# How to Manage Migratory Pests and Potential Food Crises: Locusts Plagues in the 2020's

Michel Lecoq, Arianne Cease

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Michel Lecoq, Arianne Cease. How to Manage Migratory Pests and Potential Food Crises: Locusts Plagues in the 2020's. MDPI, 2022, 978-3-0365-5752-6. 10.3390/books978-3-0365-5751-9. hal-03965245

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agronomy

# How to Manage Migratory Pests and Potential Food Crises

## Locusts Plagues in the 2020's

Edited by

Michel Lecoq and Arianne Cease

Printed Edition of the Special Issue Published in Agronomy
How to Manage Migratory Pests and
Potential Food Crises: Locusts Plagues
in the 2020's
<figure><img src="image_3.png" /></figure>
How to Manage Migratory Pests and
Potential Food Crises: Locusts Plagues
in the 2020's

Editors

Michel Lecoq

Arianne Cease

MDPI • Basel • Beijing • Wuhan • Barcelona • Belgrade • Manchester • Tokyo • Cluj • Tianjin

<figure><img src="image_4.png" /></figure>
Editors

Michel Lecoq
CBGP
CIRAD
Montpellier
France

Arianne Cease
School of Sustainability and
School of Life Sciences
Arizona State University
Tempe
United States

Editorial Office

MDPI
St. Alban-Anlage 66
4052 Basel, Switzerland

This is a reprint of articles from the Special Issue published online in the open access journal *Agronomy* (ISSN 2073-4395) (available at: www.mdpi.com/journal/agronomy/special_issues/locusts_plague).

For citation purposes, cite each article independently as indicated on the article page online and as indicated below:

Last Name, A.A.; Last Name, B.B.; Last Name, C.C. Article Title. *Journal Name Year, Volume Number, Page Range*.

ISBN 978-3-0365-5752-6 (Hbk)

ISBN 978-3-0365-5751-9 (PDF)

Cover image courtesy of Michel Lecoq

© 2022 by the authors. Articles in this book are Open Access and distributed under the Creative Commons Attribution (CC BY) license, which allows users to download, copy and build upon published articles, as long as the author and publisher are properly credited, which ensures maximum dissemination and a wider impact of our publications.

The book as a whole is distributed by MDPI under the terms and conditions of the Creative Commons license CC BY-NC-ND.
# Contents

About the Editors vii

Preface to "How to Manage Migratory Pests and Potential Food Crises: Locusts Plagues in the 2020's" ix

Michel Lecoq and Arianne Cease

What Have We Learned after Millennia of Locust Invasions?
Reprinted from: *Agronomy* 2022, 12, 472, doi:10.3390/agronomy12020472 1

Michel Lecoq and Arianne J. Cease

Are Mycopesticides the Future of Locust Control?
Reprinted from: *Agronomy* 2022, 12, 2344, doi:10.3390/agronomy12102344 11

Koutaro Ould Maeno, Sidi Ould Ely, Sid'Ahmed Ould Mohamed, Mohamed El Hacen Jaavar and Mohamed Abdallahi Ould Babah Ebbe

Adult Desert Locust Swarms, *Schistocerca gregaria*, Preferentially Roost in the Tallest Plants at Any Given Site in the Sahara Desert
Reprinted from: *Agronomy* 2020, 10, 1923, doi:10.3390/agronomy10121923 13

Robert A. Cheke, Stephen Young, Xia Wang, Jamie A. Tratalos, Sanyi Tang and Keith Cressman

Evidence for a Causal Relationship between the Solar Cycle and Locust Abundance
Reprinted from: *Agronomy* 2020, 11, 69, doi:10.3390/agronomy11010069 31

Jun Liu, Michel Lecoq and Long Zhang

Desert Locust Stopped by Tibetan Highlands during the 2020 Upsurge
Reprinted from: *Agronomy* 2021, 11, 2287, doi:10.3390/agronomy11112287 53

Allan T. Showler, Mohammed Abdallahi Ould Babah Ebbe, Michel Lecoq and Koutaro O. Maeno

Early Intervention against Desert Locusts: Current Proactive Approach and the Prospect of Sustainable Outbreak Prevention
Reprinted from: *Agronomy* 2021, 11, 312, doi:10.3390/agronomy11020312 67

Allan T. Showler and Michel Lecoq

Incidence and Ramifications of Armed Conflict in Countries with Major Desert Locust Breeding Areas
Reprinted from: *Agronomy* 2021, 11, 114, doi:10.3390/agronomy11010114 83

Michael G. Sergeev

Ups and Downs of the Italian Locust (*Calliptamus italicus* L.) Populations in the Siberian Steppes: On the Horns of Dilemmas
Reprinted from: *Agronomy* 2021, 11, 746, doi:10.3390/agronomy11040746 107

Battal Çiplak

Locust and Grasshopper Outbreaks in the Near East: Review under Global Warming Context
Reprinted from: *Agronomy* 2021, 11, 111, doi:10.3390/agronomy11010111 129

Ludivina Barrientos-Lozano, Hojun Song, Aurora Y. Rocha-Sánchez and Jorge Ariel Torres-Castillo

State of the Art Management of the Central American Locust *Schistocerca piceifrons piceifrons* (Walker, 1870)
Reprinted from: *Agronomy* 2021, 11, 1024, doi:10.3390/agronomy11061024 147
# About the Editors

## Michel Lecoq

Dr. Michel Lecoq has been involved with pest locust population dynamics, ecology, surveillance and control during more than 50 years, mainly within the frame of CIRAD (Montpellier, France), where he was director of the research unit "Locust Ecology and Control" from 1997 to 2010. He has travelled extensively in tropical areas and worked in many countries affected by pest locusts. He contributes to the development of preventive control strategies for various species, including desert locust, migratory locust, red locust, Mato Grosso locust, and Senegalese grasshopper, aiming to better perceive the obstacles—mainly sociological and financial—to the implementation of effective controls. Michel Lecoq earned a PhD in entomology at Paris-Saclay University in 1975. His research activities have resulted in more than 110 peer-reviewed scientific publications, 60 books or book chapters, and numerous technical and consultation reports, as well as many popularization papers. Michel Lecoq was Consultant of the Food and Agriculture Organization of the United Nations on locust problems on numerous occasions; he served as president of the Orthopterists' Society from 2005 to 2009 and received the D.C.F. Rentz Award from the society in 2019 "in recognition of a lifetime dedicated to the study of Orthopteroid insects".

## Arianne Cease

Dr. Arianne Cease has been studying locusts and grasshoppers since she was first exposed to locust swarms as a Peace Corps Volunteer in Senegal in 2005. Her research involves transdisciplinary approaches to understanding how human-plant-insect interactions affect the sustainability of agricultural systems, including laboratory and field studies. She works with collaborative teams to study locust plagues in response to agricultural practices in West Africa, South America, Australia, and China. A key goal of her research is to improve sustainable ecosystem management and rural livelihoods. For this work, she was named in the Brilliant 10 by *Popular Science* magazine in 2015. Her work has been supported by the US National Science Foundation, Foundation for Food & Agriculture Research, US Agency for International Development, and the US Department of Agriculture. She is currently an Associate Professor at Arizona State University. In 2018, she became the Founding Director of the Global Locust Initiative, which she continues to co-direct with Dr. Rick Overson.
<figure><img src="image_5.png" /></figure>
Preface to "How to Manage Migratory Pests and Potential Food Crises: Locusts Plagues in the 2020's"

Locusts are a threat to agriculture and livelihoods in many countries globally. The economic, social, and environmental consequences of these highly migratory pests are substantial; they are treated as a national priority by many countries, and several international commissions have been established to unite efforts. Currently, one century after the revolutionary discovery of the phase phenomenon in locusts by Boris P. Uvarov, preventive strategies are increasingly being implemented as the most effective and economically viable control methods. They are based on the improved knowledge of locust biology and ecology, more efficient monitoring and control techniques, high-level technologies such as satellite imagery and geographic information systems, and the increased use of biological alternatives to chemical pesticides. As a result of the research efforts conducted over the past century, locust invasions are now better controlled and often shorter in duration and smaller in extent. Nevertheless, large-scale outbreaks persist, often arising unexpectedly. Considerable progress is still necessary in order to better understand locust behavior and key factors determining outbreaks, and to improve monitoring and forecasting methods. Critically, more focus is needed to understand the socio-political and economic aspects of locust governance to support sustained locust research and management capacity, as well as provide effective responses during a crisis. This book is a compilation of articles from a special issue of the *Agronomy* journal. It gathers the contributions of 54 authors from 19 countries in North and South America, Europe, Africa, Asia and Australia. It covers topics related to the main species of economic importance, in particular the Desert locust, Italian locust, Central American locust, Brown locust, Moroccan locust, and Migratory locust. It mobilizes diverse disciplines ranging from ecology, biology and biogeography to social sciences and political philosophy. Throughout the pages and articles, this book aims to shed light on some overarching questions: what have we learned from historical outbreaks; how serious is the threat; what research is ongoing and is needed to better manage these insects; how should the world respond to plagues today, especially in the context of climate change; are recommended preventive strategies really effective and what are the constraints to their application; and is there a possibility to make better use of biological alternatives to chemical pesticides? We hope that this book will contribute by highlighting recent research and management advancements, and stimulate new activities to improve management strategies for these dangerous pests that have plagued humanity for millennia.

Michel Lecoq and Arianne Cease
*Editors*
<figure><img src="image_6.png" /></figure>
Editorial

# What Have We Learned after Millennia of Locust Invasions?

Michel Lecoq <fnref n="1" />,<fnref n="2" />,* ID and Arianne Cease <fnref n="3" /> ID

**Abstract:** Locust outbreaks have long been a very serious problem for agriculture and livelihoods in many countries globally. This article is an introduction to a Special Issue of the journal Agronomy devoted to the management of these pests. Although not exhaustive, the nineteen articles herein cover a variety of species, many regions of the world and many aspects of pest locust management and research in the early 21st century. This book is a source of information and reflection, as well as a resource, to support new areas of investigation and practice contributing to the process of developing sustainable solutions for locust invasions.

**Keywords:** locust plagues; control; management; preventive strategy; locust biology; locust ecology

## 1. Introduction

Since time immemorial, pest locusts in their gregarious phase have been a very serious problem for agriculture and livelihoods of human populations in many countries around the world. The economic and social consequences of locusts are so substantial that these highly migratory pests are treated as a national priority by many countries, and several international commissions have been established to unite efforts. The start of 2020 was marked by the continued South American Locust upsurge—the first major upsurge of this species in 60 years, as well as the onset of a dangerous Desert Locust invasion extending from Kenya to India—the first many of these countries have seen in decades. This Special Issue aims to shed light on the following overarching questions: What have we learned from historical outbreaks? What research is ongoing? What action is needed? How serious is the threat? How should the world respond to plagues today?

Around twenty-five of the estimated 6700 Acrididae (Order: Orthoptera) species [1] are considered to be major pest locusts to varying degrees [2–6]. When populations of these insects increase under favorable ecological conditions, they acquire gregarious and migratory behavior, leading to the formation of spectacular swarms that can migrate vast distances and leave behind devastated crops and desolation (Figure 1). However, these insects are also a source of food for various human populations [7], and swarms certainly have beneficial but largely unknown ecological roles [8].

**Citation:** Lecoq, M.; Cease, A. What Have We Learned after Millennia of Locust Invasions? *Agronomy* **2022**, *12*, 472. https://doi.org/10.3390/agronomy12020472

Received: 29 January 2022

Accepted: 10 February 2022

Published: 14 February 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

<figure><img src="image_7.png" /></figure>

**Copyright:** © 2022 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).

<footnote n="1">¹ CIRAD, UMR CBGP, F-34398 Montpellier, France</footnote>

<footnote n="2">² CBGP, CIRAD, INRAE, IRD, SupAgro, University Montpellier, Montpellier, France</footnote>

<footnote n="3">³ School of Sustainability and School of Life Sciences, Arizona State University, Tempe, AZ 85287, USA;
acease@asu.edu</footnote>

<footnote>* Correspondence: mlecoq34@gmail.com</footnote>
<figure><img src="image_8.png" /></figure>

**Figure 1.** Example of locust swarms and hopper bands: the Migratory Locust, *Locusta migratoria*, in Madagascar (a,b) and the Mato Grosso Locust, *Rhammatocerus schistocercoides*, in Brazil (c,d) (photos M. Lecoq). The Migratory Locust swarm is approximately 5 × 2 km, measured from a helicopter, and it completely covers the landscape due to its high density. Its color varies according to the position of the observer in relation to the sun.

**2. History of Locusts and People**

Due to the extent of their devastation and the spectacular nature of their migratory swarms, locusts have left an indelible mark since ancient times in the minds, beliefs, literature and arts of many cultures all over the world [9,10]. The first mention of their damage—albeit conjectural—dates to Assyrian times, ca. 3200 BC, and was noted on a stone tablet in pre-cuneiform writing [11,12]. Since then, the evidence has multiplied, in numerous chronicles, books, religious texts, and testimonies of travelers, missionaries and naturalists throughout the ages [13–15]. Some cultures include attribution to locusts in the creation of the world and mastery of fire [16,17]. Locusts have also been used for political and military purposes [18–20].

Locust management has been ongoing for centuries, probably since the beginning of agriculture, as testified by ancient Egyptian, Chinese, Hebrew, Roman and Greek texts [9,21,22]. For example, China has 3000 years of recorded Migratory Locust outbreaks, with >800 outbreak episodes. The frequency of invasions led ancient emperors and their governments to impose treatment programs: full-time locust control officers were appointed in the Tang Dynasty in the eighth century [23]. Laws were also passed in the 11th, 12th and 15th centuries requiring local governors to conduct control, which included paying people to catch locusts and bury them in pits [23,24].

Over the centuries, more and more resources have been deployed on all continents. At the beginning of the 20th century, concerted international cooperation was developed (see, for example, [25–30]). Since then, research, surveillance and control efforts have steadily increased and been refined. In the present day, the Food and Agriculture Organization of the United Nations plays a centralized role in coordinating survey and control activities, strengthening national capacities and providing emergency assistance in response to locust upsurges and plagues [31–36].
### 3. 100 Years since Pivotal Phase Theory Published

This Special Issue coincides with the 100th anniversary of the phase theory Sir Boris Petrovitch Uvarov published in 1921 in a famous article [37–39]. The article demonstrated that locusts have two phases and that a single individual can shift phases in response to population density. The solitarious phase is prevalent at a low density, avoids conspecifics, does not form swarms and is typically a cryptic green or tan color. The gregarious phase is prevalent when favorable ecological conditions support high-density populations; it then aggregates and migrates *en masse*, and typically has a combination of dark and bright markings. Prior to this discovery, the two phases were thought to be different species. This article corresponds to a pivotal, founding moment, from which the phenomenon of locust invasions began to be better understood. Phase theory became the major paradigm within which locust research has been able to flourish and develop effectively for a century.

After Uvarov's discovery, research was aimed at locating the so-called “outbreak areas”—areas of origin of the outbreaks, which were previously unknown—and studying the ecological conditions that supported the development of gregarious populations in these areas. Additional focal areas over the century included efficient and environmentally safer control methods, the real-time monitoring of field populations, and general biology and ecology, with a particular focus on the mechanisms of phase change. Several recent articles have masterfully reviewed these various topics [36,40–42].

The result of this research and scientific and technical progress is that outbreaks have now become less frequent and smaller in scale. If they do occur, they are now controlled quicker, although the damage caused can still jeopardize the food security of rural populations in many Global South countries [36]. Recent outbreaks of Desert Locust in Africa and Asia [43–45] and the South American Locust *Schistocerca cancellata* [46,47] are indeed reminders that the problem remains, albeit to a lesser extent than in the past. Each resurgence is a surprise to many—most often through unawareness—that this ancient and spectacular problem is still around. Beyond the immense scientific and technical progress attested by the reviews mentioned above, many lessons have been learned over the century.

### 4. A Few Key Lessons

1. **The catastrophic impact of major locust outbreaks is a reality.** Despite denials and challenges in recent decades [48–50], the impact is real, devastating and documented, affecting not only crops but many aspects from family to national economics. Crop and pasture losses can lead to severe food shortages, large price fluctuations in markets, insufficient availability of grazing areas, the sale of animals at very low prices to meet household subsistence needs, high tensions between pastoralists and local farmers, and large human migrations to urban areas [36,51]. Impacts can reverberate for many years. Children born during plague years in impacted communities are less likely to ever start or to perform well in school [52,53]. Importantly, conventional techniques and simple cost-benefit ratios based on the market value of affected crops fail to capture true costs where >90% of crops are for subsistence. Furthermore, in the case of migratory pests such as locusts, the burden of control may fall on one region, while the benefits may be recorded completely elsewhere [54].

