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The contribution of community-based conservation models to conserving large herbivore populations

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In East Africa, community-based conservation models (CBCMs) have been established to support the conservation of wildlife in fragmented landscapes like the Tarangire Ecosystem, Tanzania. To assess how different management approaches maintained large herbivore populations, we conducted line distance surveys and estimated seasonal densities of elephant, giraffe, zebra, and wildebeest in six management units, including three CBCMs, two national parks (positive controls), and one area with little conservation interventions (negative control). Using a Monte-Carlo approach to propagate uncertainties from the density estimates and trend analysis, we analyzed the resulting time series (2011–2019). Densities of the target species were consistently low in the site with little conservation interventions. In contrast, densities of zebra and wildebeest in CBCMs were similar to national parks, providing evidence that CBCMs contributed to the stabilization of these migratory populations in the central part of the ecosystem. CBCMs also supported giraffe and elephant densities similar to those found in national parks. In contrast, the functional connectivity of Lake Manyara National Park has not been augmented by CBCMs. Our analysis suggests that CBCMs can effectively conserve large herbivores, and that maintaining connectivity through CBCMs should be prioritized.
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The contribution
of community‑based conservation
models to conserving large
herbivore populations
Christian Kiner
1,2,3*, Charles A. H. Foley
4, Derek E. Lee
5, Monica L. Bond
5,6, John Kioko
2,
Bernard M. Kissui
2, Alex L. Lobora
7, Lara S. Foley
4 & Fred Nelson
8
In East Africa, community‑based conservation models (CBCMs) have been established to support the
conservation of wildlife in fragmented landscapes like the Tarangire Ecosystem, Tanzania. To assess
how dierent management approaches maintained large herbivore populations, we conducted line
distance surveys and estimated seasonal densities of elephant, girae, zebra, and wildebeest in six
management units, including three CBCMs, two national parks (positive controls), and one area with
little conservation interventions (negative control). Using a Monte‑Carlo approach to propagate
uncertainties from the density estimates and trend analysis, we analyzed the resulting time series
(2011–2019). Densities of the target species were consistently low in the site with little conservation
interventions. In contrast, densities of zebra and wildebeest in CBCMs were similar to national parks,
providing evidence that CBCMs contributed to the stabilization of these migratory populations in the
central part of the ecosystem. CBCMs also supported girae and elephant densities similar to those
found in national parks. In contrast, the functional connectivity of Lake Manyara National Park has
not been augmented by CBCMs. Our analysis suggests that CBCMs can eectively conserve large
herbivores, and that maintaining connectivity through CBCMs should be prioritized.
Keywords Conservation eectiveness, Community-based conservation, Population dynamics, Social-
ecological systems, Fortress conservation
Populations of large herbivores in East African savanna and grassland ecosystems move widely through land-
scapes where the distribution of forage and water vary considerably across space and time, resulting in seasonal
animal migrations1. ese herbivore populations in East Africa have declined markedly during the last decades2
both inside and outside fully protected areas35 as a result of unsustainable legal and illegal hunting6, deteriora-
tion of rangelands7, and loss and fragmentation of habitat due to expansion of agriculture and infrastructure8,9.
Acknowledging that government protected areas alone are insucient as a single measure to halt or reverse
wildlife declines and to create landscapes that support both people and wildlife10, a diverse set of community-
based conservation models (CBCMs) has been implemented in several parts of East Africa. In practice, these
CBCMs oen augment existing protected area networks and safeguard critical habitats on community and pri-
vate lands11,12. In ecosystems that still sustain long distance migrations of large herbivores, CBCMs can provide
suitable and safe habitat for wildlife and can contribute to eective conservation of migratory populations1,13.
CBCMs in the region oen aim for sustainable coexistence between wildlife and the livestock of pastoralist
communities, as well as the Indigenous communities’ rangeland management practices, which include seasonal
grazing reserves and rules for pasture access14,15.
Testing the eectiveness of conservation interventions is a key topic in conservation biology16, yet fre-
quently hampered by a lack of monitoring data in CBCMs17,18. e few site-specic assessments of the ecological
OPEN
1Junior Research Group Human-Wildlife Conict and Coexistence, Leibniz Centre for Agricultural Landscape
Research (ZALF), Müncheberg, Germany. 2The School for Field Studies, Centre For Wildlife Management Studies,
PO Box 304, Karatu, Tanzania. 3Department of Land Use & Governance, Humboldt-University of Berlin, Berlin,
Germany. 4Tanzania Conservation Research Program, Lincoln Park Zoo, Chicago, IL, USA. 5Wild Nature Institute,
Concord, NH, USA. 6Department of Evolutionary Biology and Environmental Studies, University of Zurich, Zurich,
Switzerland. 7Tanzania Wildlife Research Institute (TAWIRI), Arusha, Tanzania. 8Maliasili, Essex Junction, VT,
USA. *email: christian.kiner@zalf.de
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eectiveness of dierent Tanzanian CBCMs indicate mixed results, including wildlife population declines, sta-
ble population trends, and marked population size increases1924. Ecological assessments of CBCMs are oen
solely based on temporal trends of target species in one study area25 or on comparisons with density estimates
in either human-dominated20,21,26 or fully protected areas22. While valuable for informing local management in
the short-term, such site-specic monitoring eorts only partially inform whether CBCMs eectively contribute
to conserving wildlife populations at the ecosystem scale. is is because the distribution of large mammals in
heterogeneous savanna ecosystems is dynamic2729 with local population sizes aected by animal movement30.
