emph{Circaetus pectoralis} & Least
concern\tabularnewline
Martial eagle & \emph{Polemaetus bellicosus} & Endangered\tabularnewline
Brown snake eagle & \emph{Circaetus cinereus} & Least
concern\tabularnewline
\bottomrule
\end{longtable}

Due to their extremely rapid rates of decline, six of these species are
already regarded as globally threatened. Global estimates have not
considered the eight remaining species' worrying national plunges, such
as those in the black kite . Despite all these, a disproportionate
amount of studies is expended on raptors' studies in Kenya . There are
knowledge gaps regarding the interactions between raptors' geographic
distribution and their ecological needs over time and space, which, in
order to direct conservation and management efforts, must be filled.

Raptors' movements and feeding preferences are known to be influenced by
environmental variables, as well as the plausible links between their
distribution and environmental predictors such as precipitation,
temperature, human influence, and vegetation, which have been
demonstrated in numerous studies (e.g. . Understanding the relationships
between raptor distribution and environmental variables may aid in
determining their ideal niche. Such data would be crucial in creating
spatially explicit management and conservation measures when combined
with anthropogenic variables . To advance their spatio-temporal
management on a landscape scale, more data are needed on significant
environmental variables, niche distribution, and anthropogenic
predictors.

Accurate species occurrence determination is necessary for proper
biodiversity conservation policy decisions, including nature reserve
preselection, biologically-based invasion monitoring and the
identification of vital habitats for endangered species . Aforesaid data
is frequently arbitrarily gathered and made accessible as museum records
or through websites that collect biodiversity data, like the Global
Biodiversity Information Facility (GBIF) . Seeking out new presence
locations for endangered or rare species is critical because a better
understanding of their distribution may aid in management and
conservation efforts . In order to inform conservation management,
species distribution models (SDMs) are employed to deduce the ecological
requirements of species as well as predict their geographic
distribution. In a variety of applications, such as regional
biodiversity assessment in rangelands, conservation planning and
wildlife management, these models have grown in significance

The biggest dangers to raptors are habitat loss brought on by logging
and agricultural growth, persecution (including shooting, poisoning, and
trapping), electrocution and collisions with energy infrastructure,
contamination (primarily from lead, pesticides, or veterinary drugs),
and illegal harvesting for actions motivated by religious convictions .
This study estimates the distribution and ecological requirements of
five raptor species in Kenya in order to better understand their niches
and threats. It also examines the contribution of continuous remotely
sensed predictor variables to their distribution. The objectives of this
research are to; 1) identify the ecological niche, geographic
distribution, and impact of protected areas in raptor conservation in
Kenya.; 2) determine the relative importance of environmental predictors
to raptor distribution ; 3) offer recommendations for raptor habitat
management and protection in Kenya. The results of this study will
improve the understanding of raptors' niche and the critical
environmental/anthropogenic variables influencing its distribution to
inform sustainable management efforts of its habitats.

\textbf{DATA AND METHODS}

\textbf{Study area and data acquisition}

Approximately 580,367 km\textsuperscript{2} in size, Kenya is situated
between latitudes 5 N and 5 S and longitudes 34 and 42. Kenya's
predominant bimodal rainfall and temperature patterns are determined by
the~Inter-Tropical Convergence Zone (ITCZ). Kenya's topography is
diverse, with elevations ranging from 0 to 5197 meters above sea level.
The dry land mass is typically represented by six agro-ecological zones:
agroalpine (0.1\%), high potential (9.3\%), medium potential (9.3\%),
semi-arid (8.5\%), and dry (52.9\%). Agro-pastoralists and pastoralists
predominately live in the semi-arid to very arid zones~which make up
80\% of the country . Kenya has 28 national reserves totaling 18,042
km\textsuperscript{2} (11.7\%), 22 national parks totaling 29,357
km\textsuperscript{2} (5.16\%), and 160 conservancies totaling 36,300
km\textsuperscript{2} (11.0\%). Kenya has 68 important biodiversity
areas (IBAs), with 55 of them threatened .

