What factors drive ungulate dynamics in Laikipia District?

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What factors drive ungulate dynamics in Laikipia District? Nicholas J. Georgiadis 1 , J. G. Nasser Olwero 1 , and Gordon Ojwang’ 2 1. Mpala Research Centre, 2. Department of Resource Surveys and Remote Sensing 0 200 400 600 800 1000 1200 1965 1970 1975 1980 1985 1990 1995 2000 2005 Y ears A nnualRainfall(m m ) . RATIONALE Conservation strategy in Africa has broadened in scope from an initial focus on protectionism to the inclusion of human-occupied landscapes. Conserving large mammals in non-protected areas will depend partly on management intervention that is better informed about wildlife dynamics in livestock-dominated landscapes. We define and interpret the dynamics over the last 21 years of wild ungulates that share the landscape with livestock in Laikipia District, a non-protected savanna in central Kenya. The factors examined that could potentially influence ungulate dynamics were land use, rainfall-dependence, density-dependence, and trends over time. THE APPROACH The data comprised 13 sample surveys of the entirety of Laikipia District conducted by the Department of Resource Surveys and Remote Sensing between 1985 and 2005, the latter half in collaboration with Mpala Research Centre. Variation in the biomass density of 9 wild and two domestic species was compared among three contrasting land- use types (Fig. 1): commercial ranches that favor wildlife (‘pro-wildlife ranches’), communal ‘group ranches’ largely practicing pastoralism, and the remainder (‘transitional’ properties) using linear model selection. The influence of rainfall on ungulate dynamics was tested using a cumulative index of monthly rainfall deficit or surplus between successive surveys (Fig. 2). RESULTS 1.A summary of coefficients associated with variation in the biomass density of each species (Table 1) showed land use to greatly influence herbivore abundance. Wild herbivores were diverse and abundant on pro-wildlife properties (mean of 1.7 t.km -2 ), despite sharing the landscape with a slightly higher biomass density of livestock (mean of 2.7 t.km -2 ). Densities and diversities of wild species were lower on wetter transitional properties (mean of 0.64 t.km -2 ), and much lower on drier group ranches (mean of 0.10 t.km -2 ). Mean biomass densities of livestock on transitional and group ranches were respectively 7 and 23 times greater than wildlife densities. 2.Only the numerically dominant wild grazers and browsers showed evidence of density- dependence (zebra and giraffe). Four wild species exhibited rainfall-dependence (zebra, giraffe, Thomson’s gazelle and hartebeest), the latter three only in the first half of the time series. Cattle were only weakly rainfall-dependent because this is not a closed system for livestock (for example, livestock enter or leave the District during droughts). 3.All but two wild species (zebra and Grant’s gazelle) showed declining trends on at least one land-use type (Fig. 3). Declines were especially steep on pro-wildlife properties over the latter half of the series. SYNTHESIS AND APPLICATIONS 1.Well-supported insights into the dynamics of savanna herbivores affirmed the sufficiency of sample counting for most conservation and monitoring purposes in non- protected landscapes. 2.Where wildlife is not favored, a resilient few species (gazelles and plains zebra) can persist with high densities of livestock. Maintaining higher wild species diversity on communal properties will depend on the creation of conservancies from which livestock are, in effect, excluded, and which are sufficiently large and connected to prevent repeated local extinction and restoration. Such conservancies are expected to endure only if benefits from wildlife to community members exceed opportunity costs. Fig. 3. Biomass density time series and model fits for wildlife and livestock species in Laikipia District between 1985-2005, on three land-use types: pro-wildlife (open circles, solid line), transitional (black circles, dotted line) and group ranches (grey circles, dashed line). Table 1. Coefficients (with probability values in parenthesis) associated with model parameters remaining after model selection. Fig. 2. Mean annual rainfall over 40 years from 5 gauging stations scattered across the study area (bold black line, with standard errors). The 5-year moving average (dotted line) and long- term mean (639 mm; dashed line) are also given. Fig. 1. Distribution and relative abundance of livestock and wildlife in Laikipia District, February 2005, with 3 land-use types featured: pro- wildlife (white), group ranch (light grey), and transitional (dark grey). Apparent absence of herbivores signifies that none were observed within transects, not necessarily actual absence. Mpala Wildlife Foundation, the African Wildlife Foundation, and the Kenya Wildlife Service supported sample surveys. We acknowledge the dedication and expertise of the survey crews at DRSRS. M. Stanton, J. Goheen, M. Norton-Griffiths, R. Sensenig and R. Pringle gave helpful advice. Thanks to G. Grant, C. Mortensen, C. Burt, R. Vigne and J. Evans for rainfall data.

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What factors drive ungulate dynamics in Laikipia District? Nicholas J. Georgiadis 1 , J. G. Nasser Olwero 1 , and Gordon Ojwang’ 2 1. Mpala Research Centre, 2. Department of Resource Surveys and Remote Sensing. RESULTS - PowerPoint PPT Presentation

Transcript of What factors drive ungulate dynamics in Laikipia District?

