Genomic prediction using machine learning: a comparison of the performance of regularized regression, ensemble, instance-based and deep learning methods on synthetic and empirical data.

BACKGROUND: The accurate prediction of genomic breeding values is central to genomic selection in both plant and animal breeding studies. Genomic prediction involves the use of thousands of molecular markers spanning the entire genome and therefore requires methods able to efficiently handle high dimensional data. Not surprisingly, machine learning methods are becoming widely advocated for and used in genomic prediction studies. These methods encompass different groups of supervised and unsupervised learning methods. Although several studies have compared the predictive performances of individual methods, studies comparing the predictive performance of different groups of methods are rare. However, such studies are crucial for identifying (i) groups of methods with superior genomic predictive performance and assessing (ii) the merits and demerits of such groups of methods relative to each other and to the established classical methods. Here, we comparatively evaluate the genomic predictive performance and informally assess the computational cost of several groups of supervised machine learning methods, specifically, regularized regression methods, deep, ensemble and instance-based learning algorithms, using one simulated animal breeding dataset and three empirical maize breeding datasets obtained from a commercial breeding program. RESULTS: Our results show that the relative predictive performance and computational expense of the groups of machine learning methods depend upon both the data and target traits and that for classical regularized methods, increasing model complexity can incur huge computational costs but does not necessarily always improve predictive accuracy. Thus, despite their greater complexity and computational burden, neither the adaptive nor the group regularized methods clearly improved upon the results of their simple regularized counterparts. This rules out selection of one procedure among machine learning methods for routine use in genomic prediction. The results also show that, because of their competitive predictive performance, computational efficiency, simplicity and therefore relatively few tuning parameters, the classical linear mixed model and regularized regression methods are likely to remain strong contenders for genomic prediction. CONCLUSIONS: The dependence of predictive performance and computational burden on target datasets and traits call for increasing investments in enhancing the computational efficiency of machine learning algorithms and computing resources.

Genome-based prediction of maize hybrid performance across genetic groups, testers, locations, and years.

KEY MESSAGE: The calibration data for genomic prediction should represent the full genetic spectrum of a breeding program. Data heterogeneity is minimized by connecting data sources through highly related test units. One of the major challenges of genome-enabled prediction in plant breeding lies in the optimum design of the population employed in model training. With highly interconnected breeding cycles staggered in time the choice of data for model training is not straightforward. We used cross-validation and independent validation to assess the performance of genome-based prediction within and across genetic groups, testers, locations, and years. The study comprised data for 1,073 and 857 doubled haploid lines evaluated as testcrosses in 2 years. Testcrosses were phenotyped for grain dry matter yield and content and genotyped with 56,110 single nucleotide polymorphism markers. Predictive abilities strongly depended on the relatedness of the doubled haploid lines from the estimation set with those on which prediction accuracy was assessed. For scenarios with strong population heterogeneity it was advantageous to perform predictions within a priori defined genetic groups until higher connectivity through related test units was achieved. Differences between group means had a strong effect on predictive abilities obtained with both cross-validation and independent validation. Predictive abilities across subsequent cycles of selection and years were only slightly reduced compared to predictive abilities obtained with cross-validation within the same year. We conclude that the optimum data set for model training in genome-enabled prediction should represent the full genetic and environmental spectrum of the respective breeding program. Data heterogeneity can be reduced by experimental designs that maximize the connectivity between data sources by common or highly related test units.

Bird species richness and diversity responses to land use change in the Lake Victoria Basin, Kenya.