All raptor species' occurrence data for the years 2013 to 2020 was
acquired from the Global Biodiversity Information Facility's online
platform (https://www.gbif.org/, https://doi.org/10.15468/dl.hzyp6v,
GBIF, 7th September, 2021). A total of 11529 records were obtained.
These data were used to project the overall occurrence distribution of
raptor occurrence in Kenya. From these data, we then selected raptor
species that were endangered and/or threatened based on their IUCN
status and selected the following five raptors: Steppe Eagle
(\emph{Aquila nipalensis} ) (EN), Secretary bird (\emph{Sagittarius
serpentarius} ) (EN), Martial Eagle (\emph{Polemaetus bellicosus} )
(EN), Bateleur (\emph{Terathopius ecaudatus} ) (EN), and Southern ground
hornbill (\emph{Bucorvus leadbeateri} ) (VU). As previously stated , in
order to fully cover the raptor's entire climatic and environmental
range, records for the entire nation of Kenya were downloaded .
OpenRefine software Version 2.1 was used for cleaning, which included
removing duplicate records and records without georeferences. In order
to eliminate data points with spatial correlation, and ensure
independence, all records were checked in R for spatial autocorrelation
using the K nearest neighbor analyses . We retained the following
occurrence points following cleaning: Steppe Eagle, n=169; Martial Eagle
n=267; Secretary bird, n=191; Bateleur, n=676; and Southern ground
hornbill, n=25, for a total of 1328 occurrence records. We only modelled
for species with more than 20 records of occurrence to warrant~the
precision of the estimates of species niches. These records were then
used to produce SDMs.

\textbf{Ecogeographical variables}

A set of criteria was developed to predict raptor habitat suitability
which included choosing variables that were potentially useful for
raptors' distribution. The Worldclim database version 2.1 (Fick and
Hijmans, 2017; https://www.worldclim.com/current) provided 19
bioclimatic variables. Because of their direct effects on species
distribution, climate variables are commonly employed~in habitat
modeling . The Shuttle Radar Topographic Mission's digital elevation
model (SRTM DEM, opendata.rcmrd.org/datasets/) was used to extract
digital elevation data for Kenya, slope and aspect were derived from the
DEM, and the topographic roughness index calculated as the surface area
ratio, which is also derived from the DEM . Data for the Normalized
Difference Vegetation Index (NDVI) (2013-2020) were obtained from NOAA,
as were observations from the Advanced Very High Resolution Radiometer
(AVHRR) and Visible Infrared Imaging Radiometer Suite (VIIRS) satellites
(https://www.star.nesdis.noaa.gov/smcd/emb/vci/VH/vh\_browseByCountry.php).
The Human Influence Index (HII), which best represents anthropogenic
impacts spanning the years 1995 to 2004, was derived from Last of the
Wild v2
(https://sedac.ciesin.columbia.edu/data/collection/wildareas-v2/).
ArcGIS was used to rasterize all predictions at a spatial extent n of
about 1 km (Version 10.5). We used the `\emph{usdm} ' package in R to
carry out a variance inflation factor stepwise procedure to decrease
multicollinearity in predictor variables . Variables with variance
inflation factors greater than 10 were eliminated. As a result, we only
kept the 12 best-fitting covariates based on the raptors' ecological
requirements from an initial set of 25 variables.

\textbf{Model validation and model setting}

Generalized Linear Models (GLM), Random Forest (RF), Generalized
Additive Models (GAM), Artificial Neural Networks (ANN), Multivariate
Adaptive Regression Splines (MARS), Generalized Boosted Models (GBM)~,
and Maximum Entropy Models (MaxEnt), are the seven modeling techniques
employed in this study~. SDMs were generated using the `\emph{biomod2} '
package's ensemble forecasting method found in R under the following
parameters; MARS models had a highest interaction level of 2, whilst RF
models were fitted by growing 750 trees with half the available
predictors sampled to split at each node. The default settings and the
highest iteration count of 1000 were applied to MaxEnt models. While
GAMs were computed using a logistic link function, GLMs were readjusted
using a binomial link function. On the other hand,~GBMs were generated
by performing 5000 three-fold cross-validation procedures to determine
the optimal number of trees to keep and a maximum depth of variable
interactions of 7. The default specifications were used to fit ANN
models. This method has previously been used in other research (e.g. .
We added a background set of 10,000 randomly chosen background points to
the study area because our dataset only contained presence data

. As in previous research with species distribution modeling, the
occurrence dataset was randomly divided into a 30\% sample for
evaluating the performance of the model ~and a 70\% sample for model
calibration . We performed 175 SDMs in total (7 algorithms X 5 splitting
replicates for model evaluation X 1 repetition X 5 species).