2. **Prevention strategies have been shown to be effective where they have been applied** [55–57]. Advancements in locust biology since Uvarov's 1921 article revealed how plagues develop from pockets of gregarious locust outbreaks. These advancements created opportunities for preventative management involving the regular monitoring of high-risk areas and early treatment, in contrast to reactive control once a crisis has evolved. While prevention has been considered an unattainable goal [58], delayed control interventions lead to a locust situation that is overwhelming and exponentially more difficult and costly to manage [59]. Preventive management strategies for locusts have thus been developed with dedicated organizations set up in many regions globally; however, the sustainability of these management systems remains an important concern [55,60].
(3) **Current prevention strategies may not be widely feasible** since they require con-
sistent resources and training and can be weakened by the erosion of infrastructure in the
time between outbreaks, insecurity and war; however, community partnerships can help. If
preventative management is successful, it inevitably leads to a vicious cycle with decreased
investment when the problem is perceived to be solved [60,61]. Outbreaks then occur un-
fettered when ecological conditions become favorable. Regional insecurity and conflict can
also preclude preventative management in key regions [62]. One aspect that has a clearer
solution is the better engagement of communities in locust-prone regions. Prevention plans
focused exclusively on the permanent surveillance of smaller outbreak areas that neglect
surrounding rural communities during quiet periods and fail to inform them about the ins
and outs of preventive control may prove to be largely counter-productive in the long term.
Management plans that are better able to include local communities and learn from their
traditional ecological knowledge through periods of calm as well as in the event of failure
of prevention may be more apt to maintain their effectiveness over many years.

(4) **During major outbreaks, there continues to be pressures requiring the massive use of fast-acting insecticides.** The various shortcomings of preventative strategies continue to result in the development of large-scale outbreaks that directly threaten crops, thus requiring the massive use of fast-acting insecticides. This situation regularly raises controversy about the quantities used and their harmfulness. It habitually calls into question all preventive control strategies, forgetting—most often due to lack of information—their largely positive aspects. However, there are still no synthetic chemical pesticide alternatives that are viable on a large scale. Some biopesticides have been developed, most notably, the fungus *Metarhizium acridum*. Though, perhaps apart from China (Zhang and Hunter p.c. in [40]), many logistical challenges remain [36]. A targeted treatment option with limited secondary environmental impacts that takes into account the many inherent logistical challenges could make great strides in improving sustainable locust management.

(5) **Climate change is likely to alter the probability, severity and location of out-breaks** [63–65]. However, the level of understanding of the magnitude and directionality of impacts and responses of various locust species to climate change remains low [66]. In fact, in view of the many uncertainties, there is an urgent need to coordinate research and monitoring actions on the topic of global change [67].

(6) **With each new major outbreak, a similar cycle of events repeats itself.** As a locust crisis hits international headlines, consistent themes arise. The initial response is concern for the lack of action and food security, followed by the questioning of control strategies, profitability of control and environmental impacts. Emphasis on safer techniques leads to a multitude of miracle solutions being proposed (e.g., massive locust vacuums). Articles in the media flourish, many opportunistic scientific publications are born, and declarations of good intentions multiply, including developing new research in various fields, training people, reforming institutions and reinforcing their resources. Each major crisis has its share of recommendations, always more or less the same, but between two major outbreaks, the locust problem is quickly considered to be solved, the memory of the problem and of past outbreaks fades, vigilance is reduced and qualified personnel retire or are fired, resulting in a devastating loss of knowledge. In this way, major outbreaks succeed one another, regularly followed by collective amnesia. In this sense, locust plagues are no exception in the list of calamities that have affected humanity for millennia. This can be likened to the myth of Sisyphus, the hero of Greek mythology. Everything appears to be an eternal and absurd restart [68].

(7) **Transdisciplinary approaches are critical.** The situation of the repetitive cycle has long been denounced [60,61,69]. Despite the immense improvements in monitoring and control methods, if outbreaks remain—albeit at a more modest level than in the past—it indicates it is no longer only a problem of limited biological and technical advancements. It is also, and above all, a question of removing the obstacles linked to the way we, as a global community, approach the problem, and of realizing that we have always forgotten major components: people, human behavior, decision-making processes, and the hazards
of any human action. A better integration of social sciences in the process of developing
sustainable solutions seems increasingly necessary, in addition to the more traditional
approaches, which are obviously indispensable [61]. There is a need to develop a more
comprehensive research, response, and resilience framework by engaging actors across
disciplines, sectors, cultures, and boundaries. Supporting a global network to enable
transdisciplinary approaches and continuity between outbreaks is part of the mission of
the Global Locust Initiative at Arizona State University.

**5. Summary of Articles Included in This Special Issue**

This Special Issue covers a variety of species, many regions of the world, and many
different aspects of locust management and research in the early 21st century. It discusses
the Desert Locust—a major pest and model species—but also the Central American Locust,
the Brown Locust in southern Africa, the South American Locust and the Italian Locust
in the Siberian steppes. The aspects discussed are diverse: ecology and behavior; control
strategies and methods; pesticides, their impact and substitutes; possible contributions
from social sciences to improve the management of locust outbreaks, and even the positive
aspects of locust invasions, both in terms of human nutrition and ecosystem health. Here,
we present briefly the various contributions that constitute this Special Issue.

First, concerning the behavioral aspects, Maeno et al. [70] studied the previously
poorly understood nocturnal behavior of swarms of the Desert Locust, *Schistocerca gregaria*.
This nocturnal behavior appears to be predictable and dependent on plant size, and the
authors propose that it can be used to facilitate locust swarm management and to adopt a
general strategy of nocturnal locust control.

Using time series of abundance indices for the Desert Locust and the Oriental Mi-
gratory Locust (*Locusta migratoria*), Cheke et al. [71] analyzed them independently and in
relation to measures of solar activity and ocean oscillation systems. The results suggest that
solar activity can be used to predict locust abundance, offering hope that information on
these phenomena might enable a better early forecasting of Desert Locust upsurges.

Liu et al. [72], for the first time, studied the behavior of Desert Locust swarms in
southern Tibet at very high altitudes of up to 5400 m. They show that low temperatures,
high humidity and low atmospheric oxygen put the locusts under severe stress and that the
Himalayan mountains provided an important natural barrier that limited the northward
expansion of Desert Locust populations.

Concerning management strategies, Showler et al. [59] discuss the three major ap-
proaches to Desert Locust population control: reaction, proaction and outbreak prevention.
They suggest that while research on the biology and behavior of this species is no longer
a highly urgent requirement for improving control efficacy, new research priorities have
emerged for developing epidemic prevention capability (and for improving proactive man-
agement). Salient needs presently include long residual tactics for prophylactic (preventive)
control in breeding areas; intervention thresholds; and improved, sustainable coordination
among stakeholders at national, regional and international levels. The most recent Desert
Locust episode of 2020 illustrates how prophylactic control could have prevented the
entire upsurge, and how proactive management in some countries contained the spread
of swarms.

Despite the high level of progress in recent years, Showler and Lecoq [62] show
that locust surveillance and control are impaired by many obstacles, perhaps the most
intractable of which is insecurity. After a historical analysis of insecurity in the various
Desert Locust range countries, the 2020 upsurge is used to show how direct insecurity still
contributes to the genesis and expansion of locust outbreaks. The possible mitigation of the
effects of direct insecurity on some Desert Locust operations is discussed.

Sergeev [73] covers the eco-geographic distribution and long-term dynamics of the
Italian Locust, *Calliptamus italicus*, in Asia, especially in Western Siberia, and analyzes their
consequences for the management of pest species and rare forms. He discusses issues
arising from insecticide experimental treatments, as well as ecological associations between
the Italian Locust and rare Orthoptera. Suggestions are provided regarding the need for the
improved monitoring, supervision, control and forecasting of Italian Locust distribution
and population dynamics.

Ciplak [74] provides a historical background for locust outbreaks in Southwest Asia (mainly Arabian Peninsula, Cyprus, Iraq, Israel, Jordan, Lebanon, Palestine, Syria and Turkey), assesses the potential for outbreaks of local species, and defines pathways for future actions, especially with regard to global change and agricultural expansion that may increase locust activities.

Barrientos et al. [75] present a review of the Central American Locust, *Schistocerca piceifrons piceifrons*, an important transboundary pest distributed from Mexico to Panama. The paper covers life history, habitat, ecology, permanent breeding areas, band and swarm formation, phase polyphenism, gregarization and migration. They provide a history of recent outbreaks and consider the current management strategy for this locust and recommended control measures, as well as current and future research.

In South Africa, the regular and often intense outbreaks of the Brown Locust, *Locustana pardalina*, are a formidable pest control problem. Price [76] discusses the operational constraints associated with the traditional ground control strategy in the Karoo outbreak area of this species. He makes recommendations for a modernized and technologically equipped integrated management strategy for the Brown Locust, combining ground and aerial tactics with the flexibility and capacity to effectively deal with outbreaks.

Trumper et al. [47] present a very interesting case of preventive management in the South American Locust, which has been successful for many years but has recently experienced a major resurgence. Their paper provides a review of this species’ biology, management history and perspectives on navigating a plague period after a 60-year recession.

Monitoring and control methods have made many advances in recent decades, but challenges remain. Matthews [77] discusses the difficulties of regularly monitoring locust populations, often in remote and unpopulated areas, and the recent contribution of new technologies, particularly drones and mycopesticides. Drones would facilitate more efficient surveys, determining where sprays need to be applied at an early stage, and minimize the risk of swarms developing and migrating to feed on large crop areas. They could also spray groups of hoppers and adults. The use of biopesticides, which have been available for many years, is encouraged due to their effectiveness and greater acceptability to the environment and non-target fauna.

Control operations are naturally a crucial tool for managing locust crises. Retkute et al. [78] use the example of the recent Desert Locust upsurge in 2019–2021 to analyze the nature of control operations conducted to contain the crisis. These treatments were used both for the immediate protection of crops and to reduce overall locust numbers. Without the latter, the upsurge might have lasted many years as they did in the first half of the 1900s.

The harmful effects of pesticides used in locust control are illustrated by Peveling's article [79]. His studies in Madagascar show a long-term decline of harvester termites following multiple barrier treatments with fipronil carried out to control an outbreak of the Migratory Locust in the late 1990s. The main outcome of this research is a strikingly low resilience of harvester termite populations, which did not recover within eight years, with likely repercussions on food webs. The environmental benefit of barrier treatments is lost if the same areas are treated repeatedly during the same campaign. Recommendations are given as to how to mitigate those risks.

As we have seen above, biopesticides appear to be a promising and readily available solution to control locusts, though there are currently logistical barriers to their widespread use. Abdelatti and Hartbauer [80] propose an alternative treatment whose main component is linseed oil. The authors describe the impact of this formulation on the aggregation behavior of Desert Locusts. Their results suggest that linseed oil may act as a bioactive agent capable of disrupting swarm formation.
Protozoa may also be used to control locust and grasshopper populations. Zhang and Lecoq [81] reviewed the role of *Nosema locustae*, a biological agent developed in the 1980s for locust and grasshopper control. The authors review the many studies that have focused on pathogenicity, the host spectrum, mass production, epizootiology, applications, genomics and molecular biology. This entomopathogen has many benefits: lower environmental impacts, vertical transmission to offspring through eggs and long-term persistence in populations of locusts and grasshoppers for more than 10 years, and it is well adapted to a variety of ecosystems. However, some limitations still need to be overcome.

While locusts have been at the heart of the natural sciences for over a century, the social sciences remain vastly under-represented. The potential contributions of the social sciences to improve the management of locust plagues are explored by Therville et al. [61]. Organizational, economic and cultural variables clearly have a considerable impact on the management strategies of these pests, and the social sciences are an important means to better understand these questions. This article examines the scope and purpose of different subfields of the social sciences and explores how they can be applied to different issues facing entomologists and practitioners to implement sustainable locust research and management.

In a similar vein, Lockwood and Sardo [82] use the Desert Locust as an iconic case from a humanities perspective. They provide a summary of standard moral theories and examine their shortcomings in developing a framework for understanding the socio-economic complexity of locust management. They address some of the models of global justice and focus on two fundamental questions: Who is a moral agent in Desert Locust management? How can responsibilities be equitably distributed between agents in preventive and reactive modes? After identifying the agents, they use a quadruple set of principles to construct a Desert Locust management framework consistent with global justice and apply this conceptual system to two hypothetical scenarios.

While major locust outbreaks exacerbate food insecurity, locusts can also be a food source for many populations. Samejo et al. [83] conducted a field study in India to investigate whether locust collection could be an attractive control method to protect crops in the event of an invasion, as well as an accepted food resource for poor rural communities. The authors conclude that locust consumption could be an effective practice to prevent malnutrition and protein deficiency and, to some extent, a mitigating measure to help communities better protect themselves and their crops from locust invasions. They argue that the collection and consumption of locusts should be encouraged while being realistic about their actual impact on locust control. They also advocate that this should be carried out in concert with local authorities to minimize risks to human health by avoiding the consumption of insects treated with pesticides.

Finally, Kietzka et al. [8] further explore the ecological and human food value of locusts. They note that while locusts have been considered since the Antiquity to be very serious pests of sedentary agriculture in many parts of the world—and as such combated with increasingly sophisticated means—they can be a nutritious food source for people and of great ecological importance as major components of ecosystem nutrient cycling. The authors estimated their potential benefits for human nutrition and ecosystem function using calculations based on a 1 km² area of swarming and breeding Desert Locusts, *Schistocerca gregaria*.

Although not fully comprehensive, this Special Issue covers most major current issues in locust management. We hope that it will be a source of information and reflection for many already working in the field, as well as serving as a resource to support new areas of investigation and practice to aid in the process of developing sustainable solutions for locust outbreaks.

**Conflicts of Interest:** The authors declare no conflict of interest.
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Editorial

# Are Mycopesticides the Future of Locust Control?

Michel Lecoq <fnref n="1" />,* and Arianne J. Cease <fnref n="2" /> ID

Locusts are a very serious problem for agriculture and for the livelihoods of populations around the world. Many lessons have been learned from decades of controlling these pests [1]. After the intensive use of chemical insecticides, new perspectives that are more respectful of people and the environment have emerged over the last twenty years. Protozoa, such as *Nosema locustae*, have been used and have many benefits; however, some limitations still need to be overcome [2]. Currently, entomopathogenic fungi are the most promising alternative, at least partially, to traditional chemical insecticides. The article from Wakil et al. [3] in this Special Issue dedicated to locust management is the first field study to test the efficacy of four different entomopathogenic fungal formulations together—*Metarhizium acridum* (Green Muscle® and Green Guard®), *Metarhizium anisopliae*, and *Beauveria bassiana*—against nymphs and adults of the desert locust *Schistocerca gregaria*. The study shows that all of these formulations have the potential to control locusts under field conditions. In addition, there are sublethal effects on their reproductive ability and behavior, with reductions in diet consumption, frass production, and weight observed. The authors conclude that future research needs to explore the combination of fungus with other control agents—including chemical insecticides, microsporidia, and botanical extracts—as a means of integrated pest management of locusts in the field.

Chemical pesticides have many side effects, and these have been increasingly elucidated, including their impact on human health, the environment, nontarget organisms, and biodiversity [4]. They have been shown to contribute to insect decline worldwide [5]. In addressing this issue, the use of biopesticides as a possible important component of locust management programs is a most significant recent development [6]. The benefits of using biopesticides include specificity to locusts and grasshoppers and the preservation of natural enemies [7]. The credibility of biopesticides as part of locust management programs is increasingly recognized, and programs that have included biopesticides have found them invaluable for treating locusts and grasshoppers wherever they occur [8]. As restrictions on the use of chemical pesticides rightly increase, treatment programs in the future will need to ensure their effectiveness by including biopesticides and by putting mechanisms in place to facilitate their use. Research and development supported by governments and donors will be critical in providing pathways to navigate logistical challenges such as manufacturing, storing, applications, and environmental limitations, especially the low temperature range over which locust mycopesticides are effective. It seems that we are at a turning point in locust control and that substitutes to chemical insecticides—notably mycopesticides—will become increasingly important in the future, not only in prevention—as originally thought—but also in periods of invasion, as shown by the mycopesticide-based treatments carried out during the last desert locust invasion in 2019–2020 [9–11]. *Metarhizium anisopliae* has already been listed for some years as one of the few products recommended by the Locust Pesticide Referee Group of the FAO to control the desert locust and any other locust species [12].