The increasing demand for cultivated lands driven by human population growth, escalating consumption and activities, combined with the vast area of uncultivated land, highlight the pressing need to better understand the biodiversity conservation implications of land use change in Sub-Saharan Africa. Land use change alters natural wildlife habitats with fundamental consequences for biodiversity. Consequently, species richness and diversity typically decline as land use changes from natural to disturbed. We assess how richness and diversity of avian species, grouped into feeding guilds, responded to land use changes, primarily expansion of settlements and cultivation at three sites in the Lake Victoria Basin in western Kenya, following tsetse control interventions. Each site consisted of a matched pair of spatially adjacent natural/semi-natural and settled/cultivated landscapes. Significant changes occurred in bird species richness and diversity in the disturbed relative to the natural landscape. Disturbed areas had fewer guilds and all guilds in disturbed areas also occurred in natural areas. Guilds had significantly more species in natural than in disturbed areas. The insectivore/granivore and insectivore/wax feeder guilds occurred only in natural areas. Whilst species diversity was far lower, a few species of estrildid finches were more common in the disturbed landscapes and were often observed on the scrubby edges of modified habitats. In contrast, the natural and less disturbed wooded areas had relatively fewer estrildid species and were completely devoid of several other species. In aggregate, land use changes significantly reduced bird species richness and diversity on the disturbed landscapes regardless of their breeding range size or foraging style (migratory or non-migratory) and posed greater risks to non-migratory species. Accordingly, land use planning should integrate conservation principles that preserve salient habitat qualities required by different bird species, such as adequate patch size and habitat connectivity, conserve viable bird populations and restore degraded habitats to alleviate adverse impacts of land use change on avian species richness and diversity.

Introgression and disruption of migration routes have shaped the genetic integrity of wildebeest populations.

The blue wildebeest (Connochaetes taurinus) is a keystone species in savanna ecosystems from southern to eastern Africa, and is well known for its spectacular migrations and locally extreme abundance. In contrast, the black wildebeest (C. gnou) is endemic to southern Africa, barely escaped extinction in the 1900s and is feared to be in danger of genetic swamping from the blue wildebeest. Despite the ecological importance of the wildebeest, there is a lack of understanding of how its unique migratory ecology has affected its gene flow, genetic structure and phylogeography. Here, we analyze whole genomes from 121 blue and 22 black wildebeest across the genus' range. We find discrete genetic structure consistent with the morphologically defined subspecies. Unexpectedly, our analyses reveal no signs of recent interspecific admixture, but rather a late Pleistocene introgression of black wildebeest into the southern blue wildebeest populations. Finally, we find that migratory blue wildebeest populations exhibit a combination of long-range panmixia, higher genetic diversity and lower inbreeding levels compared to neighboring populations whose migration has recently been disrupted. These findings provide crucial insights into the evolutionary history of the wildebeest, and tangible genetic evidence for the negative effects of anthropogenic activities on highly migratory ungulates.

Evaluation of approaches for estimating the accuracy of genomic prediction in plant breeding.

BACKGROUND: In genomic prediction, an important measure of accuracy is the correlation between the predicted and the true breeding values. Direct computation of this quantity for real datasets is not possible, because the true breeding value is unknown. Instead, the correlation between the predicted breeding values and the observed phenotypic values, called predictive ability, is often computed. In order to indirectly estimate predictive accuracy, this latter correlation is usually divided by an estimate of the square root of heritability. In this study we use simulation to evaluate estimates of predictive accuracy for seven methods, four (1 to 4) of which use an estimate of heritability to divide predictive ability computed by cross-validation. Between them the seven methods cover balanced and unbalanced datasets as well as correlated and uncorrelated genotypes. We propose one new indirect method (4) and two direct methods (5 and 6) for estimating predictive accuracy and compare their performances and those of four other existing approaches (three indirect (1 to 3) and one direct (7)) with simulated true predictive accuracy as the benchmark and with each other. RESULTS: The size of the estimated genetic variance and hence heritability exerted the strongest influence on the variation in the estimated predictive accuracy. Increasing the number of genotypes considerably increases the time required to compute predictive accuracy by all the seven methods, most notably for the five methods that require cross-validation (Methods 1, 2, 3, 4 and 6). A new method that we propose (Method 5) and an existing method (Method 7) used in animal breeding programs were the fastest and gave the least biased, most precise and stable estimates of predictive accuracy. Of the methods that use cross-validation Methods 4 and 6 were often the best. CONCLUSIONS: The estimated genetic variance and the number of genotypes had the greatest influence on predictive accuracy. Methods 5 and 7 were the fastest and produced the least biased, the most precise, robust and stable estimates of predictive accuracy. These properties argue for routinely using Methods 5 and 7 to assess predictive accuracy in genomic selection studies.

Comparisons of single-stage and two-stage approaches to genomic selection.