\textbf{Model Evaluation}

The Area under the receiver operating characteristic curve (AUC) was
used to assess the models' predictive performance of the models ( Hanley
and McNeil, 1982) and the True Skill Statistic (TSS; . The sensitivity,
or the percentage of known presences predicted as presences, is plotted
on the ROC curve against specificity, or the percentage of
pseudo-absences predicted as absences. These validation techniques are
well-known and perform very well . Models with an AUC of less than 0.7
were disqualified. It has been demonstrated that weighing the individual
model projections according to their AUC scores is a particularly
trustworthy technique . Additionally, the ensemble model's relative
importance of the variables was calculated from the `\emph{biomod2} '
package devoted functionality . The ensemble models' projections from
the two-stage sampling mentioned above were averaged to produce the
final potential distribution. To evaluate the spatiotemporal habitat
dynamics, the ensemble models were projected at roughly 1 km resolution.

\textbf{Assessing the effectiveness of protected areas}

We overlaid the protected areas with the current distributions of
raptors from the above mentioned~ensemble model obtained,~to assess the
efficacy of protected areas under the prevailing climatic conditions. We
clipped the distribution of the five raptors with the Protected areas
network from
(https://geoportal.icpac.net/layers/geonode\%3Aken\_protected\_areas) in
order to identify the distribution~occurring within and outside of
protected areas.

\textbf{RESULTS}

\textbf{Spatial distribution}

Raptors can be found throughout Kenya, but they are most prevalent in
the south-western and central regions. The northern and western regions
of the nation only have a small population. The Martial eagle had the
largest realized niche range at approximately 49,169
km\textsuperscript{2}, followed by the Bateleur at approximately 43,576
km\textsuperscript{2}, the Steppe eagle at approximately 29,018
km\textsuperscript{2}, and the Secretary bird at approximately 28,826
km\textsuperscript{2}. The Southern ground hornbill had the smallest
realized niche range, measuring approximately 4,145
km\textsuperscript{2}.The Steppe Eagle's projected realized niche was in
South-western Kenya, extending to South-central, and some species were
found in Kenya's Southern region, giving it the widest projected
distribution. The Southern ground hornbill has the least~distribution,
with a projected niche primarily in Kenya's south-western region (Fig.
2).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image7/image7}

\end{center}
\end{figure}

Figure 2: Projected occurrence of all raptors species and five selected
species in Kenya. Habitat suitability indicated by 0 -- not suitable
(lack of niche); and 1 -- suitable (presence of niche). Niche
distribution maps ordered from the biggest to the smallest niche range
for each species.



\textbf{Raptors distribution with protected areas}

Martial Eagle, Steppe Eagle, Bateleur and Secretary birds have their
niche distributed outside protected areas of Central Kenya. Niche
distribution for species occurring in the Southern and South western
region of Kenya occur within the protected areas. Secretary bird and
Martial eagle had the highest niche range occurring outside protected
areas at ca. 23,320 km\textsuperscript{2} (77.57\%) and ca. 37,660
km\textsuperscript{2} (76.89\%) respectively. The species with most of
its distribution in protected areas was the Southern ground hornbill at
ca. 2, 074 km\textsuperscript{2} (50.35\%) distribution in protected
areas. Steppe eagle and Bateleur occurring outside protected areas was
at ca. 18, 339 km\textsuperscript{2} (63.46\%) and ca. 25, 436
km\textsuperscript{2} (58.55\%) (Fig. 3).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image8/image8}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image9/image9}

\end{center}
\end{figure}

Figure 3: Distribution of protected areas in Kenya and overlap of niches
of the five species of raptors.