Page 1: What factors drive ungulate dynamics in Laikipia District?

What factors drive ungulate dynamics in Laikipia District?

Nicholas J. Georgiadis1, J. G. Nasser Olwero1, and Gordon Ojwang’21. Mpala Research Centre, 2. Department of Resource Surveys and Remote Sensing

0

200

400

600

800

1000

1200

1965 1970 1975 1980 1985 1990 1995 2000 2005

Years

Ann

ual

Rai

nfal

l (m

m) .

RATIONALEConservation strategy in Africa has broadened in scope from an initial focus on protectionism to the inclusion of human-occupied landscapes. Conserving large mammals in non-protected areas will depend partly on management intervention that is better informed about wildlife dynamics in livestock-dominated landscapes.

We define and interpret the dynamics over the last 21 years of wild ungulates that share the landscape with livestock in Laikipia District, a non-protected savanna in central Kenya. The factors examined that could potentially influence ungulate dynamics were land use, rainfall-dependence, density-dependence, and trends over time.

THE APPROACHThe data comprised 13 sample surveys of the entirety of Laikipia District conducted by the Department of Resource Surveys and Remote Sensing between 1985 and 2005, the latter half in collaboration with Mpala Research Centre. Variation in the biomass density of 9 wild and two domestic species was compared among three contrasting land-use types (Fig. 1): commercial ranches that favor wildlife (‘pro-wildlife ranches’), communal ‘group ranches’ largely practicing pastoralism, and the remainder (‘transitional’ properties) using linear model selection. The influence of rainfall on ungulate dynamics was tested using a cumulative index of monthly rainfall deficit or surplus between successive surveys (Fig. 2).

RESULTS

1.A summary of coefficients associated with variation in the biomass density of each species (Table 1) showed land use to greatly influence herbivore abundance. Wild herbivores were diverse and abundant on pro-wildlife properties (mean of 1.7 t.km-2), despite sharing the landscape with a slightly higher biomass density of livestock (mean of 2.7 t.km -2). Densities and diversities of wild species were lower on wetter transitional properties (mean of 0.64 t.km -2), and much lower on drier group ranches (mean of 0.10 t.km-2). Mean biomass densities of livestock on transitional and group ranches were respectively 7 and 23 times greater than wildlife densities.

2.Only the numerically dominant wild grazers and browsers showed evidence of density-dependence (zebra and giraffe). Four wild species exhibited rainfall-dependence (zebra, giraffe, Thomson’s gazelle and hartebeest), the latter three only in the first half of the time series. Cattle were only weakly rainfall-dependent because this is not a closed system for livestock (for example, livestock enter or leave the District during droughts).

3.All but two wild species (zebra and Grant’s gazelle) showed declining trends on at least one land-use type (Fig. 3). Declines were especially steep on pro-wildlife properties over the latter half of the series.

SYNTHESIS AND APPLICATIONS

1.Well-supported insights into the dynamics of savanna herbivores affirmed the sufficiency of sample counting for most conservation and monitoring purposes in non-protected landscapes.

2.Where wildlife is not favored, a resilient few species (gazelles and plains zebra) can persist with high densities of livestock. Maintaining higher wild species diversity on communal properties will depend on the creation of conservancies from which livestock are, in effect, excluded, and which are sufficiently large and connected to prevent repeated local extinction and restoration. Such conservancies are expected to endure only if benefits from wildlife to community members exceed opportunity costs.

Fig. 3. Biomass density time series and model fits for wildlife and livestock species in Laikipia District between 1985-2005, on three land-use types: pro-wildlife (open circles, solid line), transitional (black circles, dotted line) and group ranches (grey circles, dashed line).

Table 1. Coefficients (with probability values in parenthesis) associated with model parameters remaining after model selection.

Fig. 2. Mean annual rainfall over 40 years from 5 gauging stations scattered across the study area (bold black line, with standard errors). The 5-year moving average (dotted line) and long-term mean (639 mm; dashed line) are also given.

Fig. 1. Distribution and relative abundance of livestock and wildlife in Laikipia District, February 2005, with 3 land-use types featured: pro-wildlife (white), group ranch (light grey), and transitional (dark grey). Apparent absence of herbivores signifies that none were observed within transects, not necessarily actual absence.

Mpala Wildlife Foundation, the African Wildlife Foundation, and the Kenya Wildlife Service supported sample surveys. We acknowledge the dedication and expertise of the survey crews at DRSRS. M. Stanton, J. Goheen, M. Norton-Griffiths, R. Sensenig and R. Pringle gave helpful advice. Thanks to G. Grant, C. Mortensen, C. Burt, R. Vigne and J. Evans for rainfall data.