**Author Contributions:** M.L. wrote the first draft of the manuscript; A.J.C. reviewed and edited. All authors have read and agreed to the published version of the manuscript.

**Citation:** Lecoq, M.; Cease, A.J. Are Mycopesticides the Future of Locust Control? *Agronomy* **2022**, *12*, 2344.
https://doi.org/10.3390/agronomy12102344

Received: 15 July 2022
Accepted: 26 September 2022
Published: 28 September 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

<figure><img src="image_9.png" /></figure>

**Copyright:** © 2022 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

<footnote n="1">¹ CIRAD, UMR CBGP, F-34398 Montpellier, France</footnote>

<footnote n="2">² School of Sustainability and School of Life Sciences, Arizona State University, Tempe, AZ 85287, USA</footnote>

<footnote>* Correspondence: mlecoq34@gmail.com</footnote>
**Funding:** This research received no external funding.

**Conflicts of Interest:** The authors declare no conflict of interest.

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<figure><img src="image_10.png" /></figure>

Article

# Adult Desert Locust Swarms, *Schistocerca gregaria*, Preferentially Roost in the Tallest Plants at Any Given Site in the Sahara Desert

Koutaro Ould Maeno<sup>1,2,*</sup>, Sidi Ould Ely<sup>2,3</sup>, Sid’Ahmed Ould Mohamed<sup>2</sup>,<br/>Mohamed El Hacen Jaavar<sup>2</sup> and Mohamed Abdallahi Ould Babah Ebbe<sup>2,4 ID</sup>

Received: 23 October 2020; Accepted: 27 November 2020; Published: 7 December 2020

**Abstract:** The desert locust, *Schistocerca gregaria*, is a major migratory pest that causes substantial agricultural damage. Flying adult swarms disperse widely during the daytime, but they densely roost on plants at night. Swarm control operations are generally conducted during the daytime, but night-time control is a significant potential alternative. However, the night-roosting behavior of swarms is poorly understood. We determined night-roosting plant preferences of migrating sexually immature swarms of *S. gregaria* at four different sites in the Sahara Desert in Mauritania during winter. The night-roosting sites were divided into two types based on presence or absence of large trees. Swarms tended to roost on the largest trees and bushes at a given site. Swarms used medium-sized plants when large trees were not locally available, but the same medium-sized plant species were hardly used when large trees were available. Plant choice influenced roosting group size—large locust groups roosted on larger plants. Night-roosting locusts rarely fled from approaching observers. These results suggest that swarms of *S. gregaria* exhibit plasticity in their utilization patterns of night-roosting plants depending on the plant community encountered and they selectively use larger plants. We propose that this predictable plant-size dependent night-roosting can be used to particularly ease locust swarm control and to generally adopt anti-locust night control strategy.

**Keywords:** aggregation; density-dependent phase polyphenism; migration; night-roosting site choice; *Schistocerca gregaria*

## 1. Introduction

The desert locust, *Schistocerca gregaria* (Forskål, 1775), is one of the most destructive migratory pests in the world [1–3]. Locust outbreaks are irregular and can cause serious agricultural damage over a wide range across West Africa, the Middle East, and southwest Asia [4–8]. Understanding the biology and ecology of wild locusts can help improve locust management [9,10].

Locusts are polyphenic grasshopper species that show density-dependent phase polyphenism in behavioral, morphological, and physiological characteristics [11–13]. Solitarious locusts occurring at low densities are usually dispersed across the landscape, while gregarious locusts at high densities aggregate together and migrate over long distances as a group [14,15]. Nymphal and adult groups are traditionally called “bands” and “swarms”, respectively. Swarm sizes vary greatly and sometimes cover

<footnote n="1">1 Japan International Research Center for Agricultural Sciences (JIRCAS), Livestock and Environment Division, Ohwashi 1-1, Tsukuba, Ibaraki 305-8686, Japan</footnote>

<footnote n="2">2 Centre National de Lutte Antiacridienne (CNLA), Nouakchott 665, Mauritania; sidiouldely@yahoo.com (S.O.E.); sidmd2002@yahoo.fr (S.O.M.); mjaavar@yahoo.fr (M.E.H.J.); maouldbabah@yahoo.fr (M.A.O.B.E.)</footnote>

<footnote n="3">3 National Centre of Agricultural Research and Development (CNRADA), Kaedi, Mauritania</footnote>

<footnote n="4">4 Institute of Sahel, Bamako 1530, Mali</footnote>

<footnote>* Correspondence: kmaeno@affrc.go.jp; Tel.: +81-29-838-6622</footnote>
several hundred square kilometers [7,15]. Preventive control has been promoted, where the nymphal stages are treated before they reach adulthood and can fly as swarms to agricultural areas, because control of highly mobile, gigantic swarms is costly and ineffective [1,5,9,10,16]. Although desert locust management systems have been developed by the Food and Agriculture Organization of the United Nation (FAO), locust affected countries, and researchers by using chemical pesticides and biopesticides, swarms often occur at remote and inaccessible habitats due to geography and insecurity issues and then invade cultivated areas near human activity [17]. This may be one of the reasons why we still face desert locust problems in 2020 [18,19]. Therefore, improving control techniques and methods against swarms is important.

Surveys and control operations are usually conducted during the daytime and consider environmental factors such as wind, temperature, plant community, time of day, and locust behavior [20]. Swarms show various diel-cycling behavioral activities such as migratory flying, settling on the ground for basking, feeding, and resting during the daytime, and roosting on plants at night [15]. Aerial spraying using aircrafts is one of the few control techniques against flying swarms [21–23], but the flying locusts become hazardous to low-flying aircraft. When swarms settle on the ground, ground control measures can be used, which involve less pesticides than control measures against actively flying swarms [23]. However, the settling period is relatively short and irregular during the daytime [20,22]. Therefore, the efficacy of night-time control as an additional management strategy should be explored. As a first step, knowledge of night-roosting behaviors of swarms is essential.

Swarms roost for the night in trees or bushes. Locust densities in roosting swarms are much higher than that in migrating swarms [15]—the ratio between the area taken up by a swarm in flight and that by a swarm when settled can range from 12:1 to 900:1 [22]. Therefore, night-roosting swarms may be a suitable target for control. However, little is known about the night-roosting plant preference of swarms at a fine scale [24]. Our previous studies observed that both solitary adults and small-grouped gregarious adults selectively roosted on large trees [25,26]. Habitats of *S. gregaria* are diverse in terms of topography and plant communities, including vast plains with a variety of soil types such as rocky dry soils, dunes, and playas [27–29]. Some areas lack large trees and only have small bushes [30,31]. Highly mobile swarms of *S. gregaria* could encounter a variety of plant communities during migration [6,21,32], thus it is likely that they sometimes cannot reach favorable plant communities until night. However, they can access favorable plants by flying within the local areas. Accordingly, we hypothesized that migratory swarms of *S. gregaria* exhibit plasticity in their night-roosting plant utilization patterns depending on local plant communities and preferentially use relatively large plants. Previous studies have suggested that night-roosting plant choice influenced locust group size—gregarious nymphs selectively roosted on the largest plants and formed large groups [33]. Accordingly, we also tested the hypothesis that night-roosting swarms of *S. gregaria* form larger groups on larger plants.

Mauritania is a major breeding and recession area for the desert locust [27,30,34,35]. In 2013, swarms invaded the Banc d'Arguin National Park in Northwestern Mauritania, where plants are abundant and natural conditions are preserved. This ecological Park is nationally strictly protected from any chemical pesticides or alternative pest control strategies, only mechanical methods are allowed. We used this opportunity to test our hypotheses. We investigated the night-roosting plant preference and group formation of migratory swarms of *S. gregaria* at a fine scale. This work will contribute to developing better locust swarm survey and control methods.

## 2. Materials and Methods

### 2.1. *Study Area*

Mauritania, in West Africa, is an important area for desert locust outbreaks [27,30,34]. Our study sites were located in the Banc d'Arguin National Park, midway between Nouakchott and Nouadhibou, in north-western Mauritania. The area is a vast plain with a variety of soil types including rocky dry soils, dunes, playas, and small hills along the coast of the Atlantic Ocean. The primary plant
community of the survey sites were identified according to Duranton et al. [28]. Sparse, low-growing
desert annuals (grasses, herbs, vines, etc.) grow between the bushes and trees. We conducted field
surveys from December 22–25 and 29–31, during the winter of 2013, when we encountered migrating
sexually immature swarms. At this time, rain was rare and desert annuals were still partially green
but starting to dry. Sunrise and sunset occurred at about 07:30 and 18:30 h local time, respectively.
The mean temperature during the observation period (21:00–07:00) was 13.6 °C (SE: 0.2 °C, range:
8.6–19.5 °C) and the mean humidity was 44.4% (SE: 0.6%, range: 24.1–71.4%) at 50 cm above the
ground, measured using a thermo-hygro recorder (TR-72wf, ONDOTORI, Tokyo, Japan).

## 2.2. *Study Species*

We studied migratory swarms of sexually immature adult desert locusts, *Schistocerca gregaria*
(Forskål, 1775). Classical morphometric characteristics (hind femur length/head width) of locusts [36]
confirmed their gregarious-phase status. During our study, locusts moved and fed in a daily cyclical
rhythm—during the colder night and dawn period they roosted in trees and large bushes. After dawn
they warmed by solar basking, moved to the ground, and began alternatively feeding, migrating,
and resting, often on relatively bare ground. Near dusk, they flew or climbed into trees and large
bushes for nocturnal roosting. They mainly fed on low-growing annuals and used the bushes and trees
as refuges. Sexually mature gregarious locusts become yellow in body coloration [37]. Our population
were dark red in body coloration and no sexual behaviors were observed, indicating that our population
were sexually immature.

## 2.3. *Sampling Regime*

We followed migratory swarms encountered within the survey site without disturbing them until
they roosted at night (17:00–19:00). Field surveys were conducted at night (21:00–07:00). We established
25 × 2-m belt transects and recorded information on night-roosting plants (species and abundance)
and locust group size within each transect. We recorded the aggregation level of locusts on each bush
and tree based on visual estimation. At least 30 transects were surveyed during each sample period
and transects were separated by at least 10 m.

## 2.4. *Night-Roosting Plants*

Plants were patchily distributed at the study site. In our previous studies we measured all
the plants within transects to examine plant size and locust group levels [31,33], but this was time
consuming. In the present study, we determined the maximum length, width, and height of individuals
of the dominant plant species at four survey sites within a strait belt transect until obtaining enough
sample size, according to Maeno et al. [31]. At least 35 individuals or, whenever possible, all plants
were measured and all data were pooled and analyzed to calculate mean size. The volume (m³) of each
plant species was calculated as maximum length × width × height. The abundance of each plant species
was calculated from the transects (50 m²). Maeno and Ould Babah Ebbe [33] reported that maximum
plant height rather than width and volume was the most important factor for night-roosting site choice
by gregarious late instar nymphs. Accordingly, we categorized relative plant size based on mean plant
height: small, <1.0 m; medium, 1.0–2.0 m; large, >2.0 m. At Site 1, some trees *Euphorbia balsamifera* and
bushes *Nucularia perrini* had started to dry out. Therefore, we also recorded plant states (green vs. dry)
based on leaves from the two plant species to test whether green plants attracted more locusts and
formed larger groups than dry ones.

## 2.5. *Locust Group Size*

We estimated the number of adults roosting on each plant by directly counting them after 20:00
when locusts were inactive. Each plant was given a score based on the number of roosting locusts
(i.e., group size): 0 (0 locusts), 1 (<10), 2 (10–100), 3 (100–1000), 4 (1000–10,000) and 5 (>10,000),
following the estimation method described by Maeno et al. [38].
## 2.6. *Defensive Response*

To determine defensive behaviors of the swarms at night, their response to an approaching observer carrying a light was recorded according to modified methods by Maeno et al. [39]. We carried a low-intensity wide-angle headlight (Gentos, DPX-233H, Delta Peak) and walked within 1 m from roosting locusts, filming their behavior with a video camera (Panasonic, HC-V520, Tokyo, Japan). This was only done for locusts in groups of size 5 and was repeated for seven night-roosting groups (i.e., >70,000 individuals). This observation was conducted on 30 December 2013.

## 2.7. Statistical Analysis

The effect of plant species on the percentage of plants roosted by locusts were determined using a post hoc Fisher's exact test after Bonferroni correction. To analyze differences in plant size (maximum length, height, and volume) and abundance between different plant species, Tukey-Kramer HSD tests were conducted. Percentages of plants roosted by locusts were arc-sine transformed and analyzed using Tukey-Kramer HSD tests. Two-way analysis of variance (ANOVA) was used to analyze the effects of plant species and presence of trees on locust group size. Differences in plant sizes between green and dry plants were analyzed using *t*-tests. The proportions of different group sizes were analyzed using a $\chi^2$-test. Statistical analyses were conducted using the software package R, version 4.0.1 [40] and JMP (SAS Institute, Cary, NC, USA).

# 3. Results

## 3.1. Plant Characteristics

In the present study, only Site 4 had three large tree species (Figure 1a,b, Table 1). Large trees were relatively scarce (Tables 1 and 2: Tukey-Kramer HSD test, *p* < 0.05). Smaller plants were more abundant than trees (Tukey-Kramer HSD test, *p* < 0.05).

## 3.2. Daily Cyclical Movement and Night-Roosting Plant Preference

During our study, swarms migrated, fed, settled and roosted on plants in a daily cyclical rhythm. During the colder night and dawn periods, they roosted in relatively large plants including trees and bushes (Figure 1c-e). After dawn they displayed solar basking on the plants or ground by orientating perpendicular to the sun's rays to maximize the body surface (Figure 1f,g). They began alternating between feeding and resting, often on relatively bare ground (Figure 1h). At mid-day they usually migrated downstream (Figure 1i) and sometimes settled on the ground (Figure 1j). When swarms reached the coast, they avoided the ocean and returned inland. As a result, migrating locust density apparently increased (Figure 1k). Near dusk, they flew or climbed into trees and larger bushes for nocturnal roosting. No locusts were observed on the bare ground at night during any of the 150 belt surveys conducted, suggesting that locusts avoid habitats that lack plants. Locusts roosting on large trees rarely flew away from approaching observers with lights (i.e., when we tested their defensive response) (Figure 1l).