\textbf{Relative Importance of Environmental Predictors}

The most crucial factors in predicting distribution in all species
across all model types were precipitation of the warmest quarter
(bio18), precipitation of the driest month (bio14), and precipitation of
the coldest quarter (bio19), with species-specific differences in
relative significance. For Secretary bird and Southern ground hornbill,
Precipitation of the driest month (bio14) and precipitation of the
coldest quarter (bio19) were the most significant predictors whereas
Bateleur, Martial Eagle, and Steppe Eagle were best predicted by;
precipitation of the warmest quarter (bio18)~and precipitation of the
driest month (bio14). The annual temperature range (bio7) and mean
temperature of the driest quarter (bio9)~were found to be the best
predictors of temperature variables that affect the distribution of
raptor species (Fig. 4).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image14/image14}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image15/image15}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image16/image16}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image17/image17}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image18/image18}

\end{center}
\end{figure}

\textbf{Response curve variables}

With the exception of the Steppe eagle, most species were positively
impacted by the precipitation of the warmest quarter's (bio18), with an
ideal mean range of 250mm-375mm. At precipitation levels below 25 mm,
the majority of raptor species' distributions begin to decrease during
the driest month~(bio 14). When precipitation falls below 200 mm, the
majority of species' distributions also start to decline during the
coldest quarter~(bio 19). The majority of species preferred temperatures
between 13 and 19 \selectlanguage{ngerman}°C (bio 7). The Secretary bird and the Southern ground
hornbill were unaffected by the high temperatures, which had an impact
on the distribution of the rest of the~species. The majority of species
demonstrated a preference for regions with high vegetation indices
between (0.2-0.3). A high human influence index had a detrimental effect
on the distribution of bateleur and steppe eagle (Fig. 5).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image19/image19}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image20/image20}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image21/image21}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image22/image22}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image23/image23}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image24/image24}

\end{center}
\end{figure}\selectlanguage{english}
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Figure 5: Response curves depicting the dynamic range of influence of
predictor variables on raptor species distribution. Predictor variables
are represented on the X-axis, and habitat suitability is represented on
the Y-axis.

\textbf{DISCUSSION}

\textbf{Spatial distribution of raptors and conservation planning.}

This study's main goal was to identify suitable habitats for five
endangered raptors while also taking protected areas into account for
their preservation. Priority areas, which are pivotal habitat for the
five species, were mostly found in Kenya's south, southwest, and central
regions. This might be a result of Kenya's diverse range of
environmental conditions. For instance, temperatures in the northern and
eastern parts of the country can reach 33\textsuperscript{o}C with
little to no rainfall (less than 100 mm each season, . In contrast,
South central and South western regions of the nation experience
slightly lower temperatures, with highs up to 25\selectlanguage{ngerman}°C and more
precipitation overall of more than 200 mm in every season . The western
and south central regions of Kenya, extending to the north central
region, can be recognized for a program of raptor conservation as a
result of the modeled results, particularly when comparing potential
distributions under current conditions and when overlaying with nature
reserves. The Central, Western, and South Western regions of Kenya had
the highest taxa, phylogenetic, and~rich diversity of terrestrial
animals, according to . However, preventing human encroachment on animal
habitats may be difficult because these areas are surrounded by buffer
zones that are densely populated and farmed.

All five raptor species studied here had at least some of their niche
outside protected areas; with Secretarybird having the highest
proportion outside protected areas at 77.57\% and the Southern ground
hornbill having the lowest distribution outside protected areas at
49.64\%. The Southern ground hornbill fared best, with almost half of
its core range protected (50.35\%), followed by the Bateleur at 41.45\%,
and all other species below 37\%, with the Secretarybird having the
lowest protection (22.43\%). The vast majority of priority habitats for
Kenya's five raptors are located outside of protected area networks,
which is consistent with recent research showing that raptors exist
outside of protected areas in Kenya and are in decline . , who modeled
Eastern and Central European birds that are endangered or threatened
came to similar conclusions. The author's research discovered vulnerable
bird focal points outside of protected areas in Eastern Central and
Europe. Birds like the Secretarybird, which have suitable niches in
Central Kenya, would quickly disappear if grasslands were transformed to
agricultural production as nesting season disturbance would increase,
like observed.