To more accurately assess the ecological eectiveness of CBCMS, it is benecial to compare wildlife densities
across multiple management units, including positive reference points such as national parks (while keeping in
mind that they are not entirely pristine31,32), and areas with minimal conservation eorts.
e fragmented Tarangire Ecosystem of northern Tanzania (Fig.1) maintains one of the last remaining
long-distance migrations of large herbivores in Africa1,33. Compared to historical baselines, wildlife population
sizes have declined substantially inside and outside of protected areas, with particularly pronounced declines
during the 1980s and 1990s31,34,35. During the last two decades, three strategically placed community-based
Figure1. Map of the Tarangire Ecosystem in northern Tanzania; the inset in the top right indicates the location
of the ecosystem within Tanzania. Terrestrial line transects (black lines) were carried out in Lake Manyara
(LMNP) and Tarangire National Park (TNP), Burunge (BWMA) and Randilen Wildlife Management Area
(RWMA), Manyara Ranch (MR), and the Mto wa Mbu Game Controlled Area (MGCA, no boundary data
available). Mkungunero Game Reserve (MGR) and Makame Wildlife Management Area (MWMA). ‘Northern
Plains’ and ‘Simanjiro Plains’ denote approximate locations of the wet season ranges of zebra (Equus quagga)
and wildebeest (Connochaetes taurinus). For completeness, we also mapped Mkungunero Game Reserve (MGR)
and Makame Wildlife Management Area (MWMA) which are part of the TE but not sampled. Blue polygons
are alkaline lakes. We created both maps (study area within Tanzania and details of our study area) using the
ggplot2’ (version 3.3.6), ’rgdal’ (version 1.5–23), and ’ggsn’ (version 0.5.0) packages in R (version 4.2.2)70. Maps
are based on open source (area polygons, lakes) and our own (transects) shapeles.
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conservation areas [Burunge and Randilen Wildlife Management Area (WMA), and Manyara Ranch] were
established in parts of the ecosystem36, mostly around Tarangire National Park (NP), to counteract these declines.
ese community-based conservation areas protect specic habitats for wildlife while allowing limited human
activity. Some CBCMs allow seasonal livestock grazing in specied areas, some allow regulated hunting, and
they typically employ game scouts to enforce wildlife laws and community land-use regulations. e type and
intensity of natural resource utilization and the degree of community involvement in governance dier by CBCM
category (see Table1).
Here, we test how these conservation eorts aected site-specic population dynamics of four wide-ranging,
abundant, and functionally important wildlife species: African savanna elephant (Loxodonta africana), Masai
girae (Giraa tippelskirchi), plains zebra (Equus quagga), and wildebeest (Connochaetes taurinus). Wildlife
populations were estimated from line distance sampling carried out seasonally from 2011 to 2019 in Burunge
WMA, Randilen WMA, and Manyara Ranch. As spatial reference points, we considered population densities
and associated trends in two fully protected areas: Tarangire NP, which is buered by the considered CBCMs
and Lake Manyara NP, which is not directly bordered by CBCMs. In addition, as counterfactual, we considered
wildlife population trends in an area with few restrictions on human land use, Mto wa Mbu Game Controlled
Area (GCA) (Fig.1). To estimate area-specic annual trends, we tted generalized additive models to seasonal
density data and used a two-stage Monte Carlo simulation approach which propagates uncertainties from both
the distance sampling estimates and trend analysis.
Results
Area‑specic trends of large herbivore populations
Based on estimates from terrestrial line distance surveys and generalized additive models, elephant densities
in Burunge WMA (Fig.2c) increased over the survey period, uctuated widely but appeared to remain fairly
Table 1. Key information on management practices and monitoring eorts for each considered management
unit in the Tarangire Ecosystem, northern Tanzania.
Management unit Overall conservation model Hunting regulations Livestock management Wildlifemonitoring eorts
Tarangire National Park (positive
control)
Top down management by
employees of Tanzania National
Parks; state-funded conservation;
main revenue: income from photo-
graphic tourism
No hunting allowed. Enforcement
implemented by rangers employed
by Tanzania National Parks
No livestock allowed. Enforcement
implemented by rangers employed
by Tanzania National Parks
Surveys were conducted by
driving along road transects with
open-top vehicles. A total of 24
seasonal surveys were carried out
from October 2011 to October
2019
Lake Manyara National Park
(positive control)
Top down management by
employees of Tanzania National
Parks; state-funded conservation;
main revenue: income from photo-
graphic tourism
No hunting allowed. Enforcement
implemented by rangers employed
by Tanzania National Parks
No livestock allowed. Enforcement
implemented by rangers employed
by Tanzania National Parks
Surveys were conducted by
driving along road transects with
open-top vehicles. A total of 24
seasonal surveys were carried out
from November 2011 to Novem-
ber 2019
Burunge wildlife management area
(community-based conservation
model CBCM)
Community-based conserva-
tion, managed by elected council
members from WMA villages;
main revenue: income from
photographic tourism and dona-
tions from non-governmental
organizations
e western section initially con-
tained a hunting block. However,
since 2014, allocated quotas are
not realized and the hunting block
is managed for photographic tour-
ism. Enforcement is implemented
by village game scouts who are
typically residents of the member
villages, employed by the Wildlife
Management Area
e area is structured into distinct
management zones. No livestock
is allowed in areas dedicated to
wildlife
Surveys were conducted by driv-
ing or walking along systemati-
cally distributed transects. A total
of 7 seasonal surveys were carried
out from September 2011 to July
2018
Randilen wildlife management
area (community-based conserva-
tion model CBCM)
Community-based conserva-
tion, managed by elected council
members from WMA villages;
main revenue: income from
photographic tourism and dona-
tions from non-governmental
organizations
No hunting allowed. Enforcement
is implemented by village game
scouts who are typically residents
of the member villages, and
employed by the Wildlife Manage-
ment Area
e area is structured into distinct
management zones. No livestock
is allowed in areas dedicated to
wildlife
Surveys were conducted by
driving along road transects with
open-top vehicles. A total of 12
seasonal surveys were carried out
from January 2012 to October
2015
Manyara Ranch (community-
based conservation model CBCM)
Conservancy: Community-
based conservation, managed by
employees of the conservancy with
input from advisory board which
includes representatives from
member villages; main revenue:
donations from non-governmental
organizations, supplemented with
income from photographic tour-
ism and livestock
No hunting allowed. Enforcement
is implemented by game scouts
who are typically residents of the
member villages, and employed by
a non-governmental organization
Manyara Ranch owns its own
cattle and sheep herds which are
managed using a grazing rotation.