Plant utilization patterns by swarms varied. Figure 2 summarizes the percentage of each plant species roosted by locusts at our four survey sites. Large trees, such as *Capparis decidua*, *Acacia tortilis* and *Maerua crassifolia*, were relatively scarce, but most of them were roosted by locusts (post hoc Fisher's exact test after Bonferroni correction, *p* < 0.00091). More than 50% of *Euphorbia balsamifera* trees (medium-sized), *Panicum turgidum* bushes (medium-sized), and *Salsola imbricata* bushes (small-sized) were also utilized by locusts. The other small-sized plants (*Stipagrostis plumosa*, *Crotalaria saharae* and *Hyoscyamus muticus*) were rarely used, except for *Nucularia perrini*.
<table><caption>Table 1. Abundance of 11 dominant plant species in four survey sites. Mean (±SE) number of plants per belt transect (50 m²).</caption><thead><tr><th rowspan="2">Plant Species</th><th rowspan="2">Family</th><th rowspan="2">Types of Plant</th><th rowspan="2">Plant Form</th><th colspan="2">ID</th><th colspan="2">Site 2</th><th colspan="2">Site 3</th><th colspan="2">Site 4</th></tr><tr><th>Data Location</th><th>2013/12/22 19°55' N, 16°14' W</th><th>2013/12/23 19°53' N, 16°18' W</th><th>2013/12/24 19°53' N, 16°18' W</th><th>2013/12/29 19°24' N, 16°13' W</th></tr></thead><tbody><tr><td>Capparis decidua</td><td>Capparaceae</td><td>Perennial</td><td>Tree</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>0.2 ± 0.3 a</td></tr><tr><td>Acacia tortilis</td><td>Fabaceae</td><td>Perennial</td><td>Tree</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>0.2 ± 0.3 a</td></tr><tr><td>Maerua crassifolia</td><td>Capparaceae</td><td>Perennial</td><td>Tree</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>0.2 ± 0.3 a</td></tr><tr><td>Euphorbia balsamifera</td><td>Euphorbiaceae</td><td>Perennial</td><td>Tree</td><td>4.2 ± 0.9 b</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>1.2 ± 0.3 a</td></tr><tr><td>Calotropis procera</td><td>Poaceae</td><td>Perennial</td><td>Bush &amp; tree</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>0.2 ± 0.3 a</td></tr><tr><td>Panicum turgidum</td><td>Apocynaceae</td><td>Perennial</td><td>Bush</td><td>-</td><td>-</td><td>2.0 ± 0.9 a</td><td>6.6 ± 0.8 b</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Stipagrostis plumosa</td><td>Poaceae</td><td>Annual</td><td>Bush</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>6.8 ± 0.3 b</td></tr><tr><td>Salsola imbricata</td><td>Amaranthaceae</td><td>Annual</td><td>Bush</td><td>2.7 ± 0.9 ab</td><td>10.5 ± 0.9 b</td><td>3.7 ± 0.8 ab</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Nucularia perrini</td><td>Amaranthaceae</td><td>Perennial</td><td>Bush</td><td>17.0 ± 0.9 c</td><td>10.8 ± 0.9 b</td><td>6.3 ± 0.8 b</td><td>-</td><td>-</td><td>-</td><td>0.3 ± 0.3 a</td></tr><tr><td>Crotalaria saharae</td><td>Fabaceae</td><td>Perennial</td><td>Bush</td><td>-</td><td>2.0 ± 0.9 a</td><td>1.2 ± 0.8 a</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Hyoscyamus muticus</td><td>Solanaceae</td><td>Annual</td><td>Bush</td><td>0.7 ± 0.9 a</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td colspan="3">No. of transects</td><td></td><td>30</td><td>30</td><td>30</td><td>30</td><td>30</td><td>60</td><td></td></tr></tbody></table>

Different letters after values indicate significant differences within each survey site (Tukey-Kramer HSD test, *p* < 0.05). “-” indicates the absence of the plant species at the study site.
Night-roosting plant choice varied depending on plant community structure, i.e., the presence of relatively large trees (Figure 3). Our survey sites could be roughly divided into either areas with trees (Site 4) or without trees (Sites 1–3). For example, at Site 1, *E. balsamifera* trees, which are medium-sized, were locally the largest plants and most of them were roosted by locusts, while the percentage of *E. balsamifera* trees roosted by locusts was lower at Site 4. A similar tendency was also observed for *N. perrini* between Site 2 and 4. The relatively small-sized plant, *C. saharae*, was rarely used by locusts even at sites without trees. We could not analyze *H. muticus* due to a small sample size.

<figure><img src="image_11.png" /><figcaption>**Figure 1.** Habitats used by sexually immature swarms of *Schistocerca gregaria* for night-roosting sites and various diel behaviors in a population in Northwestern Mauritania during winter. (a) Habitats without trees and (b) with trees. (c) At night, locusts roosted on large trees (*Capparis decidua*), (d) medium-sized trees (*Euphorbia balsamifera*) and (e) small bushes (*Salsola imbricata*). (f) In the morning, locusts moved to the top of night-roosting trees or (g) to the open bare ground for basking. (h) Swarms feeding on low vegetation, (i) flying swarms, (j) settled swarms on the bare ground, (k) flying swarms along a coast, and (l) an observer staying near night-roosting swarms on a tree at night.</figcaption></figure>
<table><caption>**Table 2.** Mean (±SE) plant sizes of 11 dominant plant species in the four survey sites.</caption><thead><tr><th>Plant Species</th><th>Relative Size</th><th>Maximum Width (m)</th><th>Maximum Height (m)</th><th>Volume (m³)</th><th>n Plants Measured</th></tr></thead><tbody><tr><td>Capparis decidua</td><td>Large</td><td>9.34 ± 0.23 h</td><td>3.71 ± 0.08 h</td><td>331.5 ± 13.2 d</td><td>18</td></tr><tr><td>Acacia tortilis</td><td>Large</td><td>7.08 ± 0.20 g</td><td>2.84 ± 0.07 g</td><td>117.5 ± 11.2 c</td><td>25</td></tr><tr><td>Maerua crassifolia</td><td>Large</td><td>5.03 ± 0.25 f</td><td>2.29 ± 0.08 f</td><td>55.7 ± 14.0 b</td><td>16</td></tr><tr><td>Euphorbia balsamifera</td><td>Medium</td><td>2.20 ± 0.11 e</td><td>1.26 ± 0.04 e</td><td>5.9 ± 6.0 a</td><td>87</td></tr><tr><td>Panicum turgidum</td><td>Medium</td><td>2.10 ± 0.12 de</td><td>1.18 ± 0.04 e</td><td>4.9 ± 6.7 a</td><td>70</td></tr><tr><td>Calotropis procera</td><td>Medium</td><td>0.71 ± 0.26 abc</td><td>1.03 ± 0.09 de</td><td>0.6 ± 15.0 ab</td><td>14</td></tr><tr><td>Stipagrostis plumosa</td><td>Small</td><td>1.53 ± 0.17 cd</td><td>0.74 ± 0.06 dc</td><td>1.5 ± 9.5 ab</td><td>35</td></tr><tr><td>Salsola imbricata</td><td>Small</td><td>1.23 ± 0.09 bc</td><td>0.54 ± 0.03 bc</td><td>0.8 ± 5.3 a</td><td>110</td></tr><tr><td>Nucularia perrini</td><td>Small</td><td>1.00 ± 0.08 abc</td><td>0.43 ± 0.03 ab</td><td>0.5 ± 4.5 a</td><td>157</td></tr><tr><td>Crotalaria saharae</td><td>Small</td><td>0.76 ± 0.12 ab</td><td>0.32 ± 0.04 a</td><td>0.2 ± 6.6 a</td><td>71</td></tr><tr><td>Hyoscyamus muticus</td><td>Small</td><td>0.41 ± 0.22 a</td><td>0.34 ± 0.08 ab</td><td>0.1 ± 12.5 ab</td><td>20</td></tr></tbody></table>

Different letters after values indicate significant differences within each survey site (Tukey-Kramer HSD test, *p* < 0.05). Relative plant size was based on mean plant height: small, <1.0 m; medium, 1.0–2.0 m; large, >2.0 m.

<figure><img src="image_12.png" /><figcaption>**Figure 2.** Percentage of plants from 11 plant species roosted by swarms of *Schistocerca gregaria* at four survey sites at night. Numbers above bars indicate sample sizes. Different letters above bars indicate significant differences at *p* < 0.00091 (post hoc Fisher's exact test after Bonferroni correction).</figcaption></figure>
<figure><img src="image_13.png" /><figcaption>**Figure 3.** Mean percentage of plants from 11 plant species roosted by swarms of *Schistocerca gregaria* at night at four survey sites [Site 1: (a), Site 2: (b), Site 3: (c), and Site 4: (d)]. Each box plot displays the median value with the ends of the boxes representing the 25th and 75th percentiles and the ends of the lines representing the 10th and 90th percentiles. Crosses indicate mean values and open circles are outliers. Different letters above each bar indicate significant differences at *p* < 0.05 based on a Tukey-Kramer HSD test after arc-sine transformation. Numbers above bars indicate sample sizes (numbers of transects which certain plant species were recorded). “NO” in the figure indicates absence of the plant species at the study site.</figcaption></figure>
### 3.3. Group Size on Roosting Plants

At night, locust groups were rarely observed on the ground or on grass; they mainly aggregated on relatively large plants. Figure 4 summarizes the different group sizes observed for 11 plant species across the four survey sites. Group sizes ranged from size 0 to 5 for each plant species. Larger groups (>10,000) tended to be formed on large trees, and medium-sized groups (3, 100–1000 locusts; 4, 1000–10,000 locusts) on medium-sized trees and bushes, while large-sized group was not formed on small bushes. The 11 plant species differed significantly in the proportion of group sizes that roosted on them (Figure 4: $\chi^2 = 3861.814$, d.f. = 10, *p* < 0.01).

<figure><img src="image_14.png" /><figcaption>**Figure 4.** Percentage of plants roosted by each group size of *Schistocerca gregaria* swarms for the 11 dominant plant species at the four study sites. Numbers above bars indicate sample sizes (numbers of plants observed).</figcaption></figure>

Plant community structure also influenced group size (Figure 5). The largest groups (>10,000 locusts per single plant) were observed at Site 4, where there were trees, but not at Sites 1, 2 and 3.

Group sizes on night-roosting plants varied depending on local plant community structure. For example, the size of *E. balsamifera* plants did not differ significantly between Site 1 and 4 (Figure 6a; Wilcoxon rank sum test, *z* = -0.598, *p* > 0.05), but significantly more large locust groups were observed on this species in Site 1 than in Site 4 (Figure 6b: $\chi^2 = 121.170$, d.f. = 4, *p* < 0.001).
<figure><img src="image_15.png" /><figcaption>**Figure 5.** Percentage of plants roosted by each group size of *Schistocerca gregaria* swarms for the 11 dominant plant species at the four study sites [Site 1: (a), Site 2: (b), Site 3: (c), and Site 4: (d)]. Numbers above bars indicate sample sizes (numbers of plants observed). “NO” in the figures indicates absence of the plant species at the study site.</figcaption></figure>
<figure><img src="image_16.png" /><figcaption>**Figure 6.** Height of *Euphorbia balsamifera* plants (a) and the percentage of *E. balsamifera* plants roosted by each group size of *Schistocerca gregaria* swarms (b) at a site without trees (Site 1) and one with trees (Site 4). Each box plot displays the median value with the ends of the boxes representing the 25th and 75th percentiles and the ends of the lines representing the 10th and 90th percentile values. Crosses indicate mean values and open circles are outliers. N.S. indicates no significant differences at *p* > 0.05 according to a Wilcoxon rank sum test. Numbers in figures indicate sample sizes. ***, significant differences between the two groups (χ²-test, *p* < 0.001).</figcaption></figure>

3.4. *Plant Conditions: Green vs. Dry*

At Site 1, some *E. balsamifera* and *N. perrini* plants were still green but others were dry. We examined whether greenness of plants influenced night-roosting plant choice of swarms. The utilization of green and dry plants by swarms was compared for each species. Plant size did not differ between green and dry plants for either plant species (Wilcoxon rank sum test, *p* > 0.05 for both). For *E. balsamifera*, greenness did not affect roosting. Conversely, for *N. perrini*, green plants were roosted by locusts significantly more than dry plants (Figure 7a; Wilcoxon rank sum test, *z* = -3.573, *p* < 0.01). Plant greenness did not influence group size composition on *E. balsamifera* (Figure 7: χ² = 0.8731, d.f. = 4, *p* > 0.05). Conversely, for *N. perrini*, there were significantly more large locust groups on green plants than on dry plants (Figure 7: χ² = 24.5, d.f. = 4, *p* < 0.01).

<figure><img src="image_17.png" /><figcaption>**Figure 7.** Percentage of green and dry plants roosted by *Schistocerca gregaria* swarms for *Euphorbia balsamifera* and *Nucularia perrini* at Site 1 (a) and the percentage of each locust group size on each plant (b). Each box plot displays the median value with the ends of the boxes representing the 25th and 75th percentiles and the ends of the lines representing the 10th and 90th percentiles. Crosses indicate mean values and open circles are outliers. Numbers in figures indicate sample sizes. (a) **, *p* < 0.01; N.S. indicates no significant differences between the two groups at *p* > 0.05 according to a Wilcoxon rank sum test. (b) **, *p* < 0.01; N.S. indicates no significant differences between the two groups at *p* > 0.05 according to a χ²-test.</figcaption></figure>
# 4. Discussion

The present results support our hypothesis that migratory swarms of *S. gregaria* exhibit plasticity regarding their utilization patterns of night-roosting plants depending on the plant community encountered and they selectively use larger plants. Furthermore, we found that their night-roosting plant choice influenced locust group size—larger groups were formed on larger trees, and night-roosting locusts rarely fled from approaching observers. This knowledge not only aids our understanding of night-roosting behaviors of swarms, but is also important for effective and easier locust control by night. In the following, we will discuss night-roosting plant preference and group formation by swarms of *S. gregaria* and how to apply this knowledge for improving locust control operations.

## 4.1. Night-Roosting Plant Preference

Many grasshopper species are known to roost on plants overnight [15]. In *S. gregaria*, swarms are known to roost on large trees or bushes at night [15,22,24]. The present study confirmed this trend for sexually immature swarms in Northwestern Mauritania during a winter season. Further, we found plasticity in their night-roosting plant utilization—they preferentially used relatively larger plants within local plant communities, but they used medium-sized trees and bushes as refuges when large trees were not available. For example, swarms were observed on most of the *E. balsamifera* trees, which are medium-sized, at Site 1, where this plant species was locally the largest. Conversely, the percentage of *E. balsamifera* trees roosted by locusts at Site 4 was less than half that at Site 1, as large trees were present in Site 4. Height of *E. balsamifera* trees was not significantly different between the two sites, thus swarms might use relative plant size to select night-roosting plants. Swarms also occupied medium-sized bushes at Site 2 and 3 where large trees were not available. To our best knowledge, we are the first to report that swarms of *S. gregaria* change their night-roosting plant preference depending on the local plant community, preferentially using the largest plants within the local area. We have previously observed that marching gregarious nymphs of *S. gregaria* also selectively roosted on the largest trees in the local plant community and plant height rather than width and volume was the primary cue for night-roosting plant choice [33]. Solitarious adults and gregarious adults at relatively low densities, and even solitarious nymphs, used relatively larger plants as their night-roosting sites [25,26,31]. These results suggest that *S. gregaria* tend to use relatively larger night-roosting plants irrespective of population density, developmental stage, and phase.

Plant height seems to be a useful criterion not only for ground-dwelling locust nymphs but also for flying adults, likely because they can visually recognize tall plants from all directions and they are comparable and available in all locust habitats. In fact, plant height is frequently used by insects as a cue for selecting aggregation sites, known as a “hill-topping” behavior [41]. At Site 3 where the ground was almost flat and there were no large trees, our car (TOYOTA, Land Cruiser, 5 m × 2 m × 2 m) was the largest object at the site. We observed that some flying locusts tried to land on our car around dusk (18:20–18:50). This observation also suggested that locusts preferred relatively large plants (or objects) within the local area and provided a hint for developing an artificial trapping system which can be used as an environmentally friendly control option. The simple criterion of plant height seems to be useful for migratory locusts as they encounter various habitats characterized by different vegetation cover, plant species, and plant sizes [24,27,28].

Plants are frequently used as a refuge by prey animals [42]. Greenness, i.e., the amount of leaves on refuge plants, may influence detectability by predators [42]. In the present survey sites where trees were not available, greenness of medium-sized plants such as *E. balsamifera* did not influence night-roosting site choice by swarms of *S. gregaria*, but more green plants of small-sized plants (*N. perrini*) were used than dry ones. Plant size of *N. perrini* did not differ depending on greenness, thus this may suggest that greenness is an important factor for small-sized plants. These plants are mainly used as a shelter rather than as food during a night. However, larger dry *E. balsamifera* plants attracted more locusts than green *N. perrini* plants, thus attraction of greenness could be masked by plant size. Although plant height was almost similar between the small bushes *S. plumosa* and *S. imbricata*, the former were
rarely used by locusts. The stem and leaves of *S. plumosa* are thin and soft, thus they seem to have an
unstable structure. This observation suggests that morphological characteristics of plants could be
another factor for night-roosting plant choice.

Anti-predatory strategies are usually associated with microhabitat selection [42]. In the present survey area, ambient temperature can fall below 20 °C at night. Because escaping performance is temperature-dependent in grasshoppers [43,44], adult locusts cannot escape quickly from approaching predators below 20 °C [39]. This is most likely why locusts roost in large trees and bushes at night, away from nocturnal ground-foraging mammals. Morphological structures of medium-sized *P. turgidum* bushes and small-sized *N. perrini* bushes, which locusts used in the present study, were complex and apparently prevented access of relatively large predators. Some smaller plants such as *S. plumosa* and *C. saharae* had enough space for many locusts to roost, but the percentages used by swarm were small, probably because they did not provide shelter. Furthermore, aggregation with conspecifics could reduce the risk of predation through a dilution effect, or the "selfish herd effect" [45], as suggested for lubber grasshoppers, *Romelea guttata* [46], and Mormon crickets, *Anabrus simplex* [47]. Therefore, using refuges and grouping on the night-roosting plants might jointly serve as an anti-predator strategy. Interestingly, night-roosting locusts sometimes responded to an approaching observer by dropping and moving their hind legs, but they usually remained in place. It remains unclear how darkness (i.e., intensity of moon light) and low temperature influence escaping behaviors of night-roosting locusts, but this reduction of escaping performance may be useful, because sprayers can closely approach swarms.

