Landscape-wide changes in land use and land cover correlate with, but rarely explain local biodiversity change

Context There is an ongoing debate whether local biodiversity is declining and what might drive this change. Changes in land use and land cover (LULC) are suspected to impact local biodiversity. However, there is little evidence for LULC changes beyond the local scale to affect biodiversity across multiple functional groups of species, thus limiting our understanding of the causes of biodiversity change. Objectives Here we investigate whether landscape-wide changes in LULC, defined as either trends in or abrupt changes in magnitude of photosynthetic activity, are driving bird diversity change. Methods Linking 34 year (1984–2017) time series at 2745 breeding bird survey (BBS) routes across the conterminous United States of America with remotely-sensed Landsat imagery, we assessed for each year what proportion of the landscape surrounding each BBS route changed in photosynthetic activity and tested whether such concomitant or preceding landscape-wide changes explained changes in bird diversity, quantified as relative abundance (geometric mean) and assemblage composition (Bray–Curtis index). Results We found that changes in relative abundance was negatively, and assemblage composition positively, correlated with changes in photosynthetic activity within the wider landscape. Furthermore, landscape-wide changes in LULC in preceding years explained on average more variation in bird diversity change than concomitant change. Overall, landscape-wide changes in LULC failed to explain most of the variation in bird diversity change for most BBS routes regardless whether differentiated by functional groups or ecoregions. Conclusions Our analyses highlight the influence of preceding and concomitant landscape-wide changes in LULC on biodiversity

Validating commonly used drought indicators in Kenya

Drought is a complex natural hazard that can occur in any climate and affect every aspect of society. To better prepare and mitigate the impacts of drought, various indicators can be applied to monitor and forecast its onset, intensity, and severity. Though widely used, little is known about the efficacy of these indicators which restricts their role in important decisions. Here, we provide the first validation of 11 commonly-used drought indicators by comparing them to pasture and browse condition data collected on the ground in Kenya. These ground-based data provide an absolute and relative assessment of the conditions, similar to some of the drought indicators. Focusing on grass and shrublands of the arid and semi-arid lands, we demonstrate there are strong relationships between ground-based pasture and browse conditions, and satellite-based drought indicators. The Soil Adjusted Vegetation Index (SAVI) has the best relationship, achieving a mean r2 score of 0.70 when fitted against absolute pasture condition. Similarly, the 3-month Vegetation Health Index (VHI3M) reached a mean r2 score of 0.62 when fitted against a relative pasture condition. In addition, we investigated the Kenya-wide drought onset threshold for the 3-month average Vegetation Condition Index (VCI3M; VCI3M<35), which is used by the country’s drought early warning system. Our results show large disparities in thresholds across different counties. Understanding these relationships and thresholds are integral to developing effective and efficient drought early warning systems (EWS). Our work offers evidence for the effectiveness of some of these indicators as well as practical thresholds for their use

Assessing drivers of intra-seasonal grassland dynamics in a Kenyan savannah using digital repeat photography

Understanding grassland dynamics and their relationship to weather and grazing is critical for pastoralists whose livelihoods depend on grassland productivity. Studies investigating the impacts of climate and human factors on inter-seasonal grassland dynamics have focused mostly on changes to vegetation structure. Yet, quantifying the impact of these on the inter-seasonal dynamics of specific grassland communities is not known. This study uses digital repeat photography to examine how intra-seasonal grassland dynamics of different grassland communities are affected by precipitation, temperature, and grazing in a heterogeneous semi-arid savannah in Kenya. A low-cost digital repeat camera network allowed for fine-scale temporal and spatial variability analysis of grassland dynamics and grazing intensity. Over all grass communities, our results show precipitation driving mainly early-season and in some cases mid-season flushing, temperature driving end-of-season senescence, and grazing influencing mid-season declines. Yet, our study quantifies how these three drivers do not uniformly impact grassland species communities. Specifically, Cynodon and Cynodon/Bothriochloa communities are rapidly and positively associated with precipitation, where mid-season declines in Cynodon communities are associated with grazing and late-season declines in Cynodon/Bothriochloa communities are associated with temperature increases. Setaria communities, on the other hand, have weaker associations with the drivers, with limited positive associations with precipitation and grazing. Kunthii/Digitaria diverse communities had no association with the three drivers. Highly diverse mixed communities were associated with increased precipitation and temperature, as well as lower intensity grazing. Our research sheds light on the complex interactions between plants, animals, and weather. Furthermore, this study also demonstrates the potential of digital repeated photography to inform about fine-scale spatial and temporal patterns of semi-arid grassland vegetation and grazing, with the goal of assisting in the formulations of management practises that better capture the intra-annual variability of highly heterogeneous dryland systems