Genomic selection (GS) is a method for predicting breeding values of plants or animals using many molecular markers that is commonly implemented in two stages. In plant breeding the first stage usually involves computation of adjusted means for genotypes which are then used to predict genomic breeding values in the second stage. We compared two classical stage-wise approaches, which either ignore or approximate correlations among the means by a diagonal matrix, and a new method, to a single-stage analysis for GS using ridge regression best linear unbiased prediction (RR-BLUP). The new stage-wise method rotates (orthogonalizes) the adjusted means from the first stage before submitting them to the second stage. This makes the errors approximately independently and identically normally distributed, which is a prerequisite for many procedures that are potentially useful for GS such as machine learning methods (e.g. boosting) and regularized regression methods (e.g. lasso). This is illustrated in this paper using componentwise boosting. The componentwise boosting method minimizes squared error loss using least squares and iteratively and automatically selects markers that are most predictive of genomic breeding values. Results are compared with those of RR-BLUP using fivefold cross-validation. The new stage-wise approach with rotated means was slightly more similar to the single-stage analysis than the classical two-stage approaches based on non-rotated means for two unbalanced datasets. This suggests that rotation is a worthwhile pre-processing step in GS for the two-stage approaches for unbalanced datasets. Moreover, the predictive accuracy of stage-wise RR-BLUP was higher (5.0-6.1%) than that of componentwise boosting.

Mapping brucellosis risk in Kenya and its implications for control strategies in sub-Saharan Africa.

In Sub-Saharan Africa (SSA), effective brucellosis control is limited, in part, by the lack of long-term commitments by governments to control the disease and the absence of reliable national human and livestock population-based data to inform policies. Therefore, we conducted a study to establish the national prevalence and develop a risk map for Brucella spp. in cattle to contribute to plans to eliminate the disease in Kenya by the year 2040. We randomly generated 268 geolocations and distributed them across Kenya, proportionate to the area of each of the five agroecological zones and the associated cattle population. Cattle herds closest to each selected geolocation were identified for sampling. Up to 25 cattle were sampled per geolocation and a semi-structured questionnaire was administered to their owners. We tested 6,593 cattle samples for Brucella immunoglobulin G (IgG) antibodies using an Enzyme-linked immunosorbent assay (ELISA). We assessed potential risk factors and performed spatial analyses and prevalence mapping using approximate Bayesian inference implemented via the integrated nested Laplace approximation (INLA) method. The national Brucella spp. prevalence was 6.8% (95% CI: 6.2-7.4%). Exposure levels varied significantly between agro-ecological zones, with a high of 8.5% in the very arid zone with the lowest agricultural potential relative to a low of 0.0% in the agro-alpine zone with the highest agricultural potential. Additionally, seroprevalence increased with herd size, and the odds of seropositivity were significantly higher for females and adult animals than for males or calves. Similarly, animals with a history of abortion, or with multiple reproductive syndromes had higher seropositivity than those without. At the herd level, the risk of Brucella spp. transmission was higher in larger herds, and herds with a history of reproductive problems such as abortion, giving birth to weak calves, or having swollen testes. Geographic localities with high Brucella seroprevalence occurred in northern, eastern, and southern regions of Kenya all primarily characterized by semi-arid or arid agro-ecological zones dominated by livestock pastoralism interspersed with vast areas with mixed livestock-wildlife systems. The large spatial extent of our survey provides compelling evidence for the widespread geographical distribution of brucellosis risk across Kenya in a manner easily understandable for policymakers. Our findings can provide a basis for risk-stratified pilot studies aiming to investigate the cost-effectiveness and efficacy of singular and combined preventive intervention strategies that seek to inform Kenya's Brucellosis Control Policy.

The distribution of large herbivore hotspots in relation to environmental and anthropogenic correlates in the Mara region of Kenya.