During the dry season, herders
from member villages are permit-
ted to graze their own livestock in
specied areas of the ranch
Surveys were conducted by
driving along road transects with
open-top vehicles. A total of 24
seasonal surveys were carried out
from November 2011 to Novem-
ber 2019
Mto wa Mbu game controlled area
(negative control)
Top down management by
employees of Tanzania Wildlife
Authority; state-funded conserva-
tion; main revenue: income from
hunting blocks and photographic
tourism
e northern section of the area
contains a hunting block where
trophy hunting is allowed. Hunt-
ing is limited though a quota
system. Hunting restrictions are
enforced by the Tanzania Wildlife
Authority
High densities of livestock, grazing
is largely unregulated
Surveys were conducted by
driving along road transects with
open-top vehicles. A total of 24
seasonal surveys were carried out
from November 2011 to Novem-
ber 2019
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constant in Tarangire NP (Fig.2a) and Manyara Ranch (Fig.2e), and seemed to decline in Lake Manyara NP
(Fig.2b, TableS1). During 24 seasonal surveys, we did not detect any elephant in the Mto wa Mbu GCA (Fig.2f).
Across the monitoring period, girae densities showed a slight increase in Tarangire NP (Fig.3a) and
remained relatively constant in both Burunge WMA (Fig.3c) and Lake Manyara NP (Fig.3b; TableS1). In
Randilen WMA (Fig.3d) and Manyara Ranch (Fig.3e), girae densities showed a slight upward trend. In the
Mto wa Mbu GCA, girae densities were much lower than in the other management units (Fig.3f).
Zebra densities increased markedly in Tarangire NP (Fig.4a), and remained fairly constant in Lake Manyara
NP (Fig.4b), Burunge WMA (Fig.4c), Randilen WMA (Fig.4d), and Manyara Ranch (Fig.4e; TableS1). In the
Mto wa Mbu GCA (Fig.4f), zebra densities were considerably lower compared to the other management units.
Seasonality strongly aected zebra densities in Tarangire NP (Fig.4a), reecting their seasonal long-distance
Figure2. Population density estimates and associated 95% condence intervals for elephant (Loxodonta
africana). e trend lines are based on 1000 Monte Carlo replicates and modelled as season-specic (LR:
long rains; Dry: dry season; SR: short rains) generalized additive models across six management units (TNP:
Tarangire National Park; LMNP: Lake Manyara National Park; BWMA: Burunge Wildlife Management Area;
RWMA: Randilen Wildlife Management Area; MR: Manyara Ranch; MGCA: Mto wa Mbu Game Controlled
Area) of the Tarangire Ecosystem, northern Tanzania. Population density estimates are based on terrestrial line
distance sampling surveys. In RWMA, elephant were not counted during the surveys.
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movements (i.e. concentration during the dry season inside Tarangire NP, migration to areas outside the NP
during the long rains).
Across the survey period, wildebeest densities remained relatively constant in Tarangire NP (Fig.5a), with
potential increases during the dry season in this core dry season range. In Lake Manyara NP (Fig.5b) and Man-
yara Ranch (Fig.5e; TableS1), their densities did not change substantially over the study period. In Burunge
WMA, however, their densities increased markedly (Fig.5c). Similar to zebra, wildebeest densities were com-
parably low in Randilen WMA (Fig.5d) and the Mto wa Mbu GCA (Fig.5f). As with zebra, seasonality strongly
aected wildebeest densities in Tarangire NP, with both species practically absent from the park during the long
rains and reaching high densities during the dry season.
Figure3. Population density estimates and associated 95% condence intervals for girae (Giraa
tippelskirchi). e trend lines are based on 1000 Monte Carlo replicates and modelled as season-specic (LR:
long rains; Dry: dry season; SR: short rains) general additive models across six management units (TNP:
Tarangire National Park; LMNP: Lake Manyara National Park; BWMA: Burunge Wildlife Management Area;
RWMA: Randilen Wildlife Management Area; MR: Manyara Ranch; MGCA: Mto wa Mbu Game Controlled
Area) of the Tarangire Ecosystem, northern Tanzania. Population density estimates are based on terrestrial line
distance sampling surveys.