Classification of grassland community types and palatable pastures in semi-arid savannah grasslands of Kenya using multispectral Sentinel-2 imagery

Semi-arid grassland ecosystems are crucial for biodiversity, carbon sequestration, and animal fodder; however, they are increasingly threatened by overgrazing degradation and climate variability. Understanding their spatial distribution and palatability is essential for sustainable land management and maintenance of pastoralist livelihoods. This study aimed to map grassland communities and assess their palatability in semi-arid Kenya using Multiple Endmember Spectral Mixture Analysis (MESMA) and Sentinel-2 satellite imagery, integrating species abundance with forage quality metrics. Sentinel-2 imagery was processed using MESMA to classify the fractional cover of four key grass species (Cynodon, Setaria, Themeda, and Kunthii) along with non-grass land cover types (bare ground, forests, shrubs, and water). An iterative endmember selection method optimized the classification, achieving a root mean square error (RMSE) of 23.5% and a 6% improvement in the overall accuracy compared to the unoptimized models. Palatability was assessed based on literature-derived chemical analyses and pastoralists’ perceptions of the forage quality. In the study area, medium and low-palatable species (Setaria and Kunthii) predominated lowland and midland areas, whereas highly palatable Cynodon was found in small, scattered areas across varied elevations. Mixed-grass communities were found in the central areas. The optimized MESMA model effectively identified overgrazed areas and areas vulnerable to degradation by observing grass palatability with grazing pressure from wildlife and livestock. The MESMA model utilized Sentinel-2 imagery and successfully characterized grassland communities’ spatial distribution and palatability in the study area. These findings provide actionable insights for sustainable grazing management and land protection, assisting pastoralists in identifying optimal grazing areas and enabling land managers to implement targeted restoration measures

Mapping Opuntia stricta in the arid and semi-arid environment of Kenya using sentinel-2 imagery and ensemble machine learning classifiers

Globally, grassland biomes form one of the largest terrestrial covers and present critical social–ecological benefits. In Kenya, Arid and Semi-arid Lands (ASAL) occupy 80% of the landscape and are critical for the livelihoods of millions of pastoralists. However, they have been invaded by Invasive Plant Species (IPS) thereby compromising their ecosystem functionality. Opuntia stricta, a well-known IPS, has invaded the ASAL in Kenya and poses a threat to pastoralism, leading to livestock mortality and land degradation. Thus, identification and detailed estimation of its cover is essential for drawing an effective management strategy. The study aimed at utilizing the Sentinel-2 multispectral sensor to detect Opuntia stricta in a heterogeneous ASAL in Laikipia County, using ensemble machine learning classifiers. To illustrate the potential of Sentinel-2, the detection of Opuntia stricta was based on only the spectral bands as well as in combination with vegetation and topographic indices using Extreme Gradient Boost (XGBoost) and Random Forest (RF) classifiers to detect the abundance. Study results showed that the overall accuracies of Sentinel 2 spectral bands were 80% and 84.4%, while that of combined spectral bands, vegetation, and topographic indices was 89.2% and 92.4% for XGBoost and RF classifiers, respectively. The inclusion of topographic indices that enhance characterization of biological processes, and vegetation indices that minimize the influence of soil and the effects of atmosphere, contributed by improving the accuracy of the classification. Qualitatively, Opuntia stricta spatially was found along river banks, flood plains, and near settlements but limited in forested areas. Our results demonstrated the potential of Sentinel-2 multispectral sensors to effectively detect and map Opuntia stricta in a complex heterogeneous ASAL, which can support conservation and rangeland management policies that aim to map and list threatened areas, and conserve the biodiversity and productivity of rangeland ecosystems