1. The distributions of large herbivores in protected areas and their surroundings are becoming increasingly restricted by changing land use, with adverse consequences for wildlife populations. 2. We analyse changes in distributions of herbivore hotspots to understand their environmental and anthropogenic correlates using 50 aerial surveys conducted at a spatial resolution of 5 × 5 km(2) (n = 289 cells) in the Mara region of Kenya during 1977-2010. We compare the distributions across seasons, land use types (protection, pastoralism and agro-pastoralism) and 10 species with different body sizes and feeding styles. 3. Small herbivores that are the most susceptible to predation and dependent on high-quality forage concentrate in the greenest and wet areas and close to rivers in Masai pastoral ranches in both seasons. Livestock grazing creates conditions favouring small herbivores in these ranches, including high-quality short grasses and better visibility, implying facilitation. But in the reserve, they concentrate in browner, drier and flatter areas and farther from rivers, suggesting facilitation by large grazers in the wet season, or little competition with migratory herbivores occupying the reserve in the dry season. 4. In the wet season, medium herbivores concentrate in similar areas to small herbivores in the ranches and reserve. However, in the dry season, they stay in the reserve, and also concentrate in green and wet areas close to rivers when migrants occur in the reserve. As such areas typically have higher predation risk, this suggests facilitation by the migrants by absorbing most predation pressure or, alternatively, competitive displacement by the migrants from preferred habitats. 5. Large herbivores, which suffer the least predation, depend on bulk forage and are the most likely to engender conflicts with people, concentrate in the reserve all year. This suggests attraction to the taller and denser grass and perceived greater safety in the reserve in both seasons. 6. These results reveal how predation risk, forage quantity and quality, water, competition with and facilitation by livestock interact with individual life-history traits, seasons and land use in shaping the dynamics of herbivore hotspots in protected and human-dominated savannas.

A stage-wise approach for the analysis of multi-environment trials.

Plant breeders and variety testing agencies routinely test candidate genotypes (crop varieties, lines, test hybrids) in multiple environments. Such multi-environment trials can be efficiently analysed by mixed models. A single-stage analysis models the entire observed data at the level of individual plots. This kind of analysis is usually considered as the gold standard. In practice, however, it is more convenient to use a two-stage approach, in which experiments are first analysed per environment, yielding adjusted means per genotype, which are then summarised across environments in the second stage. Stage-wise approaches suggested so far are approximate in that they cannot fully reproduce a single-stage analysis, except in very simple cases, because the variance-covariance matrix of adjusted means from individual environments needs to be approximated by a diagonal matrix. This paper proposes a fully efficient stage-wise method, which carries forward the full variance-covariance matrix of adjusted means from the individual environments to the analysis across the series of trials. Provided the variance components are known, this method can fully reproduce the results of a single-stage analysis. Computations are made efficient by a diagonalisation of the residual variance-covariance matrix, which necessitates a corresponding linear transformation of both the first-stage estimates (e.g. adjusted means and regression slopes for plot covariates) and the corresponding design matrices for fixed and random effects. We also exemplify the extension of the general approach to a three-stage analysis. The method is illustrated using two datasets, one real and the other simulated. The proposed approach has close connections with meta-analysis, where environments correspond to centres and genotypes to medical treatments. We therefore compare our theoretical results with recently published results from a meta-analysis.

Elevational distribution of montane Afrotropical butterflies is influenced by seasonality and habitat structure.

Determinants of elevational distribution of butterfly species richness and abundance in the tropics are poorly understood. Here we assess the combined effects, both additive and interactive, of seasonality and habitat structure on the elevational distribution of butterflies in the Uluguru Mountains, Tanzania. We sampled butterflies along a 1100 m elevational gradient that extended from 1540 to 2639 m using a time-constrained fixed-area method during the short to long rains and long to short rains transitions, and in habitat structure classified as closed or open. We used semi-parametric generalized linear mixed models to assess the relation between butterfly species richness or abundance, and seasonality, habitat structure, family and elevation. For all species combined, species richness declined with elevation in both open and closed habitats during the long to short rains transition. During the short to long rains transition, species richness displayed a mid-elevation peak across habitats. Among the three focal families (Nymphalidae, Papilionidae and Pieridae) similar patterns in the elevational distribution of species richness were observed. Species abundance declined or remained stable with elevation across seasons and habitat structure; the exception being species abundance in open habitat during the short to long rain transition and increased slightly with elevation. Abundance by family did not vary significantly by habitat structure or season. Our results indicate that seasonality and habitat structure shape species richness and abundance of butterflies along an elevational gradient in the Uluguru Mountains. These patterns are important for informing conservation actions because temperature as well as annual and seasonal variation in precipitation are predicted to increase in East Africa as a result of climate change, important determinants of seasonality, while habitat disturbance may increase due to a projected doubling in Tanzania's population over the next 27 years.

