Comparing Empirical with Perceived Trends in Wildlife, Livestock, Human Population and Settlement Numbers in Pastoral Systems: The Greater Maasai Mara Ecosystem, Kenya

Human activities are driving wildlife population declines worldwide. However, empirical understandings of their operation and consequences for wildlife populations and habitats are limited. We explored relationships between empirical and perceived wildlife and livestock population trends in Kenya using data on i) aerial monitoring of wildlife and livestock populations during 1977-2018, ii) human population censuses; and iii) semi-structured interviews with 338 male and female respondents from 250 households from four zones of the Greater Maasai Mara Ecosystem in 2019 and 2020. Wildlife numbers declined by 72.3% but sheep and goats increased by 306.4%. Yet nearly 50% of the interviewees perceived increases in wildlife numbers during 2011-2020 but concurrent decreases in livestock numbers because wildlife compete with livestock for resources. About one third of the respondents perceived an increase in the number of people living within conservancies and around the reserve and considered this indicative of a developing and thriving community. Notable discrepancies between the empirical and perceived trends were often more apparent than real and collectively suggest that incentives that promote wildlife are evidently viewed as less attractive than those that encourage increasing human and livestock numbers. Reconciling such apparent contradictions in empirical and perceived patterns is essential to extracting insights for formulating policies for sustaining livestock and wildlife populations and their habitats while promoting human welfare in grasslands

Spatiotemporal dynamics of wild herbivore species richness and occupancy across a savannah rangeland:Implications for conservation

Private lands are critical for maintaining biodiversity beyond protected areas. Across Kenyan rangelands, wild herbivores frequently coexist with people and their livestock. Human population and livestock numbers are projected to increase dramatically over the coming decades. Therefore, a better understanding of wildlife-livestock interactions and their consequences for biodiversity conservation on private lands is needed. We used a Bayesian hierarchical, multi-species and multi-year occupancy model on aerial survey data of 15 wild-herbivore species, spanning 15 years (2001–2016) to investigate a) spatiotemporal trends in species occurrence and richness across a mosaic of properties with different land uses in Laikipia County, central Kenya; and b) the effects of distance to water, vegetation and livestock relative abundance on species occurrence and richness. Although mean herbivore species richness varied little over time, we observed high spatial variation in species occurrence across Laikipia, mainly driven by negative effects of high livestock relative abundance. As expected, ‘wildlife friendly’ properties had higher herbivore species richness than other areas. However, high variability suggests that some pastoral properties support rich herbivore communities. The area occupied by five species with global conservation concerns (reticulated giraffe, Grevy's zebra, Beisa Oryx, Defassa waterbuck and gerenuk) and for which Laikipia County is one of the last refuges was <50% across years. We conclude that ‘wildlife friendly’ properties remain crucial for conservation, although some pastoralist areas offer suitable habitats for wild herbivores. Effective management of stocking rates is critical for maintaining ecosystems able to sustain livestock and wildlife on private lands, ensuring protection for endangered species

An integrated hierarchical classification and machine learning approach for mapping land use and land cover in complex social-ecological systems

Mapping land use and land cover (LULC) using remote sensing is fundamental to environmental monitoring, spatial planning and characterising drivers of change in landscapes. We develop a new, general and versatile approach for mapping LULC in landscapes with relatively gradual transition between LULC categories such as African savannas. The approach integrates a well-tested hierarchical classification system with the computationally efficient random forest (RF) classifier and produces detailed, accurate and consistent classification of structural vegetation heterogeneity and density and anthropogenic land use. We use Landsat 8 OLI imagery to illustrate this approach for the Extended Greater Masai Mara Ecosystem (EGMME) in southwestern Kenya. We stratified the landscape into eight relatively homogeneous zones, systematically inspected the imagery and randomly allocated 1,697 training sites, 556 of which were ground-truthed, proportionately to the area of each zone. We directly assessed the accuracy of the visually classified image. Accuracy was high and averaged 88.1% (80.5%–91.7%) across all the zones and 89.1% (50%–100%) across all the classes. We applied the RF classifier to randomly selected samples from the original training dataset, separately for each zone and the EGMME. We evaluated the overall and class-specific accuracy and computational efficiency using the Out-of-Bag (OOB) error. Overall accuracy (79.3%–97.4%) varied across zones but was higher whereas the class-specific accuracy (25.4%–98.1%) was lower than that for the EGMME (80.2%). The hierarchical classifier identified 35 LULC classes which we aggregated into 18 intermediate mosaics and further into five more general categories. The open grassed shrubland (21.8%), sparse shrubbed grassland (10.4%) and small-scale cultivation (13.3%) dominated at the detailed level, grassed shrubland (31.9%) and shrubbed grassland (28.9%) at the intermediate level, and grassland (35.7%), shrubland (35.3%) and woodland (12.5%) at the general level. Our granular LULC map for the EGMME is sufficiently accurate for important practical purposes such as land use spatial planning, habitat suitability assessment and temporal change detection. The extensive ground-truthing data, sample site photos and classified maps can contribute to wider validation efforts at regional to global scales

