Desert locusts (Schistocerca gregaria) represent a major threat for agro-pastoral resources and food security over almost 30 million km2 from northern Africa to the Arabian peninsula and India. Given the differential food preferences of this insect pest and the extent and remoteness of the their distribution area, near-real-time remotely-sensed information on potential habitats support control operations by narrowing down field surveys to areas favorable for their development and prone to gregarization and outbreaks. The development of dynamic greenness maps, which detect the onset of photosynthetic vegetation, allowed national control centers to identify potential habitats to survey, as locusts prefer green and fresh vegetation. Their successful integration into the daily control operations led to a new need: the near-real-time identification of the onset of dryness, a synonym for the loss of habitat attractiveness, likely to be abandoned by locusts. The timely availability of this information would enable control centers to focus their surveys on areas more prone to gregarization, leading to more efficiency in the allocation of resources and in decision making. In this context, this work developed an original method to detect in near-real-time the onset of vegetation senescence. The design of the detection relies on the temporal behavior of two indices: the Normalized Difference Vegetation Index, depending on the green vegetation, and the Normalized Difference Tillage Index, sensitive to both green and dry vegetation. The method is demonstrated in Mauritania, an ever-affected country, with 10-day MODIS mean composites for the years 2010 and 2011. The discrimination performance of three classes (“growth”, “density reduction” and “drying”) were analyzed for three classification methods: maximum likelihood (61.4% of overall accuracy), decision tree (71.5%) and support vector machine (72.3%). The classification accuracy is heterogeneous in both time and space and is affected by several factors, such as vegetation density, the north-south climatic gradient and the relief. Smoothing the vegetation time series resulted in an increase of the overall accuracy of about 5% at the expense of a loss in timeliness of ten days. To simulate near-real-time monitoring conditions, the decision tree was applied to the decade of 2010. Overall, the seasonal vegetation cycle appeared clear and consistent. The results obtained pave the way for an operational implementation of the senescence dynamic mapping and, consequently, to further strengthen the capacity of the locust control management.

Desert locust swarms intermittently damage crops and pastures in sixty countries from Africa to western Asia, threatening the food security of 10% of the world’s population. During the 20th century, desert locust control operations began organizing, and nowadays, they are coordinated by the Food and Agriculture Organization (FAO), which promotes a preventative strategy based on early warning and rapid response. This strategy implies a constant monitoring of the populations and of the ecological conditions favorable to their development. Satellite remote sensing can provide a near real-time monitoring of these conditions at the continental scale. Thus, the desert locust control community needs a reliable detection of green vegetation in arid and semi-arid areas as an indicator of potential desert locust habitat. To meet this need, a colorimetric transformation has been developed on both SPOT-VEGETATION and MODIS data to produce dynamic greenness maps. After their integration in the daily locust control activities, this research aimed at assessing those dynamic greenness maps from the producers’ and the users’ points of view. Eight confusion matrices and Pareto boundaries were derived from high resolution reference maps representative of the temporal and spatial diversity of Mauritanian habitats. The dynamic greenness maps were found to be accurate in summer breeding areas (F-score = 0.64–0.87), but accuracy dropped in winter breeding areas (F-score = 0.28–0.40). Accuracy is related to landscape fragmentation (R2 = 0.9): the current spatial resolution remains too coarse to resolve complex fragmented patterns and accounts for a substantial (60%) part of the error. The exploitation of PROBA-V 100-m images at the finest resolution (100-m) would enhance by 20% the vegetation detection in fragmented habitat. A survey revealed that end-users are satisfied with the product and find it fit for monitoring, thanks to an intuitive interpretation, leading to more efficiency.

Significance: In animal societies, social life often generates male mating harassment. How do communal animals manage such conflicts without escalating antagonistic relationships? In the Sahara Desert, we studied the mating system of gregarious-behaving desert locusts, the world’s most destructive locust. Despite being widespread and abundant during plagues, its populations are otherwise difficult to access, and its reproductive behaviors in the field are understudied. We show that female locusts behaviorally overcome the costs of male mating harassment: females occupy separate sites before and after mating. Only females with ripe ovaries arrive at male-biased lekking groups. Hence, substantial social conflicts can be simply managed by behaviors. These findings invite to explore the evolutionary hypotheses behind lekking with a density-dependent plastic species.

The Desert locust Schistocerca gregaria is a major world pest that causes substantial agricultural and economic damage. Effective pest control relies on effective monitoring, which requires knowledge of locust microhabitat selection. Yet little is known about microhabitat selection of solitarious adult locusts in the field. We conducted field surveys to investigate fine-scale diel temporal and spatial distributions of solitarious adults in the Sahara Desert in Mauritania, a major breeding and recession area. We found that solitarious adults moved among different, specific microhabitats throughout the 24-h period in a cyclical manner. At night, they roosted in trees, moved to the ground to feed shortly after dawn, sheltered in low vegetation during the hot midday, and returned to the ground in the late afternoon. Hence, they switched microhabitats and plant species throughout each day. These cyclical daily movements among diverse microhabitats and specific plant species were correlated with time of day, light intensity, temperature, humidity, and specific plant species, and may relate to anti-predator defence, thermoregulation, and feeding. The present study suggests that locust monitoring should be adjusted, based on time of day, locust age, phase state and relative abundance of specific plant species. For example, we recommend surveying ground after morning and trees at night, for solitarious adults, when at low density.

Animals often aggregate at certain sites during vulnerable periods such as night-roosting as an anti-predatory strategy. Some migratory gregarious animals must regularly find new night-roosting sites, but how they synchronously choose such sites is poorly understood. We examined how gregarious nymphs of the desert locust, Schistocerca gregaria Forskål (Orthoptera: Acrididae), aggregate at certain plants for night-roosting in the Sahara Desert. Migratory bands of last instar nymphs climbed trees around dusk and roosted there overnight. A spatial autocorrelation analysis of plants indicated that the larger locust groups formed at the larger plants within the local plant community. Other large groups were not formed near the large tree, but smaller groups were patchily distributed. Plant height was the primary cue used by migratory bands to choose night-roosting plants. A nearest-neighbor distance analysis showed that single conspicuous large trees with scattered smaller plants were distributed locally. This plant community structure and negative geotactic ascending behavior of gregarious nymphs may force them to concentrate at the landmark plant from all directions and afar. This plant-size-dependent roosting site choice may contribute for developing artificial trapping systems for locusts and inciting to a new environment-friendly night control approach.