Livestock water and land productivity in Kenya and their implications for future resource use.

Population growth and rising affluence increase the demand for agricultural commodities. Associated growth in production increases dependency on natural resources in countries that attempt to meet part or all of the new demand locally. This study assesses the impact of changing meat and milk production on natural resource use in Kenya under three plausible scenarios of socio-economic development, namely Business-As-Usual (BAU), Sustainable Development (SDP) and Kenya Vision 2030 (V2030) scenarios. The IMPACT model is used to estimate projected cattle, sheep, goats and camel production parameters for meat and milk. The BAU and SDP represent standard scenarios for Kenya of a global economic model, IMPACT, while V2030 incorporates in the model features specific to Kenya's medium-term national development plan. We use calculations of water footprint and land footprint as resource use indicators to quantify the anticipated appropriation of water and land resources for meat and milk production and trade by 2040. Though camel dairy production numbers increase the most by quadrupling between 2005 and 2040, it is cattle dairy production that significantly determined gains in production between the scenarios. Productivity gains under the SDP scenario does not match the investments made thereby leading to only slightly better values for water and land productivity than those achieved under the BAU scenario. Relative to the BAU scenario, improvement in land productivity under the V2030 scenario is the most dramatic for shoat milk production in the arid and semi-arid systems but the least marked for cattle milk production in the humid system. By quantifying water and land productivity across heterogenous production systems, our findings can aid decision-makers in Kenya and other developing countries to understand the implications of strategies aimed at increasing domestic agricultural and livestock production on water and land resources both locally and through trade with other countries.

Corrigendum/Erratum to "Livestock water and land productivity in Kenya and their implications for future resource us" [].

[This corrects the article DOI: 10.1016/j.heliyon.2022.e09006.].

Forecasting the Potential Effects of Climate Change on Malaria in the Lake Victoria Basin Using Regionalized Climate Projections.

BACKGROUND: Malaria epidemics are increasing in East Africa since the 1980s, coincident with rising temperature and widening climate variability. A projected 1-3.5 °C rise in average global temperatures by 2100 could exacerbate the epidemics by modifying disease transmission thresholds. Future malaria scenarios for the Lake Victoria Basin (LVB) are quantified for projected climate scenarios spanning 2006-2100. METHODS: Regression relationships are established between historical (1995-2010) clinical malaria and anaemia cases and rainfall and temperature for four East African malaria hotspots. The vector autoregressive moving average processes model, VARMAX (p,q,s), is then used to forecast malaria and anaemia responses to rainfall and temperatures projected with an ensemble of eight General Circulation Models (GCMs) for climate change scenarios defined by three Representative Concentration Pathways (RCPs 2.6, 4.5 and 8.5). RESULTS: Maximum temperatures in the long rainy (March-May) and dry (June-September) seasons will likely increase by over 2.0 °C by 2070, relative to 1971-2000, under RCPs 4.5 and 8.5. Minimum temperatures (June-September) will likely increase by over 1.5-3.0 °C under RCPs 2.6, 4.5 and 8.5. The short rains (OND) will likely increase more than the long rains (MAM) by the 2050s and 2070s under RCPs 4.5 and 8.5. Historical malaria cases are positively and linearly related to the 3-6-month running means of monthly rainfall and maximum temperature. Marked variation characterizes the patterns projected for each of the three scenarios across the eight General Circulation Models, reaffirming the importance of using an ensemble of models for projections. CONCLUSIONS: The short rains (OND), wet season (MAM) temperatures and clinical malaria cases will likely increase in the Lake Victoria Basin. Climate change adaptation and mitigation strategies, including malaria control interventions could reduce the projected epidemics and cases. Interventions should reduce emerging risks, human vulnerability and environmental suitability for malaria transmission.

Long-term historical and projected herbivore population dynamics in Ngorongoro crater, Tanzania.