Are Kenya Meteorological Department heavy rainfall advisories useful for forecast-based early action and early preparedness for flooding?

Preparedness saves lives. Forecasts can help improve preparedness by triggering early actions as part of pre-defined protocols under the Forecast-based Financing (FbF) approach; however it is essential to understand the skill of a forecast before using it as a trigger. In order to support the development of early-action protocols over Kenya, we evaluate the 33 heavy rainfall advisories (HRAs) issued by the Kenya Meteorological Department (KMD) during 2015–2019. The majority of HRAs warn counties which subsequently receive heavy rainfall within the forecast window. We also find a significant improvement in the advisory ability to anticipate flood events over time, with particularly high levels of skill in recent years. For instance actions with a 2-week lifetime based on advisories issued in 2015 and 2016 would have failed to anticipate nearly all recorded flood events in that period, whilst actions in 2019 would have anticipated over 70 % of the instances of flooding at the county level. When compared against the most significant flood events over the period which led to significant loss of life, all three such periods during 2018 and 2019 were preceded by HRAs, and in these cases the advisories accurately warned the specific counties for which significant impacts were recorded. By contrast none of the four significant flooding events in 2015–2017 were preceded by advisories. This step change in skill may be due to developing forecaster experience with synoptic patterns associated with extremes as well as access to new dynamical prediction tools that specifically address extreme event probability; for example, KMD access to the UK Met Office Global Hazard Map was introduced at the end of 2017. Overall we find that KMD HRAs effectively warn of heavy rainfall and flooding and can be a vital source of information for early preparedness. However a lack of spatial detail on flood impacts and broad probability ranges limit their utility for systematic FbF approaches. We conclude with suggestions for making the HRAs more useful for FbF and outline the developing approach to flood forecasting in Kenya

Exploring synergies and trade-offs among the sustainable development goals: collective action and adaptive capacity in marginal mountainous areas of India

Global environmental change (GEC) threatens to undermine the sustainable development goals (SDGs). Smallholders in marginal mountainous areas (MMA) are particularly vulnerable due to precarious livelihoods in challenging environments. Acting collectively can enable and constrain the ability of smallholders to adapt to GEC. The objectives of this paper are: (i) identify collective actions in four MMA of the central Indian Himalaya Region, each with differing institutional contexts; (ii) assess the adaptive capacity of each village by measuring livelihood capital assets, diversity, and sustainable land management practices. Engaging with adaptive capacity and collective action literatures, we identify three broad approaches to adaptive capacity relating to the SDGs: natural hazard mitigation (SDG 13), social vulnerability (SDG 1, 2 and 5), and social–ecological resilience (SDG 15). We then develop a conceptual framework to understand the institutional context and identify SDG synergies and trade-offs. Adopting a mixed method approach, we analyse the relationships between collective action and the adaptive capacity of each village, the sites where apparent trade-offs and synergies among SDGs occur. Results illustrate each village has unique socio-environmental characteristics, implying distinct development challenges, vulnerabilities and adaptive capacities exist. Subsequently, specific SDG synergies and trade-offs occur even within MMA, and it is therefore crucial that institutions facilitate locally appropriate collective actions in order to achieve the SDGs. We suggest that co-production in the identification, prioritisation and potential solutions to the distinct challenges facing MMA can increase understandings of the specific dynamics and feedbacks necessary to achieve the SDGs in the context of GEC