## 4.2. Local Locust Group Size on Night-Roosting Plants

The present results supported our hypothesis that migratory swarms of *S. gregaria* would form larger groups on larger night-roosting plants. We found that larger locust groups (>10,000 individuals) roosted on large trees (>2 m tall), while medium- and smaller-sized locust groups (10–10,000 individuals) roosted on medium- and small-sized trees and bushes. This variation cannot be explained by plant size as a capacity of roosting space alone. Of course, large trees have more roosting space than small bushes, but the presence of large trees influenced the locust group size on other roosting plants. Locust group size on medium-sized plants significantly decreased when large trees were nearby, as observed for the medium-sized tree *E. balsamifera* at Site 1 and 4. This suggests that larger trees attracted more locusts from surrounding smaller plants. This spatial pattern was similar to that observed in gregarious late instar nymphs of *S. gregaria*—marching bands formed one large group on the largest plant with several smaller groups scattered at the local level [33]. This trend could be associated with plant distribution patterns and sizes in the semi-arid area. In the desert plant community, resource competition for water often results in heterogeneous patchy distributions [48], as was observed in the present survey area. The largest trees in a local area were conspicuous, because no other large plants existed near them. This may allow swarming locusts to see the roosting tree from afar without the need to evaluate other potential roosting plants.

In *S. gregaria*, it has been reported that heterogeneous plant distributions promote local crowding leading to gregarization, whereas a uniform distribution promotes scattering [29,30,49–53]. Cisse et al. [29] reported that vegetation cover and greenness influenced the threshold of gregarization in adults—low vegetation cover and dry vegetation led to a low density threshold of gregarization. Drying or dry vegetation forces locusts to meet on the few usable resources of a given area. Ould Babah and Sword [30] showed that specific plant communities promoted gregarization in nymphs. The present results suggest that not only plant distribution, communities, greenness, and cover, but also relative plant size, particularly the presence of large trees, and plant community structure played an important role in aggregation and local locust density.

Physical contact with conspecifics is the primary factor inducing gregarization in locusts [54–57], but flying locusts stay at a distance from conspecifics [15]. This raises the questions of when and where swarms aggregate and maintain gregariousness during migration. Microhabitat preferences
are critical to recruit scattered conspecifics to a particular site [58], and individual attraction, group basking behavior and microhabitat preferences jointly lead conspecifics to concentrate within a limited area [49]. In *S. gregaria*, some scattered swarming members were also attracted to the large roosting trees, suggesting that scattered swarms can fuse to form an aggregation via night-roosting behaviors. Local crowding in the habitat has been observed on discrete resources such as host plants, basking, or shelter sites [49,59]. It is reasonable to assume that night-roosting plant preference of gregarious locusts may concentrate them in limited areas where they would receive physical contact while roosting and ground basking, leading to the maintenance of gregariousness.

### 4.3. Applications and Future Directions

The present study suggests that future locust control methods could also target night-roosting swarms for the following reasons: (1) night-roosting was a regular event and predictable, (2) locusts densely aggregated on large plants, which were patchily distributed, instead of on the ground or in the air, (3) roosting locusts were relatively immobile and escaping performance was low, thus sprayers could approach roosting locusts even using lights, (4) large trees or bushes are easily located in the habitat. Conversely, it may be difficult to conduct aerial spraying of night-roosting locusts due to the following: (1) a large proportion of the locusts were often inside trees and so would be sheltered if spraying was done from the air, (2) locusts roosted so densely that they would shield one another from aerial spraying, and (3) night-roosting locust swarms are not easily visible from aircraft in addition to the spatial dispersion of trees which make full coverage spraying not rational. To overcome these disadvantages, we suggest the following: (a) ground survey teams should follow migratory swarms until roosting time to determine roosting areas, (b) spraying should be done from the ground by car or by man with backsprayers, (c) sprayers should target night-roosting locusts and control them directly. (d) an eventual trapping system based on high similar object of the trees could be created to attract swarm before they roost, (e) although night-time control operations have been poorly documented, we are planning to test the efficacy of spraying from the ground at night. In 2013, the Mauritanian National Anti-locust centre (CNLA) have collected adults in bags by hands in the early morning within the natural park.

In addition, although the present study did not show any data about locust density of migratory swarms, their density was apparently high when they migrated along the coast. Migratory swarms avoid the polarization of oceans [60], thus members of swarms could be locally concentrated due to such a natural barrier. Therefore, remote sensing techniques and historical survey data could be used in the future to predict night-roosting areas [27,34,61–63]. As suggested by multi-agent models [19,64], the spatial conditions and accessibility of locusts should be considered to improve preventive control system.

In the future, if we can develop techniques to repel and attract swarms, a combination of these two techniques can concentrate swarms in a limited area. At the present, unfortunately, we have not developed such techniques, but integrating various information such as night-roosting site preference, natural barriers such as water, and meteorological and topographical information can help to inform which areas are suitable for extensive control operations. The present study identified factors potentially influencing locust group size on night-roosting plants such as site, plant type, plant conditions (green/dry), plant relative size, plant abundance and plant community. We should determine major parameters by using appropriate statistical models. It will be necessary to repeat this exercise with data from other countries and seasons in order to broaden our understanding.

Local people and field workers have empirically known that swarms roost on large trees at night, but this has yet to be sufficiently reported in the scientific literature. It is important to share field and local observations, not only to inspire scientists but also to improve control methods.

**Author Contributions:** Conceptualization, formal analysis, and investigation, K.O.M., S.O.E., S.O.M., M.E.H.J. and M.A.O.B.E. organized field surveys; original draft preparation, K.O.M. All authors have read and agreed to the published version of the manuscript.
**Funding:** This study was funded by the Ministry of Agriculture, Forestry and Fisheries (MAFF) of Japan through the Japan-CGIAR Fellowship Program implemented by the Japan International Research Center for Agricultural Sciences (JIRCAS).

**Acknowledgments:** We thank Ahmed Salem Bennahi, Cheikh Tidjani Babe Taleb and members of the Centre National de Lutte Antiacridienne for their assistance with the field survey. We are grateful to Editage (www. editage.com) for English language editing. We greatly appreciate the constructive and valuable comments on the manuscript from two anonymous reviewers.

**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

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Article

# Evidence for a Causal Relationship between the Solar Cycle and Locust Abundance

Robert A. Cheke <fnref n="1" />,*, Stephen Young <fnref n="1" />, Xia Wang <fnref n="2" />, Jamie A. Tratalos <fnref n="3" />, Sanyi Tang <fnref n="2" /> and Keith Cressman <fnref n="4" />

**Abstract:** Time series of abundance indices for Desert Locusts *Schistocerca gregaria* (Forskål 1775) and Oriental Migratory Locusts *Locusta migratoria manilensis* (Meyen 1835) were analysed independently and in relation to measures of solar activity and ocean oscillation systems. Data were compiled on the numbers of territories infested with swarms of the Desert Locust from 1860–2015 and an inferred series that compensated for poor reporting in the 1860 to 1925 period. In addition, data for 1930 to 2014, when reports are considered to have been consistently reliable were converted to numbers of 1° grid squares infested with swarms and separated according to four different geographical regions. Spectral analysis to test the hypothesis that there are cycles in the locust dynamics revealed periodicities of 7.5 and 13.5 years for the inferred series that were significant according to the Ornstein-Uhlenbeck state-space (OUSS) test. Similar periodicities were evident in the 1° grid square data and in each of the regions but even though these were significantly different from white noise, they were not significant according to the OUSS criterion. There were no significant peaks in the Oriental Migratory Locust results with the OUSS test, but the data were significantly different from white noise. To test hypotheses that long term trends in the locust dynamics are driven by solar activity and/or oceanic oscillation systems (the Southern Oscillation Index (SOI), the North Atlantic Oscillation Index (NAO) and the Indian Ocean Dipole (IOD)), the original locust data series and their Kalman-filtered low frequency (LF) components were tested for causality using both spectral coherence tests and convergent cross mapping. Statistically significant evidence was found that solar activity measured by numbers of sunspot groups drive the dynamics, especially the LF components, of both species. In addition, causal links were inferred between both the SOI and NAO data and Desert Locust dynamics. Spectral coherence was also found between sunspot groups and the NAO, the IOD and LF SOI data. The data were also analysed showing that the LF SOI had causal links with the LF inferred Desert Locust series. In addition, the LF NAO was causally linked to the LF 1° grid square data, with the NAO for December-March being most influential. The results suggest that solar activity plays a role in driving locust abundance, but that the mechanisms by which this happens, and whether they are mediated by fluctuations in oceanic systems, is unclear. Furthermore, they offer hope that information on these phenomena might enable a better early warning forecasting of Desert Locust upsurges.

**Keywords:** Desert Locust *Schistocerca gregaria* (Forskål 1775); Oriental Migratory Locust *Locusta migratoria manilensis* (Meyen 1835); spectral analysis; Kalman filter; spectral coherence; convergence cross mapping; sunspot groups; ENSO; SOI; IOD; NAO

**Citation:** Cheke, R.A.; Young, S.; Wang, X.; Tratalos, J.A.; Tang, S.; Cressman, K. Evidence for a Causal Relationship between the Solar Cycle and Locust Abundance. *Agronomy* 2021, 11, 69. https://doi.org/10.3390/agronomy11010069

Received: 30 November 2020

Accepted: 28 December 2020

Published: 31 December 2020

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**Copyright:** © 2020 by the authors. License MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

<footnote n="1">¹ Natural Resources Institute, University of Greenwich at Medway, Central Avenue, Chatham Maritime, Kent ME4 4TB, UK; Stephen.Young@greenwich.ac.uk</footnote>

<footnote n="2">² School of Mathematics and Information Science, Shaanxi Normal University, Xi'an 710119, China; xiawang@snnu.edu.cn (X.W.); sytang@snnu.edu.cn (S.T.)</footnote>

<footnote n="3">³ Centre for Veterinary Epidemiology and Risk Analysis, School of Veterinary Medicine, University College, Belfield, Dublin 4, Ireland; jamie.tratalos@ucd.ie</footnote>

<footnote n="4">⁴ Food and Agriculture Organization of the United Nations, Viale delle Terme di Caracalla, 00153 Rome, Italy; Keith.Cressman@fao.org</footnote>

<footnote>* Correspondence: r.a.cheke@greenwich.ac.uk</footnote>
# 1. Introduction

The Desert Locust *Schistocerca gregaria* (Forskål 1775) is a major agricultural pest in Africa, the Middle East and Asia. As with all locusts, the species is polyphenic, occurring in different phases. When low density solitarious phase insects become sufficiently numerous they begin to congregate and transform into gregarious phase insects, via a *transiens* intermediate phase. Triggers for such population increases are rainfall and consequent vegetation growth, with the cover and status of the vegetation affecting threshold densities that elicit gregarisation in both hoppers and adults [1,2]. Once in the gregarious phase, the nymphs occur in hopper bands and later, as adults, they may form swarms which migrate long distances by day. In contrast, solitarious insects do not swarm and migrate by night. For details of the biology and control of the Desert Locust see [3–8].

Desert Locusts require adequate rain at sites where their egg-pods have been laid to allow hatching and, if the rainfall has been sufficiently high, the hatching will coincide with flushes of green vegetation for them to feed on. Consequently, there has been extensive research on the causes of outbreaks such as relations between rainfall patterns and Desert Locust upsurges from which we know that changes in rainfall determine locust breeding (proximate causes) [9,10] and regional studies have sought patterns that could be used for forecasting [11–13]. In this study we examined whether drivers of these changes in rainfall—the effects of sunspot activity or oceanic cooling—are themselves correlated with locust abundance (ultimate causes). Although it has been shown that outbreaks of the Brown Locust *Locustana pardalina* (Walker 1870) are influenced by the Pacific *El Niño/La Niña* Southern Oscillation (ENSO) system, with generally more locusts associated with cold *La Niña* events [14], no published attempt to investigate if this is also the case for the Desert Locust has been made. Here we make good this omission by analysing long-term data sets on numbers of territories and on numbers of 1° grid squares in which swarms of the Desert Locust have been reported in relation to ENSO and North Atlantic Oscillation (NAO) data.

As the genesis of locust outbreaks may vary according to the timing and locations of upsurges, we further hypothesise that any links between such upsurges and oceanic systems will vary with season and geographic region and so we have also analysed the 1° grid square locust data, which are available as monthly series, according to quarterly periods for four different regions in addition to our analyses of the annual data.

Swinton [15] proposed that periodicities in the now extinct Rocky Mountain Locust *Melanoplus spretus* (Walsh 1866) were linked to variations in solar phenomena, an idea supported by others in relation to sunspot minima and the Desert Locust [16–20]. However, Ramchandra Rao [16] and Uvarov [17] questioned how the proposed link could be causal, except indirectly via changes in rainfall and temperature. It is such an indirect link that we re-examine here with more up-to-date data, given that there is now renewed interest in how solar activity might cause temperature changes on earth [21,22] and hence influence weather patterns. We also examine whether any such link with sunspot activity may be mediated by fluctuations in oceanic oscillation systems, using indices of oceanic systems such as the Southern Oscillation Index (SOI) and the NAO. When the NAO index is positive, conditions are colder and drier than average over the north-western Atlantic and Mediterranean regions and when it is negative conditions are warmer and wetter than average in northern Europe, the eastern United States of America and parts of Scandinavia. However, of particular relevance for this study, the NAO also has influences on the weather patterns remote from the Atlantic across parts of Asia and the Middle East [23]. Another index likely to influence weather patterns in the eastern part of the Desert Locust's geographical range is the Indian Ocean Dipole (IOD).

In addition, in a preliminary explorative study, we examined possible effects of sunspot activity on populations of the Oriental Migratory Locust. The biology of the Migratory Locust *Locusta migratoria* (Linnaeus 1758) is similar to that of the Desert Locust and other polyphenic locusts. The form that occurs in China and neighbouring countries, the Oriental Migratory Locust *L. m. manilensis* (Meyen 1835), was monitored by monks
for many years and a very long time series of 1910 years for this insect exists [24]. Its dynamics are determined by precipitation and temperature, with there being more locusts under dry and cold conditions and when locust abundance was high in the year or years before [24], so this locust's dynamics may warrant further investigations of how they might be influenced by fluctuations in oceanic systems, a topic to be considered elsewhere.

**2. Materials and Methods**

**2.1. Data Sets**

Details of the data sets used are summarised in Table 1. Data on numbers of territories infested with swarms of Desert Locusts between 1860 and 2015 inclusive were collated from reports held at the Food and Agricultural Organisation of the United Nations (FAO) to provide a yearly time series (DL) (Figure 1). A second time series, known as the inferred time series of the numbers of territories infested with swarms (IT), was prepared according to the methods described by Waloff [25]. This includes information on probable or deduced occurrences of Desert Locust swarms for the period 1860 to 1925. The IT series is considered to be a more accurate reflection of events than the original data series, given the vagaries of reporting rates and the efficiencies of recording surveys which varied during the above lengthy period. The IT was added to the 1926 to 2015 data and the two data sets are depicted in Figure 1, which also distinguishes periods of recession, plague onset, plague peak and plague decline. Given the variation in sizes of the territories used to compile these data sets, data from the more reliable recording period from 1930 to 1987 were converted in a previous study to numbers of 1° grid squares infested with locust swarms and analysed in relation to rainfall at monthly intervals [10]. Here we also use these data, but we have updated them to provide an annual series for 1° grid squares during the 1930 to 2014 period. The 1° grid square data were also separated according to recording regions (Western, North Central, South Central and Eastern; see maps in [26] in which it is also shown that the numbers of reported swarms are cross-correlated).

<figure><img src="image_21.png" /><figcaption>**Figure 1.** Numbers of territories as defined by Waloff (1976) [24] infested with swarms of the Desert Locust 1860 to 2015 inclusive. The solid line represents the inferred time series for 1860 to 1925 (see text), grey areas are recession periods, the dashed line depicts periods of plague onset, solid black lines indicate plagues and open bars show periods of plague declines.</figcaption></figure>
Data on the Oriental Migratory Locust *L. m. manilensis* for the period 1700 to 1911 were analysed using data published by Tian et al. [24].

Although monthly data for the locust, sunspot and oceanic data were available, annual data were analysed as being most appropriate because the locusts need more than a month to complete their life cycles.