The Ngorongoro Crater is an intact caldera with an area of approximately 310 km2 located within the Ngorongoro Conservation Area (NCA) in northern Tanzania. It is known for the abundance and diversity of its wildlife and is a UNESCO World Heritage Site and an International Biosphere Reserve. Long term records (1963-2012) on herbivore populations, vegetation and rainfall made it possible to analyze historic and project future herbivore population dynamics. NCA was established as a multiple use area in 1959. In 1974 there was a perturbation in that resident Maasai and their livestock were removed from the Ngorongoro Crater. Thus, their pasture management that was a combination of livestock grazing and fire was also removed and 'burning' stopped being a regular occurrence until it was resumed in 2001 by NCA management. The Maasai pasture management would have selected for shorter grasses and more palatable species. Vegetation mapping in 1966-1967 recorded predominately short grasslands. Subsequent vegetation mapping in the crater in 1995 determined that the grassland structure had changed such that mid and tall grasses were dominant. After removal of the Maasai pastoralists from the Ngorongoro Crater in 1974, there were significant changes in population trends for some herbivore species. Buffalo, elephant and ostrich numbers increased significantly during 1974-2012. The zebra population was stable from 1963 to 2012 whereas population numbers of five species declined substantially between 1974 and 2012 relative to their peak numbers during 1974-1976. Grant's and Thomson's gazelles, eland, kongoni, and waterbuck (wet season only) declined significantly in the Crater in both seasons after 1974. In addition, some herbivore species were consistently more abundant inside the Crater during the wet than the dry season. This pattern was most evident for the large herbivore species requiring bulk forage, i.e., buffalo, eland, and elephant. Even with a change in grassland structure, total herbivore biomass remained relatively stable from 1963 to 2012, implying that the crater has a stable carrying capacity. Analyses of rainfall indicated that there was a persistent cycle of 4.83 years for the annual component. Herbivore population size was correlated with rainfall in both the wet and dry seasons. The relationships established between the time series of historic animal counts in the wet and dry seasons and lagged wet and dry season rainfall series were used to forecast the likely future trajectories of the wet and dry season population size for each species under three alternative climate change scenarios.

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.

Rainfall trends and variation in the Maasai Mara ecosystem and their implications for animal population and biodiversity dynamics.

Rainfall exerts a controlling influence on the availability and quality of vegetation and surface water for herbivores in African terrestrial ecosystems. We analyse temporal trends and variation in rainfall in the Maasai Mara ecosystem of East Africa and infer their implications for animal population and biodiversity dynamics. The data originated from 15 rain gauges in the Mara region (1965-2015) and one station in Narok Town (1913-2015), in Kenya's Narok County. This is the first comprehensive and most detailed analysis of changes in rainfall in the region of its kind. Our results do not support the current predictions of the International Panel of Climate Change (IPCC) of very likely increases of rainfall over parts of Eastern Africa. The dry season rainfall component increased during 1935-2015 but annual rainfall decreased during 1962-2015 in Narok Town. Monthly rainfall was more stable and higher in the Mara than in Narok Town, likely because the Mara lies closer to the high-precipitation areas along the shores of Lake Victoria. Predominantly deterministic and persistent inter-annual cycles and extremely stable seasonal rainfall oscillations characterize rainfall in the Mara and Narok regions. The frequency of severe droughts increased and floods intensified in the Mara but droughts became less frequent and less severe in Narok Town. The timings of extreme droughts and floods coincided with significant periodicity in rainfall oscillations, implicating strong influences of global atmospheric and oceanic circulation patterns on regional rainfall variability. These changing rainfall patterns have implications for animal population dynamics. The increase in dry season rainfall during 1935-2015 possibly counterbalanced the impacts of resource scarcity generated by the declining annual rainfall during 1965-2015 in Narok Town. However, the increasing rainfall extremes in the Mara can be expected to create conditions conducive to outbreaks of infectious animal diseases and reduced vegetation quality for herbivores, particularly when droughts and floods persist over multiple years. The more extreme wet season rainfall may also alter herbivore space use, including migration patterns.

Trade-offs for climate-resilient pastoral livelihoods in wildlife conservancies in the Mara ecosystem, Kenya.