\textbf{Relative importance of environmental covariates}

The availability of suitable nesting sites and trophic nutrients
determines distribution and abundance patterns, notably for raptors .
Temperature can directly affect animal growth and reproduction, as well
as indirectly by affecting the food chain, which can have an impact on
animal survival. Extreme precipitation generally poses a threat to bird
survival . The description of raptor species declines in response to
periods of extremely low or high precipitation, as shown by the curves
of precipitation in the wettest quarter (bio18), precipitation in the
driest month (bio14), and precipitation in the coldest quarter (bio19),
supports the conclusions of our studies

The ensemble model's relative contributions from each environmental
covariate are consistent with our theoretical understanding of each
species' biology. The limiting factors of precipitation and temperature
have a significant impact on the distribution of all five species. The
fact that bio18 (Precipitation of the warmest quarter) has such a strong
impact on all species (Fig. 3) may indicate that these species prefer
habitats with the right amount of precipitation and temperature.
Compared to the rest of the other species, the Steppe Eagle and the
Secretarybird appreciated the inclusion of NDVI (Table 1). This might
lend credence to the idea that these two birds prefer environments with
some kind of woody vegetation. In Africa, Steppe eagles typically
inhabit savannas and grasslands, with occasional use of dry woodlands.
The Secretarybird migrates in response to rainfall and the increase in
prey. It favors open grasslands, savannas, and shrubland over forests
and dense shrubbery, which could obstruct its fleeting existence.

For all species, the effect of the human influence index was
surprisingly low. Our results are in line with those of , and show that
human influence did not have a significant impact on the distribution of
suitable habitat for the five species. Raptor interactions, such as
potential prey competition, have the potential to influence the
distribution of suitable habitats . Given that precipitation and
temperature were the most significant predictors for all five species,
the~ramifications of changing climate~is likely to have a significant
impact on how raptors are distributed and may cause their ranges to
contract .

The predicted spatial distributions of the five species of raptors
differed in size. These differences might be primarily brought on by
variations in the predicted distributions' environmental ranges. The
Martial eagle, for instance, had a much wider~coverage range than the
Southern ground hornbill. Species with wider geographic ranges are more
resilient to climate change . Due to the lack of protection in raptor
priority areas, conservation efforts should~be functional~outside of the
current conservation areas. It is likely that extensive conservation and
governmental measures will be needed to preserve raptors and the
ecosystem services they provide. Legislation may be passed, for
instance, to control the distribution and utilization of veterinary
drugs, which are reducing the number of vultures in Africa .
Additionally, thorough environmental impact assessments (EIAs)~should be
carried out before developing energy infrastructure in order to
recognize and eliminate risks to raptors especially vultures . Our
biodiversity will continue to face serious threats and challenges,
particularly in Kenya's key biodiversity areas (KBAs) as they~are put
under increasing pressure by an increase in infrastructure developments
like roads, power and wind energy, pipelines, land use changes, and
illegal activities like logging . Furthermore, it will be crucial to
work with stakeholders to recognize and address regional problems like
conflict between people and wildlife, which may be a factor in the
decline of raptors.

A more complete study of the spatial distributions of threats will be a
crucial next step in improving raptor conservation in Kenya. Raptors in
Sudan, for instance, have been shown to be electrocuted~by power lines
and similarly alarming levels of electrocution have been recorded in
Kenya . Flying birds like raptors and other species can be severely
harmed by high voltage transmission lines, even though increasing
renewable energy is generally a desirable plan of action for Kenya's
global environmental sustainability and development for a host of
reasons .

\textbf{Conclusion}

Using niche models, five raptor species in Kenya were analyzed for their
distribution in suitable habitat. Our results indicate that a) of the
five species of raptors, the Martial Eagle and Bateleur had the most
suitable habitat (or, in our case, a realized niche). In contrast, the
southern ground hornbill had a small area of suitable habitat. This
indicates the two species' excellent climate adaptation to a wide range
of environments in Kenya. Even though there was a predictor for
anthropogenic influence, it is obvious that this predictor had no
appreciable impact on the five species' suitable habitat. b) The
majority of raptors are found outside of protected areas, with a high
concentration in Kenya's southwestern and central regions. c)
Temperature and precipitation-related variables influenced the habitat
suitability of the five species the most. Our results have significant
ramifications for Kenya's management and conservation of raptors. To
begin with, raptor management, whether using a single species,
multispecies, or ecosystem-based approach, can be aided by identifying
raptors' spatial ranges and overlap with protected areas. Further, as
temperature and precipitation in Kenya are altered with climate change,
continued work to update spatial ranges will be necessary. Our research
on the niche distribution of various species outside of protected areas
shows that Kenya is a crucial habitat for raptor species, emphasizing
the need for ecosystem-based management both inside and outside of
protected areas. Second, understanding the key environmental factors
that link raptor distribution may aid in estimating how future climate
change will affect the occurrence of species. According to the A1 Global
IBA Criterion for globally threatened species, the five priority areas
for raptor conservation identified here could be the basis for several
additional IBAs.