The sunspot data used were the sunspot group number series compiled by Hoyt and Schatten [27,28] updated by Vaquero et al. [29]. These data comprised daily counts which we converted to yearly mean data from 1610 to 2010. The sunspot group numbers refer to counts of active regions rather than the number of, individual, small spots.

Possible effects of the North Atlantic Oscillation (NAO) on locusts were examined. Positive NAO index values are associated with stronger-than-average westerly winds over the middle latitudes, more intense weather systems over the North Atlantic, and wetter/milder weather over western Europe (https://climatedataguide.ucar.edu/climate-data/hurrell-north-atlantic-oscillation-nao-index-pc-based). The NAO data were available as annual totals and for different periods: December to February inclusive (DJF); March to May (MAM); June to August (JJA); September to November (SON) and also for December to March (DJFM) as it is known that the NAO is most noticeable during the boreal winter (December to March inclusive) [30], when its effects may reach across Europe to Asia [23]. Another index likely to influence weather patterns in the eastern part of the Desert Locust's geographical range is the Indian Ocean Dipole (IOD). As precise measurements for the IOD are only available for short periods, relationships between it and locust abundance were investigated using a proxy series, the mean unfiltered dipole mode index, which is based on measurements taken from marine corals [31].

<table><caption>**Table 1.** Data sets used in the analyses.</caption><thead><tr><th>Data Set</th><th>Unit</th><th>Interval</th><th>Start Date</th><th>End Date</th><th>Source</th></tr></thead><tbody><tr><td>Oriental Migratory Locust series</td><td>Counties with locusts multiplied by outbreak intensity (graded 1–3) adjusted for recording effort (Ladj in [9])</td><td>Annual</td><td>1610</td><td>1911</td><td>Tian et al. (2011)</td></tr><tr><td>Total Desert Locust (DL)</td><td>Territories</td><td>Annual</td><td>1866</td><td>2015</td><td>FAO</td></tr><tr><td>Total Desert Locust Inferred Series (IT)</td><td>Territories</td><td>Annual</td><td>1866</td><td>2015</td><td>FAO</td></tr><tr><td>Total Desert Locust 1° grid square series (DLSA)</td><td>1° grid square</td><td>Annual</td><td>1930</td><td>2014</td><td>Converted by JAT from FAO data</td></tr><tr><td>Regional versions of DLSA for the Western (DLSAW), North Central (DLSANC), South Central (DLSASC) and Eastern Regions (DLSAE)</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Southern Oscillation Index (SOI)</td><td></td><td>Annual</td><td>1866</td><td>2015</td><td>http://www.esrl.noaa.gov/psd/gcos_wgsp/Timeseries/SOI/</td></tr><tr><td>North Atlantic Oscillation (NAO)</td><td></td><td>Annual, DJF, MAM, JJA, SON &amp; DJFM series</td><td>1899</td><td>2016</td><td>https://climatedataguide.ucar.edu/climate-data/hurrell-north-atlantic-oscillation-nao-index-pc-based. (Principal Components based time series of the leading Empirical Orthogonal Function of Sea Level Pressure anomalies over the Atlantic sector, 20°–80° N, 90° W–40° E.)</td></tr><tr><td>Indian Ocean Dipole</td><td>Coral reconstruction of mean unfiltered dipole mode index</td><td>Annual</td><td>1846</td><td>1994</td><td>https://www.ncdc.noaa.gov/paleo-search/study/8607; [31]</td></tr><tr><td>Sunspot Group Numbers</td><td></td><td>Annual</td><td>1610</td><td>2010</td><td>http://www sidc.be/silso/groupnumberv3</td></tr></tbody></table>

## 2.2. Statistical Analyses

Spectral analyses were performed on the original unadjusted series (DL), on the inferred series (IT) and on the 1° degree gridded data series (DLSA) using the *peacots* package (version 1.3) run in R version 3.6.1 (downloaded from the University of Bristol, UK, CRAN) [32] that calculates the periodogram using a Fast Fourier Transform (FFT) and tests for significance against the null hypothesis of the Ornstein-Uhlenbeck state-space (OUSS) model [33]. Given results of the spectral analyses, for additional analyses the
data were separated into high (HF) and low frequency (LF) components by Kalman fil-
tering [34]. Kalman filtering was achieved by using the integrated random walk method
(function irwsm in the CAPTAIN toolbox programmed for MATLAB, available from:
http://captaintoolbox.co.uk/Captain_Toolbox.html/Captain_Toolbox.html). The LF com-
ponent is derived first and this is subtracted from the original series to provide the HF
component. The LF component of variability in the time series can be thought of as repre-
senting decadal variability, with the HF component illustrating interannual variability.

To compare pairs of time series to seek causal relationships, two tests were used (a) spectral coherence with the function *myspec* in the R package *atsa* [35] and (b) convergent cross mapping (CCM; [36]). The spectral coherence test used calculates the squared spectral coherence. This is a measure that picks out those frequencies for which the strongest degree of covarying by two variables occurs and for which significance values can be assigned. Prior to testing for spectral coherence, the data were smoothed with a Daniell kernel, with parameter *m* = 4, which is a centred moving average that creates a smoothed value at time *t* by averaging all values between times *t* − *m* and *t* + *m* (inclusive). For example, the smoothing formula for a Daniell kernel with *m* = 2 is *x*<sub>*t*</sub> = (x<sub>*t*−2</sub> + x<sub>*t*−1</sub> + x<sub>*t*</sub> + x<sub>*t*+1</sub> + x<sub>*t*+2</sub>)/5. The smoothed data were then tapered 10% (meaning application of a function, here a cosine taper, to split the series into short windows to minimise the effect of discontinuities between the beginning and end of the time series, thereby reducing the standard deviation of each spectral estimate to 10%; i.e., the centre of the data window is enhanced relative to the extremities) and made stationary by removing the mean and linear trends. Significant spectral coherence for ergodic, stationary, linear systems indicates causal links between pairs of variables [37].

An alternative test for causality is the Granger test [38] but, as Tsonis et al. [22] pointed out, the Granger test is inappropriate for nonlinear dynamic systems, so convergent cross mapping was carried out using the R function *CCM_boot* from the *multispatialCCM* package produced for Multispatial Convergent Cross Mapping [39]. CCM is suitable for non-linear coupled systems and tests the ability of lagged versions of one series to predict the dynamics of another series. It is also appropriate for cases in which the sign of correlations changes with time or is not significant for some sections of the series. The method’s essentials are described by Chang et al. [40]. Based on applications of Takens’s theorem [41], CCM first uses simplex projection [42] to test predictabilities by using data from numerous lagged data points of a process that is part of a bigger system to predict the dynamics of the process in question. The relationship is then tested for causality based on the assumption that interacting processes have information on each other if the information from the first process can be used to predict the second one. To achieve this, it is first necessary to estimate the optimal embedding dimension (E), i.e., the number of temporal steps needed for predictions, for each series. Next, confirmation that the system is non-linear is obtained from showing that the predictive power measured by Pearson’s correlation coefficient (ρ) decreases with increasing prediction time steps. Finally, causality can be confirmed only if the predictive power (ρ) increases with the number of historical data points (L, the library length) needed to make predictions. To prevent the order of sampling affecting the results, a bootstrapping method is used with the number of iterations needed determined by when the mean and standard deviation of the estimates of (ρ) stabilise. For the analyses reported here 1500 iterations were run. For statistical significance testing, if (ρ) is larger at the longest L available than at the shortest L and is also greater than zero at the longest L, then causal forcing is concluded. Results were only accepted as significant if, in addition to significance for a potential driver causing a particular response, the impossible reverse relation of the response causing the potential driver was not significant. In the examples of Chang et al. [40], the data were first normalised to zero mean and unit variance, but here we test unfiltered and Kalman-filtered data (see above) using the method of Clark et al. [39]. In all of these tests, values of time delays (tau) were taken as 1, as we were interested in seeking relationships rather than developing forecasting models, in contrast to methods including lags used by Ye et al. [43] and Chen et al. [44].
**3. Results**

**3.1. Spectral Analysis: Oriental Migratory Locusts**

There were no significant peaks in the Oriental Migratory Locusts results with the OUSS test, but the data were significantly different from white noise (random signals with equivalent intensities at different frequencies), according to the less stringent Kolmogorov-Smirnov test.

**3.2. Spectral Analysis: Numbers of Territories Infested with Desert Locust Swarms, 1866–2010**

Figure 2 shows the results of spectral analyses of the inferred Desert Locust series (IT), the sunspot groups and the SOI for the 1866 to 2010 data (truncated from 2014 as the available sunspot groups data only extend to 2010). For the Desert Locusts (1866 to 2014) there was a significant peak at a periodicity of 13.5 years (p = 0.02; based on the OUSS test) and another at a periodicity of 7.5 years (p = 0.02). The sunspot groups show a strong peak corresponding to a periodicity of 11.19 years (p < 0.0001). The SOI has a series of peaks at various periodicities but even the highest at a periodicity of 3.5 years was not significant (p = 0.62). The strongest peak in the annual NAO data for the 1899–2016 period was at a periodicity of 7.9 years, but this was only just significant with the OUSS test (p = 0.05).

<figure><img src="image_22.png" /><figcaption>**Figure 2.** Periodograms of (A) the inferred Desert Locust series (IT), (B) the Southern Ocean oscillation Index (SOI), (C) the sunspot groups; and (D) the North Atlantic Oscillation Index (NAO).</figcaption></figure>

**3.3. Spectral Analysis: Numbers of 1° Grid Squares Infested with Desert Locust Swarms, 1930–2014**

Figure 3 shows the results of spectral analyses of the 1° grid square locust data total and according to region, together with the sunspot groups data for the shorter period (1930–2010). All of the locust series, analysed up to 2014 inclusive, show peaks for periodicities between 7 and 14 years. The peaks for the whole area's data and for the Western and North Central regions corresponding to periodicities of 14 years are not significant with the OUSS test (p = 0.61) but are significantly different from white noise (p = 0.02). However, the peak corresponding to a periodicity of 7 years in the North Central Region's data is significant
with the OUSS test ($p = 0.04$). The highest peak in the Eastern region data corresponds to a periodicity of 10.6 years, not significant with the OUSS test ($p = 0.2$) but significantly different from white noise ($p = 0.01$). This peak is similar to the sunspot groups' peak for this shorter series (1930–2010) which has a periodicity of 10.12 years, which is highly significant (OUSS test, $p < 0.0001$). Thus, although many of the peaks in the 1° grid square data are not significant with the OUSS test they do correspond in range with those for the longer IT series (see Section 3.2. above) which are significant at periodicities of about 7 and 14 years. No significant peaks were found by spectral analysis of the SOI data for 1930–2014.

<figure><img src="image_23.png" /><figcaption>**Figure 3.** Periodograms of (A) 1930–2014 data for the total number of 1° grid squares infested with Desert Locusts (DLSA), (B–E) for each of the 4 regions ((B) DLSAW, (C) DLSANC, (D) DLSASC, (E) DLSAE) and (F) the sunspot groups.</figcaption></figure>

## 3.4. Kalman Filtering

Figure 4 shows the inferred numbers of territories series (IT) split into high and low frequency (ITlf) components by Kalman filtering and Figure 5 shows the SOI and NAO and their low and high frequency Kalman-filtered series. Figure 6 shows the numbers of 1° grid squares infested with swarms of Desert Locusts from 1930 to 2014 and the results of Kalman filtering and Figure 7 show the data arranged according to region.
<figure><img src="image_24.png" /></figure>

<figure><img src="image_25.png" /><figcaption>**Figure 4.** Results of Kalman-filtering. (A). Unadjusted data of numbers of territories infested with swarms of Desert Locusts (DL, crosses), low frequency component (circles) and high frequency component (squares). (B). The same for the inferred numbers of territories infested with swarms (IT).</figcaption></figure>
<figure><img src="image_26.png" /><figcaption>**Figure 5.** Results of Kalman-filtering. (A). Southern Oscillation Index unfiltered data 1866–2014 (SOI, crosses), low frequency component (circles) and high frequency component (squares). (B). The same for the North Atlantic Oscillation Index (NAO) 1899–2016.</figcaption></figure>
<figure><img src="image_27.png" /><figcaption>**Figure 6.** Numbers of 1° grid squares infested with swarms of Desert Locusts 1930–2014 (DLSA, crosses), together with results of Kalman filtering to provide low frequency components (circles) and high frequency components (squares).</figcaption></figure>

<figure><img src="image_28.png" /><figcaption>**Figure 7.** Numbers of 1° grid squares infested with swarms of Desert Locusts 1930–2014 according to region (crosses), together with results of Kalman filtering to provide low frequency components (circles) and high frequency components (squares).</figcaption></figure>
### 3.5. *Spectral Coherence: Sunspots and Oceanic Systems*

Table 2 presents results of spectral coherence tests for the sunspot groups data and various oceanic indices. Although the coherence between the sunspots and the unfiltered SOI is not significant there is a significant result ($p < 0.01$) for the sunspots and the low frequency SOI data at cycles of 7.89 years (Figure 8). There are also significant coherences between the sunspot groups and both the NAO and the low frequency component of the NAO at cycles of 4.05 years and with the IOD at 4.5 years.

<table><caption>**Table 2.** Spectral coherence and convergent cross mapping CCM results between sunspot groups and oceanic indices. For spectral coherence significant coherent frequencies are denoted * for $p < 0.05$; ** for $p < 0.01$; *** for $p < 0.001$. Frequencies common to at least two pairs of time series in a row are highlighted in bold. NAO = North Atlantic Oscillation; NAOIf = low frequency component of NAO; IOD = Indian Ocean Dipole; SOI = Southern Oscillation Index; SOIIf = low frequency component of SOI.</caption><thead><tr><th colspan="6">Time Series 2 (Response)</th></tr><tr><th></th><th>NAO</th><th>NAOIf</th><th>IOD</th><th>SOI</th><th>SOIIf</th></tr></thead><tbody><tr><td colspan="6"><strong>Time Series 1<br>(Driver)</strong></td></tr><tr><td colspan="6"><strong>Spectral Coherence</strong></td></tr><tr><td>Sunspot Groups</td><td>4.05 **</td><td>4.05 ***, 5.79 *</td><td>4.50 *, 2.37 **</td><td>NS</td><td>7.89 **</td></tr><tr><td colspan="6"><strong>Convergence Cross Mapping</strong></td></tr><tr><td>Sunspot Groups</td><td>NS</td><td><i>p</i> &lt; 0.0001</td><td>NS</td><td>NS</td><td><i>p</i> = 0.04</td></tr></tbody></table>

<figure><img src="image_29.png" /><figcaption>**Figure 8.** Spectral coherence between sunspot groups and the low frequency component of the SOI (SOIIf). The solid horizontal red line indicates significance at $p = 0.001$, dashed line $p = 0.05$, dotted line $p = 0.05$. The blue dashed lines represent 95% confidence intervals.</figcaption></figure>

### 3.6. *Spectral Coherence: Oriental Migratory Locusts*

Significant spectral coherence was found between the 1610–1911 series of sunspot groups and the Oriental Migratory Locusts, with the main peak at 12.3 years ($p < 0.01$) and subsidiary peaks at 11.4, 5.6 and 4.2 years (all $p < 0.05$, Figure 9).
Sunspot groups and Oriental Migratory Locusts, 1610–1911

<figure><img src="image_30.png" /><figcaption>**Figure 9.** Spectral coherence between sunspot groups and Oriental Migratory Locusts, 1610–1911. The solid horizontal red line indicates significance at *p* = 0.001, dashed line *p* = 0.05, dotted line *p* = 0.05. The blue dotted lines represent 95% confidence intervals.</figcaption></figure>

3.7. *Spectral Coherence: Numbers of Territories Infested with Desert Locust Swarms, 1866–2014 (1866 to 2010 for Sunspot Groups Data)*