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Discussion
Based on long-term, systematic wildlife monitoring data, we describe population dynamics of four wide-ranging
large herbivore species for ve protected areas, including three CBCMs, two national parks, and a lesser protected
Game Controlled Area in the Tarangire Ecosystem of Tanzania. While our site-based monitoring highlights het-
erogeneity and seasonality in species-specic densities, we show that densities of the four target species in CBCMs
are comparable to those in adjacent Tarangire NP22, and occasionally even higher than those in Lake Manyara NP
(Fig.6). Moreover, large herbivore densities were consistently greater than in the Game Controlled Area, which
served as a negative control as there are limited conservation eorts in place. Moreover, in Burunge WMA, we
detected marked increases in wildebeest and elephant densities over our study period. In Randilen WMA, girae
densities have increased (Fig.6). Especially for the central part of the ecosystem (Tarangire NP, Burunge WMA,
Figure4. Population density estimates and associated 95% condence intervals for zebra (Equus quagga).
e trend lines are based on 1000 Monte Carlo replicates and modelled as season-specic (LR: long rains;
Dry: dry season; SR: short rains) generalized additive models across six management units (TNP: Tarangire
National Park; LMNP: Lake Manyara National Park; BWMA: Burunge Wildlife Management Area; RWMA:
Randilen Wildlife Management Area; MR: Manyara Ranch; MGCA: Mto wa Mbu Game Controlled Area) of
the Tarangire Ecosystem, northern Tanzania. Population density estimates are based on terrestrial line distance
sampling surveys.
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and Manyara Ranch), population trends of the target species were mostly either stable or indicated population
growth over time. is mirrors data from photographic mark-recapture studies of both wildebeest, showing that
their population in the ecosystem has stabilized since the early 2000s34, as well as girae, whose populations
in Manyara Ranch and much of Tarangire NP were stable from 2012–201637. Our population trend estimates
also align with results of a photographic mark-recapture study conducted from 2012 to 2016, indicating a slight
decrease in the girae population over that time frame in Burunge WMA37 (Fig.2c); our more recent data suggest
that this trajectory has since reversed (Fig.3c). Overall, our site-based, seasonal monitoring eorts suggest that
CBCMs contributed to the stabilization of large herbivore populations, with some areas experiencing increases
in numbers, highlighting that strategically placed28,38 and locally supported39 conservation approaches eectively
improved the resilience of wide-ranging herbivore populations in an increasingly fragmented ecosystem.
Figure5. Population density estimates and associated 95% condence intervals for wildebeest (Connochaetes
taurinus). e trend lines are based on 1000 Monte Carlo replicates and modelled as season-specic (LR:
long rains; Dry: dry season; SR: short rains) generalized additive models across six management units (TNP:
Tarangire National Park; LMNP: Lake Manyara National Park; BWMA: Burunge Wildlife Management Area;
RWMA: Randilen Wildlife Management Area; MR: Manyara Ranch; MGCA: Mto wa Mbu Game Controlled
Area) of the Tarangire Ecosystem, northern Tanzania. Population density estimates are based on terrestrial line
distance sampling surveys.
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Our results conrm previous observations that the oldest CBCM model in the ecosystem, Manyara Ranch
(established in 2001), sustains densities of girae, zebra, and wildebeest that are similar to those observed in
adjacent Tarangire NP19 and much higher than in the adjacent Mto wa Mbu GCA
26 as well as a relatively small
(compared to Tarangire NP) population of mostly male elephant40. ese relatively high densities of girae, zebra,
and wildebeest (and other species19) have been sustained over a long time span, suggesting that the concept of
Manyara Ranch appears to be working for conserving the current densities of large herbivores. Likely, this is
attributed to eective anti-poaching eorts and the enforcement of a limited grazing regime. e grazing strategy
aims to build up sucient grass biomass during the rainy season, allowing for dry season grazing of surrounding
communities’ livestock. Data associated with these interventions are currently not accessible, yet analyzing such
data would be valuable for assessing the eectiveness of these conservation interventions.
Wildlife monitoring eorts in the Burunge WMA (established in 2006) further highlight the contribution
of CBCMs in conserving large herbivores in the ecosystem. Marked increases in elephant densities over time
(Fig.2c), increased density of girae aer an initial decrease (Fig.3c), zebra densities greater than outside the
area dedicated to wildlife21, and a growing wildebeest population (Fig.5c) that reached densities similar to
those observed in neighboring, fully protected Tarangire NP22 suggest that Burunge WMA has been eective
in conserving large herbivores. is is substantiated by a before-aer-control-impact study which documented
that adjusting management in Burunge MWA improved girae survival21.
In the case of the newest CBCM of the ecosystem, Randilen WMA (established in 2014), the contribution
to herbivore conservation eorts is perhaps not as obvious. However, as Randilen WMA is not situated in the
core migratory routes of the ecosystem28,41,42, it is not surprising that zebra and wildebeest densities were low,
Figure6. Trends in population density estimates for elephant (Loxodonta africana), girae (Giraa
tippelskirchi), zebra (Equus quagga) and wildebeest (Connochaetes taurinus) across six management units (TNP:
Tarangire National Park; LMNP: Lake Manyara National Park; BWMA: Burunge Wildlife Management Area;
RWMA: Randilen Wildlife Management Area; MR: Manyara Ranch; MGCA: Mto wa Mbu Game Controlled
Area) in the Tarangire Ecosystem, northern Tanzania. e plotted values represent density estimates for each
season (LR: long rains; Dry: dry season; SR: short rains) over dierent years, based on model predictions.