Pastoralists in the wildlife-rich East African rangelands use diversification into conservation and tourism as a strategy to supplement livestock-based livelihoods and to spread risk. Tourism incomes are an important alternative source during drought, when livestock incomes decline. However, tourism may also reduce access to rangeland resources, and an abundant wildlife may destroy crops and injure, kill or transmit disease to livestock or people. This paper investigates the ability of wildlife conservancies in the Mara, Kenya, to act as an alternative for pastoralists that mitigates risks and maintains resilience in a changing climate. It analyses data to examine how conservancies contribute to and integrate with pastoral livelihoods, and to understand how pastoralists are managing their livestock herds in response to conservancies. It finds conservancy payments can provide an important, reliable, all-year-round source of income and prevent households from selling their animals during stress and for cash needs. Conservancies also retain grass banks during the dry season and provide opportunities for pastoralists to access good-quality forage. However, they reduce access to large areas of former grazing land and impose restrictions on livestock mobility. This affects the ability of pastoralists to remain flexible and able to access seasonally variable resources. Conflicts between grazing and conservancies may also heighten during drought times. Furthermore, income from land leases is not more than the contribution of livestock, meaning conservancy land leases create trade-offs for livestock-based livelihoods. Also, income is based on land ownership, which has inequity implications: women and other marginalised groups are left out.

Wildlife Population Dynamics in Human-Dominated Landscapes under Community-Based Conservation: The Example of Nakuru Wildlife Conservancy, Kenya.

Wildlife conservation is facing numerous and mounting challenges on private and communal lands in Africa, including in Kenya. We analyze the population dynamics of 44 common wildlife species in relation to rainfall variation in the Nakuru Wildlife Conservancy (NWC), located in the Nakuru-Naivasha region of Kenya, based on ground total counts carried out twice each year from March 1996 to May 2015. Rainfall in the region was quasi-periodic with cycle periods dependent on the rainfall component and varying from 2.8 years for the dry season to 10.9 years for the wet season. These oscillations are associated with frequent severe droughts and food scarcity for herbivores. The trends for the 44 wildlife species showed five general patterns during 1996-2015. 1) Steinbuck, bushbuck, hartebeest and greater kudu numbers declined persistently and significantly throughout 1996-2015 and thus merit the greatest conservation attention. 2) Klipspringer, mongoose, oribi, porcupine, cheetah, leopard, ostrich and Sykes monkey numbers also decreased noticeably but not significantly between 1996 and 2015. 3) Dik dik, eland, African hare, Jackal, duiker, hippo and Thomson's gazelle numbers first increased and then declined between 1996 and 2015 but only significantly for duiker and hippo. 4) Aardvark, serval cat, colobus monkey, bat-eared fox, reedbuck, hyena and baboon numbers first declined and then increased but only the increases in reedbuck and baboon numbers were significant. 5) Grant's gazelle, Grevy's zebra, lion, spring hare, Burchell's zebra, bushpig, white rhino, rock hyrax, topi, oryx, vervet monkey, guinea fowl, giraffe, and wildebeest numbers increased consistently between 1996 and 2015. The increase was significant only for rock hyrax, topi, vervet monkey, guinea fowl, giraffe and wildebeest. 6) Impala, buffalo, warthog, and waterbuck, numbers increased significantly and then seemed to level off between 1996 and 2015. The aggregate biomass of primates and carnivores increased overall whereas that of herbivores first increased from 1996 to 2006 and then levelled off thereafter. Aggregate herbivore biomass increased linearly with increasing cumulative wet season rainfall. The densities of the 30 most abundant species were either strongly positively or negatively correlated with cumulative past rainfall, most commonly with the early wet season component. The collaborative wildlife conservation and management initiatives undertaken on the mosaic of private, communal and public lands were thus associated with increase or no decrease in numbers of 32 and decrease in numbers of 12 of the 44 species. Despite the decline by some species, effective community-based conservation is central to the future of wildlife in the NWC and other rangelands of Kenya and beyond and is crucially dependent on the good will, effective engagement and collective action of local communities, working in partnerships with various organizations, which, in NWC, operated under the umbrella of the Nakuru Wildlife Forum.