Table 3 shows the results of spectral coherence tests for different potential drivers and each of the Desert Locust series. There were significant coherences between all potential drivers tested and both IT (inferred Desert Locust series) and ITIf (low frequency (lf) component of IT) at many different frequencies, but these significant frequencies differed amongst both drivers and locust series making interpretation difficult. Furthermore, these cycle lengths differed slightly as the Daniell smoothing factor varied (e.g., by increasing it from 4 to 7), although the same combinations of drivers and locust series were still found to be significant. Nevertheless, given the results of spectral analyses indicative of cycles of between 7 and 14 years, coherences within this range may be the most informative. Those of interest include the link between the sunspot groups and the ITlf at 7.14 years (*p*< 0.05), a frequency recurring for the effects of the low frequency component of the SOI (SOIIf) on both IT (*p* < 0.05) and ITIf (*p* < 0.01). The coherences between the SOI and both IT and ITIf at 8.8 years (both *p* < 0.01; Figure 10), a result repeated for the coherence between SOIIf and ITIf (*p* < 0.001), are also of interest. In the table, frequencies repeated along a row have been highlighted in bold; these include the coherences between SOIIf and both IT (*p* < 0.05) and ITIf (*p* < 0.01) at 10.7 years. The low frequency component of NAO (NAOIf) has highly significant (*p* < 0.001) coherences with IT and ITIf at 9.23 years, a frequency repeated for both IT and ITIf with all of the seasonal versions of NAOIf except that for June–August.
<table><caption>**Table 3.** Spectral coherence results between Desert Locust time series and oceanic indices and sunspots. All statistically significant values of coherent cycle lengths are presented. Significant results are denoted * for *p* < 0.05; ** for *p* < 0.01; *** for *p* < 0.001. IT = Inferred Desert Locust series (1866–2014 [or 2010 for link with sunspots]); ITlf = low frequency component of IT; DLSA = 1° gridded data (1930–2014); DLSAIf = low frequency component of DLSA, followed by the same for regional data (W = West; NC = North Central; SC = South Central; E = East). SOI = Southern Oscillation Index; NAO = North Atlantic Oscillation; IOD = Indian Ocean Dipole; NAOIf = low frequency component of NAO, followed by seasonal data. Frequencies common to at least two pairs of time series in a row are highlighted in bold.</caption><thead><tr><th colspan="9">Time Series 2 (Response)</th></tr><tr><th></th><th>IT</th><th>ITlf</th><th>DLSA</th><th>DLSAIf</th><th>DLSAWIf</th><th>DLSANCIf</th><th>DLSASCIf</th><th>DLSAEIf</th></tr></thead><tbody><tr><td colspan="9"><strong>Time Series 1 (Potential Driver)</strong></td></tr><tr><td>Sunspot groups</td><td>22.86*,<br>2.32**</td><td>7.14*, 2.27*</td><td>6.23*</td><td>6.75***,<br>4.26*</td><td>6.75*</td><td>6.75**,<br>4.26*</td><td>6.75*</td><td>6.75***</td></tr><tr><td>SOI</td><td>8.82**, 7.89*,<br>4.29*, 3.95*,<br>2.68***,<br>2.54**</td><td>8.83**, 8.33*,<br>3.41*, 2.67**,<br>2.54*</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td></tr><tr><td>SOIf</td><td>10.71*, 7.14*,<br>5.17*, 4.0**,<br>3.75*, 2.68**,<br>2.54**</td><td>15.0*, 10.71*,<br>8.82***, 7.14*,<br>5.0*, 3.41*,<br>3.09* 2.68**,<br>2.54*</td><td>NS</td><td>6.43**, 4.74,<br>***, 2.90*</td><td>12.86*,<br>6.92***,<br>4.09**, 3.21**</td><td>6.42*, 5.0<br>***, 3.10*,<br>2.90*</td><td>5.0***, 3.21,<br>*, 2.90*</td><td>6.43**, 5.0<br>**, 2.90*,<br>2.14**</td></tr><tr><td>NAO</td><td>2.10**</td><td>3.24*, 2.10**</td><td>NS</td><td>4.5*</td><td>4.5*</td><td>4.5*, 4.09*,<br>3.75*, 3.33*</td><td>NS</td><td>NS</td></tr><tr><td>NAOIf</td><td>9.23***, 6.32<br>***, 2.10***</td><td>60.0*, 17.14<br>**, 9.23***,<br>6.0**, 3.16*,<br>2.10***</td><td>NS</td><td>15.0*, 8.18<br>***, 4.5***</td><td>18.0*, 8.18<br>*, 6.0*, 4.74**</td><td>15.0*, 8.18<br>***, 4.5*</td><td>8.18***,<br>5.29*,<br>4.74*, 4.5*,<br>2.19*, 2.0*</td><td>20*, 7.5*,<br>6.92**, 4.5<br>***, 3.12*,<br>2.18*</td></tr><tr><td>NAODJIf<br>(winter)</td><td>9.23*, 6.0**</td><td>17.14***, 9.23<br>***, 6.0***,<br>5.22*</td><td>NS</td><td>15.0*, 8.18<br>***, 4.5***</td><td>15.0*, 8.18<br>*, 6.0*, 4.74**<br>*, 4.29*</td><td>15.0*, 8.18<br>***, 4.74***</td><td>8.18***,<br>5.29*, 4.74**<br>*</td><td>15.0*, 6.92<br>**, 4.5***,<br>3.21*, 2.90*</td></tr><tr><td>NAOMAMIf<br>(spring)</td><td>9.23*, 6.0*</td><td>9.23***, 6.0<br>***, 3.24*</td><td>3.21*</td><td>15.0*, 10.0<br>*, 6.43*,<br>4.74***</td><td>18.0*, 9.0<br>**, 6.43***,<br>4.5*</td><td>15.0**, 10.0<br>*, 6.43**,<br>4.74***</td><td>15.0**,<br>9.0*, 7.5*,<br>4.74***</td><td>18.0**, 6.43<br>***, 4.74<br>***, 2.81*</td></tr><tr><td>NAOJJAlf<br>(summer)</td><td>20*, 10*, 6.67<br>*, 4.8*</td><td>20**, 13.33**,<br>10**, 8**,<br>6.67**, 4.8**,<br>3.53***</td><td>NS</td><td>30.0**, 15.0<br>**, 9.0***,<br>6.43*, 5.29<br>***, 4.28*,<br>3.10*</td><td>30.0**, 15.0<br>**, 11.25**,<br>9.0*, 6.92**,<br>5.29*, 4.29<br>**, 3.21*,<br>2.09*</td><td>30.0**, 15.0<br>**, 9.0***,<br>5.29***,<br>3.21**</td><td>30.0**, 15.0<br>**, 9.0***,<br>5.29**, 4.29<br>***, 3.10**,<br>2.09**</td><td>15.0**, 9.0<br>**, 6.0**,<br>5.29**, 4.29<br>***, 3.10**,<br>2.09**</td></tr><tr><td>NAOSONIf<br>(autumn)</td><td>9.23*, 3.64*,<br>2.40**</td><td>15.0*, 9.23<br>***, 6.0*, 4.8<br>*, 3.33* 2.40***</td><td>9.0*,<br>5.62*</td><td>9.0***, 6.43<br>**, 5.0**</td><td>12.86*, 9.0<br>***, 6.92***,<br>3.33*</td><td>9.0***, 6.43<br>**, 5.0***</td><td>11.25***,<br>9.0***,<br>5.0***</td><td>9.0*, 6.43<br>***, 5.0*</td></tr><tr><td>IOD (Mean<br>DMI)</td><td>12.27*, 3.21*</td><td>3.37**</td><td>NS</td><td>7.20*</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td></tr></tbody></table>
<figure><img src="image_31.png" /><figcaption>**Figure 10.** Squared coherency of SOI and ITIf. The solid horizontal red line indicates significance at *p* = 0.001, dashed line *p* = 0.05, dotted line *p* = 0.05. The blue dotted lines represent 95% confidence intervals.</figcaption></figure>

3.8. *Spectral Coherence: Numbers of 1° Grid Squares Infested with Desert Locust Swarms, 1930–2014*

The sunspot groups have significant spectral coherence with the unfiltered total number of 1° grid squares infested with desert locusts (DLSA) series at a frequency of 6.23 years (*p* < 0.05) but a consistent coherence at 6.75 years for all of the low frequency gridded data series regardless of region (*p* < 0.001 for DLSAIf (Figure 11) and DLSAElf, *p* < 0.01 for DLSANCIf and *p* < 0.05 for DLSAWIf and DLSASCIf).

<figure><img src="image_32.png" /><figcaption>**Figure 11.** Squared coherency of the sunspot groups and DLSAIf (the low frequency component of DLSA, the 1° gridded data (1930–2014)). The solid horizontal red line indicates significance at *p* = 0.001, dashed line *p* = 0.05, dotted line *p* = 0.05. The blue dotted lines represent 95% confidence intervals.</figcaption></figure>
No significant spectral coherence was detected between the SOI and the DLSA series (Table 3), but the SOIIf had coherences with all low frequency gridded data sets with a frequency of 6.4 years recurring in DLSAlf and the South Central and Eastern regions.

The unfiltered NAO data showed consistent coherence at 4.5 years with DLSAlf and the low frequency regional data sets for the Western and North Central regions (all *p* < 0.05). The NAOIf and the December to January version of it were consistently coherent with all low frequency versions of the locust gridded data at 8.18 years, except in the Eastern region. The link at 9 years between the September-November version of the NAOIf and all versions of the locust gridded data, which also tallied with the 9.2 years noted for the same potential driver with the IT and the ITIf, was also remarkable.

The IOD showed weak coherence with DLSAlf at 7.2 years (*p* < 0.05), but not with the regional data, but as this oceanic system is linearly correlated with the SOI (*p* < 0.001) it was not surprising that results were similar for both systems.

### 3.9. *Convergent Cross Mapping: Sunspot Groups and Oceanic Systems*

Although there was no evidence of a causal relationship between the sunspot groups and the ocean oscillation systems when unfiltered data were used, significant causal relationships were found between sunspot groups and the low frequency components of both the SOI (*p* = 0.04) and the NAO (*p* < 0.0001), which is consistent with the spectral coherence results (Table 2).

### 3.10. *Convergent Cross Mapping: Oriental Migratory Locusts*

Although there was no evidence of a causal relationship between the sunspot groups and the unfiltered Oriental Migratory Locust data, a significant effect of the sunspot groups causing its low frequency was detected (*p* = 0.001) but this result is invalid as the reverse, impossible, result (OML causing sunspots) was also significant (*p* < 0.04).

### 3.11. *Convergent Cross Mapping (CCM): Desert Locusts*

Remarkably, CCMs gave strong evidence for sunspot groups causing changes in Desert Locust abundance as measured in all of the locust series (*p* < 0.0001 in every case; Table 4; Figure 12), except for the unfiltered data of the inferred series (IT). No potential driver was found to be causing changes in the IT but, in addition to the sunspot groups result, there was also evidence of the ITIf being driven by the SOIIf (*p* < 0.005), and by the low frequency versions of the NAO for March to May (*p* < 0.0001), June to August (*p* < 0.01) and September to November (*p* < 0.0001).

The low frequency component of the Kalman-filtered numbers of 1° grid squares infested with Desert Locust swarms for the 1930 to 2014 period (DLSAlf) was also found to be causally linked to the low frequency component of the SOI (SOIIf, *p* < 0.0001). This relationship also held true when the SOIIf data were analysed at the regional level for the South Central (*p* < 0.001) and Eastern regions (*p* = 0.002), but not for the North Central and Western regions.

The low frequency component of the NAO for the 1930–2014 period was consistently shown by CCM to drive the low frequency components of the gridded data (DLSAlf, *p* < 0.0001; DLSAWIf, *p* < 0.001; DLSANClf, *p* < 0.005; DLSASCIf, *p* < 0.0001; DLSAEIf, *p* < 0.003). These results were to a large degree matched when the seasonal versions of the NAOIf data were used, with strong evidence (*p* < 0.01) of a causal link between the December to March version and all of the low frequency gridded data sets, including the entire (whole year, all regions aggregated) series (DLSAlf) (Figure 13).
<table><caption>**Table 4.** Results of analyses using CCM tests of causality of various time series (drivers) causing the low frequency inferred Desert Locust series 1866–2014 (ITlf) and the gridded Desert Locust series (1930–2014). *p* values given if relationships statistically significant (NS = not significant). Note that the results for the unfiltered sunspot groups data (1866–2010) reveals significance for all relationships with the gridded Desert Locust data. DLSA = 1° gridded data (1930–2014); DLSAlf = low frequency component of DLSA, followed by the same for regional data (W = West; NC = North Central; SC = South Central; E = East). SOI = Southern Oscillation Index; NAO = North Atlantic Oscillation; NAOlf = low frequency component of NAO, followed by seasonal data.</caption><thead><tr><th></th><th colspan="7">Time Series 2 (Response)</th></tr><tr><th></th><th>ITlf</th><th>DLSA</th><th>DLSAlf</th><th>DLSAWlf</th><th>DLSANClf</th><th>DLSASCIf</th><th>DLSAEIf</th></tr></thead><tbody><tr><td colspan="8"><em>Time Series 1 (Driver)</em></td></tr><tr><td>Sunspot</td><td>&lt;0.0001</td><td>&lt;0.0001</td><td>&lt;0.0001</td><td>&lt;0.0001</td><td>&lt;0.0001</td><td>&lt;0.0001</td><td>&lt;0.0001</td></tr><tr><td>Groups</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>SOIf</td><td>&lt;0.005</td><td>NS</td><td>&lt;0.0001</td><td>NS</td><td>NS</td><td>0.001</td><td>0.002</td></tr><tr><td>NAOlf</td><td>NS</td><td>NS</td><td>&lt;0.0001</td><td>0.001</td><td>&lt;0.005</td><td>&lt;0.0001</td><td>0.003</td></tr><tr><td>NAODJFlf</td><td>NS</td><td>NS</td><td>0.034</td><td>NS</td><td>NS</td><td>0.0007</td><td>NS</td></tr><tr><td>NAOMAMlf</td><td>&lt;0.0001</td><td>NS</td><td>&lt;0.0001</td><td>NS</td><td>0.009</td><td>&lt;0.0001</td><td>0.0007</td></tr><tr><td>NAOJJAIf</td><td>0.01</td><td>NS</td><td>0.038</td><td>NS</td><td>0.021</td><td>&lt;0.005</td><td>&lt;0.0001</td></tr><tr><td>NAOSONlf</td><td>&lt;0.0001</td><td>NS</td><td>0.048</td><td>0.05</td><td>&lt;0.003</td><td>&lt;0.003</td><td>NS</td></tr><tr><td>NAODJFMlf</td><td>NS</td><td>NS</td><td>0.0007</td><td>0.002</td><td>0.004</td><td>&lt;0.0001</td><td>&lt;0.003</td></tr></tbody></table>

<figure><img src="image_33.png" /><figcaption>**Figure 12.** Convergent cross mapping results for sunspot groups and the 1° gridded Desert Locust series 1930–2014. (A). Showing use of maximum values of correlation coefficient rho (*ρ*) to estimate the embedding dimension (*E*) of each series; (B) showing decreasing rho with increasing number of prediction steps to confirm non-linearity and (C) showing increasing rho with increasing library length (*L*) for the hypothesis that sunspot groups cause DLSA (*p* < 0.0001) and for the converse (DLSA causes sunspot groups, *p* = 0.69).</figcaption></figure>
<figure><img src="image_34.png" /><figcaption>**Figure 13.** Convergent cross mapping results for the low frequency NAO series for December-March (NAODJFMIf) and the low frequency 1° gridded Desert Locust series 1930–2014 (DLSAlf). (A). Showing use of maximum values of correlation coefficient rho (ρ) to estimate the embedding dimension (E) of each series; (B) showing decreasing rho with increasing number of prediction steps to confirm non-linearity and (C) showing increasing rho with increasing library length (L) for the hypothesis that NAODJFMIf causes DLSAlf (p = 0.0007) and for the converse (DLSAlf causes NAODJFMIf, p = 0.29).</figcaption></figure>

**4. Discussion**

The analyses described above have provided evidence for causal links between the solar cycle, measured by numbers of sunspot groups, and low frequency components of the NAO and the SOI (Table 2) and between both unfiltered NAO and SOI data and their low frequency components with indices of population variation in both Desert Locusts (Tables 3 and 4) and Oriental Migratory Locusts (Sections 3.6 and 3.10). From these results it might appear that the solar activity is driving the oceanic systems and that these are, in turn, driving the locusts. However, such an explanation is simplistic since the links between the sunspot data and the oceanic systems are weak and the cycle lengths detected are not consistent with each other. Indeed, a recent study failed to find a significant link between solar activity and the NAO [45], so our finding of spectral coherence between sunspot group numbers and both the unfiltered series and the low frequency components of the NAO (and with the LF SOI) may be of more interest to climatologists than to entomologists. Nevertheless, what is clear from our results is that there are causal relationships between solar activity and locusts, but this may not necessarily be via the oceanic systems, directly, but perhaps via some other means whereby the sun influences weather patterns.