Initial density estimates (lighter circles) are averages from the rst two years of the time series, and nal
density estimates (bolder circles) are from the last two years (note varying time series lengths across areas).
Density estimates were derived from terrestrial line distance sampling surveys, using Monte Carlo simulations
and generalized additive models to model trends. Percent changes indicate the relative dierence in average
population density between initial and nal periods.
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or occurred sporadically during the rainy season, and did not seem to have increased systematically over the
relatively short monitoring period. Nevertheless, the seemingly positive trends in the girae population (also
supported by more detailed studies within the Randilen WMA20) is encouraging and suggest that resident wildlife
populations may have benetted from the implementation of similar management activities to those instituted
in Burunge WMA: the eective protection of rangelands from conversion to agriculture or settlements, locally
enforced grazing regulations, and community enforcement of anti-poaching measures20,22.
During the last two decades, most conservation eorts have focused on areas outside established protected
areas in the central part of the ecosystem, i.e. areas around Tarangire NP that are essential for the seasonal
migration in and out of the national park. A substantial fraction of these migratory routes are now protected
by CBCMs and other community land-use regulations12,43, with only a few bottlenecks remaining along the
migratory routes28,42. Our ground-based monitoring indicates that the zebra population has increased (Fig.4a)
and that the wildebeest population has stabilized and possibly even increased during the dry season (Fig.5a) in
Tarangire NP from 2011 to 2019. As both species spend approximately half of the year outside Tarangire NP in
village lands and CBCMs, concentrating in Tarangire NP only during the dry season, these population increases
provide circumstantial evidence that conservation measures outside Tarangire NP are eective in bolstering
migratory ungulate populations. Since Tarangire NP constitutes the main dry season range for wildebeest and
zebra34,44, it is likely that these population size trajectories are due to intrinsic population growth and not due to
immigration. On the other hand, demographic monitoring of the Tarangire elephant population documented
rapid population growth over the past decades, once poaching was eectively curbed in the 1990s45. is rapid
growth is not evident from our line transect monitoring for the core of Tarangire NP (Fig.2a).
Our comparison of wildlife trends within the ecosystem (summarized in Fig.6) supports the idea that con-
serving functional connectivity8,13 is key to supporting populations of large herbivores in the ecosystem. Little to
no conservation eorts have been directed towards protecting the remaining connectivity between Lake Manyara
NP and the wider ecosystem. While factors internal to Lake Manyara NP (especially bush encroachment)46, may
have contributed to stagnant and low densities of zebra and wildebeest (which have become largely resident) and
a declining population of elephant, it is plausible that the insularization of Lake Manyara NP47 contributed to this
worrisome development. ere remains some connectivity38, as evidenced by one documented girae movement
between Manyara Ranch to Lake Manyara NP and back again48. Further, high levels of precipitation in 2019 and
2020 swamped the shortgrass plains habitat along Lake Manyara, and in 2021, no wildebeest were observed in
Lake Manyara NP (DEL and MLB, pers. obs.). It is assumed these animals moved out of Lake Manyara NP, again
suggesting some connectivity remains.
Similar to Lake Manyara, population densities of resident large herbivores in the Mto wa Mbu GCA, where
there are limited anti-poaching measures, are very small (girae), functionally absent (elephant), and generally
well below historical baselines35. Nevertheless, wildlife still occurs in this area and the landscape seems to be
permeable for wildlife, especially for wildebeest and zebra which use this area for their annual migration to the
Northern Plains28, even though agricultural and infrastructural development threaten this functional connectiv-
ity in several locations49.
Small, isolated populations are more vulnerable to extinction than large, connected populations because of
stochastic demographic, environmental, and genetic threats50. In light of these threats, and the presence of small
populations in the Tarangire Ecosystem such as in Lake Manyara NP, our results suggest that the conservation
value of the CBCMs is at least twofold: (1) they eectively increase the area of suitable habitat well beyond core
protected areas such as national parks; (2) they support densities of resident large herbivores comparable to those
in national parks, thereby increasing eective population sizes of these species in the ecosystem. In addition,
CBCMs eectively protect areas that are essential parts of migratory routes in the ecosystem and provide access
to seasonally available resources28,42, and thus likely contribute to population increases of migratory ungulate
populations inside Tarangire NP. Consequently, CBCMs in the Tarangire Ecosystem have been instrumental in
preserving space for the annual migration of large herbivores and maintaining ecosystem functioning associated
with migratory and resident wildlife populations51.
In times of a global biodiversity crisis52 and widespread wildlife declines across Africa2,53, halting these
declines and demonstrating population increases in newly established CBCMs are important steps towards
longer-term conservation success. However, such ‘success’ should be viewed within the context of long-term
environmental processes and cognitive biases such as the shiing baseline syndrome54. We are aware that wild-
life populations were historically much more numerous in the Tarangire Ecosystem31. Historical anecdotes and
long-term data further arm higher wildlife densities in the Tarangire Ecosystem in the past31,35. For instance,
based on aerial ecosystem-wide surveys, the wildebeest population in the ecosystem exhibited markedly greater
densities during the late 1980s and early 1990s, with an average population size from 1987 to 1994 estimated
at approximately 39,000 wildebeest. In contrast, by 2011, it had dwindled to only around 12,000 wildebeest34.