\textbf{Authors' Contribution}

The concepts and methodology were developed by Peggy Ngila and David
Chiawo; the data were analyzed by Peggy Ngila and Jane Mugo; and the
manuscript was written under their direction of David Chiawo, ~Margaret
Owuor and Oliver Wasonga.~Libby Ellwood contributed greatly in improving
this manuscript.

\textbf{Acknowledgements}

The Center for Biodiversity Information Development at Strathmore
University provided research fellowship and institution support for this
study. The study was funded by the European Union through Global
Biodiversity Information Facility Project No. BID-AF2020-014-NAC.
Raptors data was provided by GBIF and the Peregrine Fund through the
GBIF data site. The National Museums of Kenya, Idigbio, the African
Conservation Center, South Eastern Kenya University, and Kenyatta
University provided institutional support for niche modeling training.

\textbf{Conflict of Interest}

The researchers acknowledge that they are not aware of any personal or
financial conflicts that might have affected the research presented in
this document.

\textbf{References}

\par\null

\textbf{Appendix}

\textbf{Table 1.} The percentage contribution of environmental variables
used in the five~raptor species distribution model. Environmental
variables are calculated at a resolution of about 1km.\selectlanguage{english}
\begin{longtable}[]{@{}lllllll@{}}
\toprule
\textbf{Abbrev} & \textbf{Variables predictors} & \textbf{Percent
contribution (\%)} & \textbf{Percent contribution (\%)} &
\textbf{Percent contribution (\%)} & \textbf{Percent contribution (\%)}
& \textbf{Percent contribution (\%)}\tabularnewline
& & \textbf{Bateleur} & \textbf{Martial Eagle} & \textbf{Steppe Eagle} &
\textbf{Southern ground hornbill} & \textbf{Secretary
bird}\tabularnewline
Aspect & Aspect (\textsuperscript{o} from 0 to 360) & 0.9 & 1.9 & 1.9 &
1.4 & 1.5\tabularnewline
bio13 & Precipitation of the wettest month (mm) & 5.9 & 5.3 & 6.2 & 7.1
& 9.9\tabularnewline
bio14 & Precipitation of the driest month (mm) & 17.8 & 18.2 & 15.4 &
13.5 & 13.1\tabularnewline
bio15 & Precipitation seasonality (cv)\textsuperscript{a} & 12.1 & 9.2 &
9.0 & 11.5 & 8.4\tabularnewline
bio18 & Precipitation of the warmest quarter (mm) & 16.7 & 14.9 & 19.9 &
21.8 & 14.0\tabularnewline
bio19 & Precipitation of the coldest quarter (mm) & 14.3 & 13.8 & 10.3 &
8.9 & 12.3\tabularnewline
bio3 & Isothermality (\%)\textsuperscript{b} & 5.5 & 6.2 & 4.4 & 5.0 &
8.7\tabularnewline
bio7 & Annual Temperature range (\textsuperscript{o}C) & 7.0 & 6.5 & 7.7
& 7.3 & 7.9\tabularnewline
bio9 & Mean temperature of the driest quarter (\textsuperscript{o}C) &
12.0 & 14.6 & 10.4 & 5.5 & 9.6\tabularnewline
HII & Human influence index & 2.2 & 2.5 & 4.1 & 4.9 & 3.2\tabularnewline
NDVI & Normalized difference vegetation index (2013-2020) & 4.1 & 4.9 &
8.5 & 4.7 & 8.3\tabularnewline
Roughness & Roughness (m) & 1.6 & 1.9 & 2.0 & 8.4 & 2.9\tabularnewline
\bottomrule
\end{longtable}

\textsuperscript{a} Precipitation seasonality (Coefficient of variation)

\textsuperscript{b} Isothermality (Mean diurnal range/Temperature annual
range) (X100)

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