The witchweed Striga gesnerioides and the cultivated cowpea: A geographical and historical analysis of their West African distribution points to the prevalence of agro-ecological factors and the parasite’s multilocal evolution potential

Abstract

The increasing severity of Striga gesnerioides attacks on cowpea across West Africa has been related to its prolificity, seed mobility and longevity, and adaptation to aridity, in a context of agricultural intensification. To understand this fast extension, we analyzed (1) the distributions of the crop and the witchweed with ecological niche modeling and multivariate climate analysis, and (2) the chronological information available from collections and the literature. The ecoclimatic envelope of S. gesnerioides attacks on cowpea is the same as on wild hosts. Consistently, the modeled distribution of cowpea infestations is closely similar to the simple superposition of the parasite model (involving all hosts) and the crop model. Striga gesnerioides infestations are restricted to the driest component of the cultivated cowpea ecoclimatic niche, corresponding to the Sahelian and Sudano-Sahelian belts and the Dahomey gap. Thus, the parasite distribution, determined by its own requirements, does not constrain cowpea cultivation under Guinean climates. The spatial and temporal distributions of S. gesnerioides field infestations are consistent with an earlier impact on cowpea production in eastern West Africa, related itself to a similar trend in cowpea cultivation intensification from Niger, Nigeria and Benin to Burkina Faso and Ghana. Mali and Senegal were affected later, and literature reports of Senegalese strains of S. gesnerioides from the wild developing virulence on cowpea offer a model for the diffusion of witchweed parasitism by multilocal evolution, through host-driven selection, instead of epidemic diffusion. A contrario, in Côte d’Ivoire, cowpea is much less widespread, so the parasite has remained confined to the wild compartment. Thus, both historical and ecogeographic analyses refute the vision of S. gesnerioides as an invader. Instead, they point to the increasing importance and intensification of the crop, and the consequent loss of biodiversity, as the main drivers of the extension and diversification of its crop-specific strains.

Habitat suitability and dispersal of invasive Striga species under climate change in Africa

“…This showed that the suitability of S. hermonthica in Africa is 49% higher than the other two species. Because Striga hermonthica has the ability to attain 50% germination under permanent wilting points, that is they can tolerate wide ranges of day/night temperatures ranging from 30 to 40°C (Sadda et al, 2021). This could be attributed to their genetic variances and various ecotypes specificity.…”

Ecological determinants and risk areas of Striga hermonthica infestation in western Kenya under changing climate

“…Studies have demonstrated that the spatial distribution of parasitic weeds can be predicted through ecological niche modelling (ENM) approaches using environmental variables as proxies for the prediction (Cotter et al, 2012;Sadda et al, 2021). The ENMs statistically correlate environmental variables with plant or other species observations by assessing the spatial variabilities (Elith et al, 2011).…”

“…There are several studies that have specifically used ENM tools to predict and map Striga infestation and suitable habitats in Africa (Cotter et al, 2012;Sadda et al, 2021). However, none of the studies has utilised ENMs to predict the spatial distribution and suitability of Striga hermonthica in Kenya.…”

“…The ENMs statistically correlate environmental variables with plant or other species observations by assessing the spatial variabilities (Elith et al, 2011). By using advanced ENM machine learning algorithms, such as maximum entropy (MaxEnt), the priority and buffer zones around high incidence areas of Striga that require urgent control operations can be identified over larger areas (Sadda et al, 2021). Predictive models of Striga occurrence could complement the field efforts and produce information that is more concise regarding areas that require priority attention during interventions (Cotter et al, 2012).…”