Because human impacts on wildlife populations started well before the rst biodiversity assessments were
conducted55, we may never know the “true” potential for wildlife densities in the ecosystem, which in any case
may have uctuated considerably. For instance, the rst dry season counts of wildebeest and zebra in Tarangire
NP during the early 1960s estimated only 1,200 and 2,500 animals, respectively, and while this was likely only a
portion of the total Tarangire Ecosystem population, populations of both species had already suered signicant
declines in the previous 50years, which was attributed to a decline in dry season water sources27. Given the past
and current human population growth rate in Tanzania56 and associated need for land required for infrastructure
and agriculture57, it is debatable if historical wildlife population sizes (if they were ever to be known) are realistic
quantitative targets for ecosystem restoration eorts. Critical wildlife habitats, such as dry season concentration
areas north of Lake Manyara NP27, have been irrevocably lost for wildlife35. ese substantial and likely irrevers-
ible landscape changes have likely reduced the overall carrying capacity for wildlife in the ecosystem. However, it
is plausible that wildlife densities in the Tarangire Ecosystem have the potential to increase even under the current
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extent of core protected areas, combined with the growing extent of multi-use areas where wildlife coexists with
livestock and people (e.g. Manyara Ranch, and village lands designated primarily for livestock grazing). Our
ndings demonstrate that such multi-use, locally managed conservation areas can contribute to the restoration
of wildlife numbers in important migratory routes and seasonal ranges, which, with appropriate support, could
provide the basis for further wildlife recoveries in the ecosystem over time.
In the last three decades, the main strategy for ecosystem restoration in the Tarangire Ecosystem has been to
focus conservation eorts on protecting functional connectivity by creating diverse CBCMs to connect dry and
wet season ranges of migratory wildlife. Overall, site-based monitoring suggests that this pragmatic conservation
approach was eective. Considering that the majority of wildebeest and zebra spend approximately half of the
year outside protected areas, crossing several main roads and traversing areas of human settlement during their
seasonal migration, this is a remarkable example for human-wildlife coexistence in a human-dominated land-
scape, with important implications for conservation policy in East African savanna rangelands and potentially
beyond. At the same time, we caution against excessive contentedness with the achieved outcomes. Foremost,
several bottlenecks along migratory routes are still threatened by land-use change and expanding settlements;
eectively securing these remaining gaps through community-supported actions should be prioritized and may
yield a high return on investment for ecosystem conservation eorts49. Considering much greater densities of
migratory wildlife species during the not too distant past31, we encourage conservationists to formulate and
strive for bolder goals for the restoration of wildlife populations in the Tarangire Ecosystem. Such ecological
restoration goals are likely best achieved if conservation measures are designed as social-ecological endeavors58,
support Indigenous land and resource tenure and management systems that foster coexistence of livestock and
wildlife, ensure that people benet from increasing wildlife populations, and provide cost-eective ways to
minimize costs associated with increasing wildlife populations such as crop damages, livestock depredation,
threats to human wellbeing and opportunity, and transaction costs associated with preventing wildlife-related
damages. Assessing the eectiveness of such restoration eorts not only requires renewed investment in long-
term and ecosystem-wide wildlife monitoring eorts but also in monitoring schemes that assess indicators of
social sustainability in CBCMs.
Methods
Study area
e climate of the Tarangire Ecosystem is semi-arid and characterized by a bimodal rainfall pattern: a long dry
season (June to October) is followed by a short rainy season (November to December), a short dry season (Janu-
ary to February), and then long rains (March to May). Annual precipitation ranges between 434 and 824mm; the
vegetation is characterized by mosaics of Vachellia-Commiphora bushland and woodland, edaphic grasslands,
and riverine vegetation59. e extent of the ecosystem (c. 30,000 km2) encompasses the annual movement range
of migratory grazers (Fig.1): during the dry season, zebra and wildebeest mainly concentrate in the northern
part of Tarangire NP (total area c. 2650 km2) and Manyara Ranch (total area c. 182 km2). In these areas, surface
water (in Tarangire NP provided by the Tarangire River, the Silale swamps, and some human-enhanced water-
holes; in Manyara Ranch provided by the Makuyuni River and human-made dams) and sucient grass biomass
is available during the dry season. At the onset of the short rains, wildebeest and zebra leave Tarangire NP. About
half of the wildebeest population migrates eastwards to the Simanjiro Plains and the other half migrates to the
northern plains near Lake Natron44,60 where females give birth in the nutrient and mineral rich grasslands61.
Around June, as the surface water on the plains dries up, they return to their dry season ranges.
e eastward migration to the Simanjiro Plains is facilitated by Certicates of Customary Rights of Occupancy
(CCRO) that began as conservation easements in 2006. e CCROs secure legal communal title over lands used
traditionally for seasonal livestock grazing. ese rangelands are conserved by local communities to protect their
livestock grazing areas; CCRO by-laws permit livestock keeping but agriculture and settlements are not allowed12.
e northern migration is strengthened by Manyara Ranch (established in 2001), an unfenced area managed
for coexistence between livestock and wildlife; here, pastoralists of two adjacent communities are granted graz-
ing rights and anti-poaching and grazing laws are enforced by rangers19. Along the northern migration route,
wildlife moves through the Mto wa Mbu GCA, where few restrictions on natural resource extraction are in
place28,35. Land-use changes from rangeland to agriculture and settlements are constricting wildlife movements42.