Cross-boundary human impacts compromise the Serengeti-Mara ecosystem

Protected areas provide major benefits for humans in the form of ecosystem services, but landscape degradation by human activity at their edges may compromise their ecological functioning. Using multiple lines of evidence from 40 years of research in the Serengeti-Mara ecosystem, we find that such edge degradation has effectively “squeezed” wildlife into the core protected area and has altered the ecosystem’s dynamics even within this 40,000-square-kilometer ecosystem. This spatial cascade reduced resilience in the core and was mediated by the movement of grazers, which reduced grass fuel and fires, weakened the capacity of soils to sequester nutrients and carbon, and decreased the responsiveness of primary production to rainfall. Similar effects in other protected ecosystems worldwide may require rethinking of natural resource management outside protected areas

Comparing Empirical with Perceived Trends in Wildlife, Livestock, Human Population and Settlement Numbers in Pastoral Systems: The Greater Maasai Mara Ecosystem, Kenya

Human activities are driving wildlife population declines worldwide. However, empirical understandings of their operation and consequences for wildlife populations and habitats are limited. We explored relationships between empirical and perceived wildlife and livestock population trends in Kenya using data on i) aerial monitoring of wildlife and livestock populations during 1977-2018, ii) human population censuses; and iii) semi-structured interviews with 338 male and female respondents from 250 households from four zones of the Greater Maasai Mara Ecosystem in 2019 and 2020. Wildlife numbers declined by 72.3% but sheep and goats increased by 306.4%. Yet nearly 50% of the interviewees perceived increases in wildlife numbers during 2011-2020 but concurrent decreases in livestock numbers because wildlife compete with livestock for resources. About one third of the respondents perceived an increase in the number of people living within conservancies and around the reserve and considered this indicative of a developing and thriving community. Notable discrepancies between the empirical and perceived trends were often more apparent than real and collectively suggest that incentives that promote wildlife are evidently viewed as less attractive than those that encourage increasing human and livestock numbers. Reconciling such apparent contradictions in empirical and perceived patterns is essential to extracting insights for formulating policies for sustaining livestock and wildlife populations and their habitats while promoting human welfare in grasslands

The relationship between total wildlife biomass (kg/km²) and human population density (people /km²), total livestock biomass (kg/km²), percentage of each county under protection (%), total annual rainfall (mm), annual average maximum and minimum temperatures (deg C).

<p>The relationship between total wildlife biomass (kg/km²) and human population density (people /km²), total livestock biomass (kg/km²), percentage of each county under protection (%), total annual rainfall (mm), annual average maximum and minimum temperatures (deg C).</p

Trends in warthog, lesser kudu, Thomson’s gazelle, eland, oryx, topi, hartebeest, impala, Grevy’s zebra and waterbuck numbers in the 21 Kenyan rangeland counties (“national” trends) between 1977 and 2016.

<p>Note that the data points do not refer to actual counts but to the sum of the counts in all the counties for the same year. If no survey was done in a county in a given year, then the missing count was predicted by the trend model for the county.</p

Percentage changes in numbers of sheep and goats, camels, donkeys, cattle, Burchell’s zebra, buffalo, elephant, ostrich, wildebeest, giraffe, gerenuk, Grant’s gazelle, warthog, Lesser kudu, Thomson’s gazelle and eland in each of the 21 rangeland counties between 1977–1980 and 2011–2016.

<p>Percentage changes in numbers of sheep and goats, camels, donkeys, cattle, Burchell’s zebra, buffalo, elephant, ostrich, wildebeest, giraffe, gerenuk, Grant’s gazelle, warthog, Lesser kudu, Thomson’s gazelle and eland in each of the 21 rangeland counties between 1977–1980 and 2011–2016.</p

The distribution of the proportion of the total biomass of warthog, lesser kudu, Thomson’s gazelle, eland, oryx, topi, hartebeest, impala, Grevy’s zebra and waterbuck among the 21 rangeland counties of Kenya during 1977–80 and 2011–2016.

<p>The distribution of the proportion of the total biomass of warthog, lesser kudu, Thomson’s gazelle, eland, oryx, topi, hartebeest, impala, Grevy’s zebra and waterbuck among the 21 rangeland counties of Kenya during 1977–80 and 2011–2016.</p

The distribution of the proportion of the total biomass of sheep and goats, camels, donkeys, cattle, Burchell’s zebra, buffalo, elephant, ostrich, wildebeest, giraffe, gerenuk and Grant’s gazelle among the 21 rangeland counties of Kenya during 1977–80 and 2011–2016.

<p>The distribution of the proportion of the total biomass of sheep and goats, camels, donkeys, cattle, Burchell’s zebra, buffalo, elephant, ostrich, wildebeest, giraffe, gerenuk and Grant’s gazelle among the 21 rangeland counties of Kenya during 1977–80 and 2011–2016.</p