Girae62 and elephant63 do not typically move as far and as predictably as wildebeest and zebra, yet both species
have large annual home ranges that exceed the boundaries of protected areas45,62. Savanna elephants are mixed
feeders (grazing and browsing) and considered a facultative partially migratory species64 whereas Masai giraes
are primarily browsers and are considered a resident species with seasonal movements65. Both species occur
year-round in all of our study sites.
Adjacent to Tarangire NP, several villages established two Wildlife Management Areas, i.e. community-based
conservation and development areas that are spatially structured by land-use plans. Burunge WMA (c. 220 km2
delineated for wildlife conservation), ocially gazetted in 2006, lies to the west of Tarangire NP and connects to
Lake Manyara NP21,22 whereas Randilen WMA (c. 300 km2 delineated for wildlife conservation), established in
2014, is located northeast of Tarangire NP20. In the wildlife areas of Burunge and Randilen WMA, agriculture
and permanent settlements are not allowed, and village game scouts enforce community land-use regulations
and protect wildlife from illegal hunting. e ecosystem also contains the Mkungunero Game Reserve, located
between Tarangire NP and the Makame WMA. Some portion of the zebra and wildebeest migrate to these areas
during the wet season. However, for these areas we do not have long-term wildlife monitoring data.
Lake Manyara NP (lowland area covers c. 168 km2), located at the western edge of the ecosystem, is increas-
ingly isolated due to human development along its northern and southern boundaries. Wildlife in this NP is
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mostly resident, although occasional movement does occur42,48. In Tarangire and Lake Manyara NPs, conserva-
tion authorities restrict human use to photographic tourism and research.
Area‑specic density estimates
All wildlife surveys were conducted with permission from the Tanzania Commission for Science and Technology,
Tanzania Wildlife Research Institute, Tanzania National Parks, Burunge and Randilen WMA, Manyara Ranch,
and the villages of Losirwa, Esilalei, Mswakini, Lolkisale, and Emboret. All methods were carried out in accord-
ance with relevant guidelines and regulations, and our observational studies did not involve experiments with
animals. Starting in 2011, we established site-specic wildlife monitoring across multiple management units of
the ecosystem. From the end of 2011 to the end of 2019 (see Table1 for start and end months of the monitoring
eorts), we surveyed Lake Manyara NP, Tarangire NP, Manyara Ranch, and the Mto wa Mbu GCA three times per
year (24 surveys; in 2019, we omitted the dry season survey) to capture the main seasons66. In Burunge WMA, we
conducted seven surveys from 2011 to 201822 and in Randilen WMA, we monitored wildlife populations during
twelve seasonal surveys from 2012 to 201520. ese monitoring eorts were designed as line transect surveys67,
with transects mostly following roads (except for Burunge WMA, where transects were placed systematically). In
Tarangire and Lake Manyara NP, Manyara Ranch, and Mto wa Mbu GCA, transect length was typically 2km and
consecutive transects were separated by 0.5km; in Burunge WMA, transect length ranged from 0.5 to 10.3km;
in Randilen WMA, we used one single transect per survey (23–85km, length varied due to road conditions). For
most surveys, we used open-top 4 WD vehicles and slowly drove (10–20km h-1) along transects. A minimum of
two trained observers counted animals along transects. In Burunge WMA, approximately half of the transects
were walked by groups of three persons each. Upon detecting an animal or animal group, we moved to a position
perpendicular to the sighting, stopped the vehicle or halted the walk, counted the number of individual animals,
and measured the perpendicular distance between the transect and the initial position of the animal using a laser
rangender. If animals occurred in a group, we measured the perpendicular distance to the approximate center
of the group. ese data have been analyzed and published previously19,20,22,46,66, but we re-analyzed these data
for consistency and comparison within the same modeling framework.
To estimate densities we used Distance 6.067. Line distance methodology accounts for imperfect detection and
explicitly models the probability
P
of detecting an animal as a function of the distance from the transect. Due to
the non-random placement of transects, density estimates are possibly biased46 (but see19,24). erefore, we focus
on the temporal trend of density estimates and density comparisons across sites. In cases when surveys were
repeated within a single season, we summed eort and sightings68. We truncated the farthest 10% of distances,
t species- and area-specic half-normal detection functions with cosine extension69, and used the mean cluster
size of each season to extrapolate from cluster to animal density. Previously, we tested if detection functions
were mediated by season19,66; since including this covariate was not supported by model selection, detection
functions were pooled across all seasons. Sample size exceeded the recommended threshold of 60 sightings for
tting robust detection functions68 in the majority (18/22) of species-area combinations (TableS2). Based on
Kolmogorov–Smirnov goodness of t tests (TableS2), the majority of detection models (16/22) t the observed
data well. Visual assessment of detection functions (Figs.S1–S4) suggested a relatively good t but also indicated
that target species occasionally either avoided (few detections in rst distance bins) or were attracted to roads
(steep peak of detections in rst distance bin). Based on the derived global detection models, we used the post-
stratication option in Distance 6.0 for estimating season-specic densities (TableS3). is option allowed us to
generate separate density estimates for each season-year-species combination. e density estimate Dij for each
area i in year-season combination j was computed as:
where
nij
is the number of detections in stratum i during year-season j;
w
is the eective strip half-width;
Lij
is
the total length of transects surveyed in stratum i during year-season j, and
P
is the global detection function.
Trend analysis
To assess overall temporal trends for site-specic population trends, we used generalized additive models coupled
with a two-stage Monte Carlo sampling approach which enhances the robustness of our analyses by propagating
uncertainties from both the distance sampling-derived density estimates and the subsequent trend analysis53.
Furthermore, this methodological choice was driven by the heterogeneity in our dataset, characterized by unequal
year-season combinations across areas. Consequently, we constructed area-, species- and season-specic time
series. One exception was the Burunge WMA, where sampling occurred more sporadically. In this case, we
aggregated all survey data, disregarding seasonal variations to maintain consistency in trend analysis.
e rst stage of our Monte Carlo simulation, implemented in R 4.2.270, involved generating 1000 replicates
for each density estimate. We achieved this by simulating data points within the 95% condence intervals of
the original density estimates. For zero densities, we used the normal distribution. For non-zero densities, we
employed truncated normal distributions (implemented via the truncnorm package71), bounded by the lower
(L) and upper (U) limits of the estimated 95% condence intervals. For each density estimate (D), we calculated
the standard deviation (SDestimated) as:
We then generated simulated densities (Dsimualted) as follows:
D
i,j=
n
ij
2
wLij
P
SD
estimated =
U
L
2×1.96
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Subsequently, for each species-, area-, and season-specic time series, we tted a generalized additive model
using the mgcv package72. In these models, the year (Y) was treated as a smooth function, enabling us to capture
non-linear temporal trends without presupposing any specic functional form. e target variable for these
models was the set of 1,000 simulated data points generated in the rst Monte Carlo stage. e model can be
expressed as:
where
s(Y,k
=
4)
represents a smoothing spline function with a basis dimension of 4, allowing for the descrip-
tion of non-linear trends.
e second stage of our Monte Carlo methodology involved using the tted generalized additive models to
predict yearly density estimates. For each predicted year, we generated 1,000 simulated values. ese simulations
were designed to encompass the uncertainty inherent in the model predictions. We obtained the prediction
(Dpredicted) and the associated standard error (SE) for each year (Yp) from each model. We then simulated new
density values based on the predicted mean and standard errors:
We then used the mean values of these 1000 simulated values per year to depict trend lines for each seasonal
time series. is two-stage Monte Carlo approach, coupled with the exibility of generalized additive models,
allowed us to produce a nuanced and statistically robust analysis of population dynamics across species and
ecological contexts. We visualized the time series by plotting the observed density estimates and the predicted
yearly trends in ggplot273.
To separate overall population trends from noise (arising from uncertainty in density and trend estimates), we
condensed the key information of the time series. For each species, area, and season, we calculated the average
density estimates for the initial and nal years of the modelled time series. e initial and nal density estimates
were derived from the rst and last two years of the monitoring period, respectively. For Randilen WMA, the
initial years were 2012–2013 and the nal years were 2014–2015. For Tarangire NP, Lake Manyara NP, Manyara
Ranch, and Mto wa Mbu GCA, the initial and nal years varied by season: short rains (2011–2012 to 2018–2019),
long rains (2012–2013 to 2018–2019), and dry season (2012–2013 to 2017–2018; in 2019 we did not conduct dry
season counts). For Burunge WMA, we aggregated data from 2011–2012 and 2017–2018 across all seasons. We
then computed the percent change in density between the initial and nal periods using the formula:
is approach smooths out non-linearity and provides anindication of the direction and magnitude of
population changes over the study period.
Data availability
Density estimates derived by line distance sampling surveys are available in TableS3. e data and the code for
the corresponding trend analyses and Figs.2, 3, 4, 5, 6 are available at GöttingenResearchOnline: https:// doi.
org/ 10. 25625/ IC16AO.
Received: 23 February 2024; Accepted: 2 July 2024
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Acknowledgements
We thank the following institutions for funding the wildlife surveys: African Wildlife Foundation, Columbus
Zoo, Chem Chem Associations, IGF Foundation, PAMS Foundation, Ruord Foundation, Sacramento Zoo,
e School for Field Studies, Tulsa Zoo. We sincerely thank all rangers, SFS sta and students who participated
in the surveys.
Author contributions
C.K. designed the study, carried out eldwork in Lake Manyara NP, Tarangire NP, Manyara Ranch, Burunge
WMA and the Mto wa Mbu GCA, analyzed the data, created the gures and wrote the rst dra of the manu-
script. C.A.H.F. designed the study and contributed to the writing of the manuscript. D.E.L. designed the study,
carried out eldwork in Randilen WMA, and contributed to the writing of the manuscript. M.L.B. carried out
eldwork in Randilen WMA and critically reviewed and edited the manuscript. J.K. carried out eldwork in Lake
Manyara NP, Tarangire NP, Manyara Ranch and the Mto wa Mbu GCA, and critically reviewed and edited the
manuscript. B.M.K. carried out eldwork in Burunge WMA, and critically reviewed and edited the manuscript.
A.L.L. critically reviewed and edited the manuscript. L.S.F. critically reviewed and edited the manuscript. F.N.
designed the study, provided funding, and contributed to the writing of the manuscript.
Funding
Open Access funding enabled and organized by Projekt DEAL.
Competing interests
e authors declare no competing interests.
Additional information
Supplementary Information e online version contains supplementary material available at https:// doi. org/
10. 1038/ s41598- 024- 66517-9.
Correspondence and requests for materials should be addressed to C.K.
Reprints and permissions information is available at www.nature.com/reprints.
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