We have also demonstrated for the first time that there is cyclicity in the dynamics of Desert Locust populations (Section 3.2). The latter result contrasts with the conclusion of Waloff [25] that there are no regular cycles in Desert Locust outbreaks, but lends support to the findings of Cheke and Holt [9] that there were some evidence for 7 and 13 year cycles in a series of data up to 1990, although these were not statistically significant. Spectral analysis confirmed the existence of a statistically significant 13.5 year cycle, a near harmonic of the similarly significant peak at 7.5 years, in the 1866 to 2015 inferred series of numbers of territories infested with Desert Locust swarms. Similar periodicities were evident in the shorter 1930–2014 1° grid square data sets but these were not significant
according to the stringent OUSS test, although they were significantly different from white noise according to the Kolmogorov-Smirnov test. Although Waloff and Green [46] argued that there was no evidence of periodicity in locust plagues, their data on the frequency distribution of intervals between onsets of successive regional plagues were highest (9 instances) at 5–7 years and 14–16 years (8 instances). With the above evidence for cycles of 7.5 and 13.5 years, it is of considerable interest that the significant and most consistent coherent frequencies between different drivers and the locust dynamics reported in Table 3 include 8.8 (SOI) and range from 6.23 to 7.14 (sunspot groups) and from 8.2 to 15 (low frequency NAO). A possible explanation for some of these results, as far as the sunspot groups is concerned given their well-known approximately 11 year cycle (confirmed here, Figure 2c), could be forthcoming if there is a certain low value of solar activity responsible for triggering appropriate meteorological events conducive to locusts. Such a value would be reached both when the solar activity is rising towards its peak and when it is descending to a trough and, depending on the magnitude of such a certain value in relation to the period of the cycle, the interval between these events could be about every 6–8 years.

Although there were no significant cycles detected by spectral analysis of the Oriental Migratory Locust data, there was significant spectral coherence between the sunspot groups and the Oriental Migratory locusts at 12.3 years, evidence of a link between them. In this context it is of interest that statistically significant ($p$ < 0.05) peaks at 17.3, 3.7 and 2.9 years have been reported previously for the Brown Locust [14].

The different values for the spectral coherences associated with different drivers (Table 3) are difficult to interpret and may reflect inaccuracies resulting from the methods used (e.g., see Section 3.7. for comment on effects of varying the Daniell smoothing span in spectral coherence) but it needs to be borne in mind that Desert Locust outbreaks do not spring from a single source outbreak area, but from a variety of gregarisation zones located in each of the four regions (Western, North Central, South Central and Eastern, see maps in [26,47]) within a huge 10,000 km wide geographical area from West Africa to India. Therefore, it is to be expected that different oceanic drivers or their seasonal variants could be responsible for causing outbreaks in different regions. Thus, Waloff [25] reported three upsurges originating in the Western region, three in the North Central region, one in the South Central region and four in the Eastern region. Therefore, different oceanic drivers or seasonal variants of them could be responsible for causing outbreaks in different regions at different frequencies, as suggested by some of the results in Table 3. In addition, once an outbreak has started the locusts will often move out of their region of origin into another region and flourish there, resulting in a detectable signal from an oceanic driver being linked to the second rather than the first region. Such considerations may explain why the low frequency series of the NAO for September-November shows consistent coherence with all of the Desert Locust series tested (Table 3) at nine year intervals, whereas they might be expected to be most influential on the Western or North Central regions. It should also be borne in mind that an increase in the low frequency component of the locust dynamics will not always be reflected in a corresponding high frequency increase if weather conditions or control activities curtail a locust upsurge.

Both conventional statistical methods and the convergent cross mapping revealed relations between the SOI and the Desert Locust time series. The results for the inferred series and the shorter 1° grid square series were not always consistent and there are some regional variations to be discussed below. The SOI has high values during cold *La Niña* conditions and low values during warm *El Niño* circumstances (See https://www.ncdc.noaa.gov/teleconnections/enso/indicators/soi/). The short-term high frequency components of Brown Locust numbers have previously been found to be high during *La Niña* periods [14], but here we have concentrated on the long-term low frequency components. The rationale for the association is that there will be higher rainfall, essential for locust success, in many of the Desert Locust outbreak areas when *La Niña* cold conditions lead to more rain than the quantities that fall during the warm *El Niño* periods. This is supported by the CCM analyses suggesting causal links with the low frequency SOI driving the low frequency DLSA data
set for the total number of infested 1° grid squares. However, at the regional level the links
only held for the low frequency SOI with the South Central and Eastern regions' locust data,
confirming our hypothesis that there are regional variations.

The NAO is a key factor that is responsible for inter-annual variability over extensive geographical zones, but especially of the north African climate [48]. When the NAO is low in winter, the north African climate tends to be warm and wet, and there is an inverse relation between the December to February NAO and November to April rainfall over Morocco [49]. Across western Africa, annual variations in seasonal climatic conditions are determined primarily by the Atlantic Ocean, although the rest of the world's oceans also play important roles (http://www.ipcc.ch/ipccreports/tar/wg2/index.php?idp=380). The temperature in the Sahara is affected by the NAO, with warmer winter temperatures during negative NAO events and cooler winters with more frosts when the NAO is positive [30]. Thus, the finding that the low frequency DJFM NAO data have the strongest causal links with the low frequency locust data according to the CCM tests (Table 4) is consistent with such NAO variability. Indeed, as already mentioned above, the NAO has correlations in DJFM that reach across Europe into the Middle East and Asia (see Figure 1 of [30]).

Inspection of Figure 1 reveals that, until the current upsurge began in 2018, in recent years there has been reduced locust activity. An exception was the Western region, where many upsurges begin. It is likely that the lack of major plagues until very recently was attributable to modern control practices. However, it may also explain why some of the cycles and trends were not as clear in the 1930–2014 1° grid square series in comparison with the long 1866–2014 inferred territories series, but this could also be explained by the higher likelihood of any patterns in the data being demonstrated to be statistically significant as the number of observations increases [50].

Given the number of statistical tests carried out (Tables 2–4), it is likely that some statistically significant results could have been found by chance alone but there are sufficient consistencies in the results for us to consider that it is unlikely that they are spurious. For instance, many of the CCM results are consistent with the spectral coherence tests (e.g., for sunspot groups) and the evidence suggests that there are links between sunspots and the locusts, probably mediated by the influence of the sunspots on oceanic systems (Table 2 shows the low frequency SOI to be coherent at a frequency of 7.89 years with the sunspots), and hence through weather systems, although exactly how this is achieved remains unclear and requires further research. In addition, given the evidence for cyclicity in the sunspots and likely delays in a similar response in the insect dynamics, it is possible that with further research a method of forecasting long-term trends in Desert Locust outbreaks may become feasible to supplement the short term methods [12] currently in use. As emphasised in the Introduction, the analyses presented here sought trends for the long term dynamics and ultimate causative factors, with results suggesting recurrences every 7–15 years, whereas the proximate factor, rainfall, is linked to the short-term dynamics with lags of 4 to 12 months [10]. Future research will seek to use the results presented here to develop models capable of providing early warning forecasts, perhaps by developing models based on the techniques described by Ye et al. [43] and Chen et al. [44]. For this, it will be necessary to examine detailed effects of lags (all of the CCM results discussed here assumed that tau = 1) to devise predictive models that could be tested to see if they anticipate the Desert Locust outbreak that began in 2018 (incidentally during a solar minimum) and to test the hypothesis that some outbreaks are driven by the SOI and others by the NAO in relation to seasonal and geographic variations in rainfall and locust abundance.

**5. Conclusions**

Statistically significant cyclicity exists in the population dynamics of the Desert Locust *Schistocerca gregaria* at periods of 7.5 and 13.5 years, according to results of spectral analysis of the well-known inferred time series. Tests using the methods of spectral coherence and convergent cross mapping revealed statistically significant evidence that dynamics
of both Desert Locusts and Oriental Migratory Locusts *Locusta migratoria manilensis* and, in particular their low frequency dynamics, are causally linked with numbers of sunspot groups. In addition, causal links were inferred between both the Southern Oscillation Index (SOI) and the North Atlantic Oscillation (NAO) and Desert Locust dynamics, with the NAO for December-March being most influential. Solar activity seems to play a role in driving locust abundance, but by exactly what means and the extent to which fluctuations in oceanic systems are determinants remains unclear, although weak causal links between the sunspots and both the SOI and the NAO were also found. Nevertheless, it is possible that further research on the phenomena identified may eventually enable better early warning forecasting of desert locust upsurges.

**Author Contributions:** R.A.C., K.C. and S.T. conceived the study. K.C. provided the locust data in GIS format that were converted to time series by J.A.T., X.W. calculated the Kalman filtered series. R.A.C. and S.Y. conducted the analyses. R.A.C. wrote the paper with input from all authors. All authors have read and agreed to the published version of the manuscript.

**Funding:** This study was supported by the National Natural Science Foundation of China (NSFC, 11471201 and NSFC, 12031010), the Fundamental Research Funds for the Central Universities (grant number: GK202007039) and research funds of the University of Greenwich.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Data on the sunspots and oceanic oscillation systems are available in the publicly accessible repositories listed in Table 1. Other data presented in this study are available on request from the corresponding author.

**Acknowledgments:** We are grateful to the late Joyce Magor for her compilations of, and corrections to, the FAO Desert Locust data set, without which this study would not have been possible, and to two anonymous referees for their helpful comments.

**Conflicts of Interest:** The authors declare no conflict of interest.

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Article

# Desert Locust Stopped by Tibetan Highlands during the 2020 Upsurge

Jun Liu<fnref n="1" />, Michel Lecoq<fnref n="2" /> and Long Zhang<fnref n="3" />,*

**Abstract:** Desert locust is an important pest to agriculture. In 2019–2020, a major upsurge originated in the southern Arabian Peninsula and gradually spread to east Africa, then to south-west Asia, as far as Pakistan and India, even reaching Nepal, resulting in major agricultural losses. For the first time, a few swarms entered southern Tibet. Using field observations and experiments, we studied their path to the Tibetan plateau and their behavior at these very high altitudes. The locusts moved up the Tibetan valleys from low-lying areas (1700 m) to much higher elevations (5400 m). The low temperatures and high humidity put them under severe stress; their activities were limited, and they did not survive long or produce local offspring. It is clear that the high-altitude environmental conditions in the Himalayan mountains provided an important natural barrier that limited the northward expansion of the desert locust populations.

**Keywords:** *Schistocerca gregaria*; Tibet; high altitude plateau; invasion route; survival; natural barrier

**Citation:** Liu, J.; Lecoq, M.; Zhang, L. Desert Locust Stopped by Tibetan Highlands during the 2020 Upsurge. *Agronomy* 2021, 11, 2287. https://doi.org/10.3390/agronomy11112287

Academic Editor: Andrea Sciarretta

Received: 27 September 2021

Accepted: 2 November 2021

Published: 11 November 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

<figure><img src="image_35.png" /></figure>

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

## 1. Introduction

Desert locust, *Schistocerca gregaria* (Forskål, 1775) is an important pest to agriculture for many countries in Africa, the Middle East, and SW Asia. This polyphagous insect can cause damage to almost all types of vegetation and crops [1–3]. It is endowed with a high reproductive potential under favorable rainfall conditions [1,4,5], as well as a great capacity for flight, frequently migrating hundreds of kilometers, sometimes even over oceans [6,7]. The transition from a solitary to a gregarious phase is another characteristic of the desert locust that depends on population density, and locusts in the gregarious phase can migrate long distances in large swarms and cause huge crop losses [8–11].

In general, the desert locust is mainly distributed in arid and semi-arid regions from the Sahara through the Arabian Peninsula to southwest Asia in lowland areas [3,4,12–14]. This recession area covers around 19M km<fnref n="2" />. During invasions, desert locusts can spread over larger territories (an invasion area of 31M km<fnref n="2" />) and reach the limits of the areas they can colonize, often unfavorable for breeding, but where they can still cause significant damage. This is the case of the humid woodlands of Africa to the south, or the too cold and mountainous regions to the north. Until now, in the Asian part of its range, the northern mountains (such as Himalayas and Kunlun Mountains with an average altitude of 4000 m) seemed to constitute an uncrossable barrier. Particularly in the southern end of the range, if northern India and Nepal have sometimes been colonized, no swarms have ever been reported from Tibet. Yet, this is what has happened recently.

In 2019 and 2020, the desert locust again threatened large areas of east Africa, the near east, and south-west. The upsurge originated in the desert regions of Arabia that received exceptional rainfall. The Food and Agriculture Organization of the United Nations (FAO) described this as the most severe locust situation in decades [15–17]. To the south, swarms

<footnote n="1">¹ College of Grassland Science and Technology, China Agricultural University, Beijing 100193, China; liujun2637096337@163.com</footnote>

<footnote n="2">² French Agricultural Research Centre for International Development, Center for Biology and Management of Populations, F-34398 Montpellier, France; mlecoq34@gmail.com</footnote>

<footnote n="3">³ Shandong Academy of Agricultural Sciences, Jinan 250100, China</footnote>

<footnote>* Correspondence: locust@cau.edu.cn</footnote>
invaded much of eastern Africa, reaching Kenya, Uganda, and Tanzania, which have not faced a threat of this magnitude in 70 years. To the east, from March 2019, the invasion spread to Iran, Pakistan, and India, even reaching Nepal, and for the first time Tibet [15–18]. In particular, in June 2020, locust swarms invaded Nepal, and some of them reached the foot of the Himalayas [17]. Until now, there had been no reports of locust swarms in the Tibetan highlands. It was considered that swarms cannot cross high mountain ranges such as the Atlas Mountains, the Hindu Kush, or the Himalayas and that the temperature was too cold for the desert locust at these high altitudes. During past major desert locust invasions, and even though northern India was widely contaminated, no desert locusts had been reported in Tibet. While Nepal is included by Waloff in the range of the desert locust, Tibet is not [19–21]. Only a solitary phase adult female has been reported in Tibet, collected by Zhang Xue Zhong, on 29 April 1974, in the Zhangmu area of Nyalam County (about 28°20′ N, 86° E, altitude 2250 m) [22,23]. According to Cai [24], the desert locust is also present in Yunnan (China), but there is no specific information to support this assertion. We believe that this is mainly an assumption based on Yunnan's proximity to neighboring areas of Burma known to be part of the desert locust invasion area. However, in early July 2020, during this new upsurge, as swarms advanced from Iran, Pakistan, India, and Nepal, seven counties or cities in Tibet reported light swarms of locusts, clearly originating in Nepal. Some were recorded at very high altitudes, around 4000 m or more.

Although the risk of desert locusts invading the high-altitude Tibetan plateaus and causing damage was low, the Tibet Forestry and Grass Bureau, Lhasa Customs, and other departments have initiated prevention and control monitoring work and issued specific prevention and control and emergency plans [25]. However, even with a near-zero risk, this situation offered the first opportunity to study locusts in these cold, high-altitude areas. How could these swarms invade Tibet and enter the Himalayan range in 2020? What was their fate in these high-altitude areas? Using field observations and experiments, we studied for the first time the path of the desert locust to the Tibetan plateau, as well as its behavior, feeding, maturation, and survival in this region, providing valuable information for eventual forecasting.

## 2. Material and Methods

Between early July and late September 2020, field surveys were conducted in seven localities in southern Tibet where desert locusts were reported: Pulan, Zhongba, Saga, Nyalam, Chentang, Jilong, and Dingri counties (Figure 1). These sites benefit from a wide range of climates: semi-arid in the intense cold zone of the Tibetan plateau (mean annual temperature, T = 6 °C, and annual rainfall, R = 280 mm: Saga) or sub-cold (T < 20 °C and R = 173 mm to 280 mm: Pulan and Zhongba, respectively); temperate semi-humid climate, with low temperature and R = 618 mm (Nyalam); monsoon climate, whether subtropical (mild climate and heavy rainfall in summer and autumn: Chentang), mountain subtropical (T = 10–13 °C, R = 1000 mm: Jilong), or semi-arid temperate (T = 12 °C, R = 319 mm: Dingri).

We focused, in particular, on Nyalam County (ranging from 1700 to 4800 m) and the Boqu River valley, flowing from the north to the south and opening towards Nepal at the level of Zhangmu town (Figure 1). This valley is one of the areas where locust swarms appear to have entered Tibet, obviously from Nepal where locusts have been reported in abundance and where the greatest number of swarm reports have been made. This valley was monitored daily in July and August 2020, except on rainy or foggy days.

Study sites included rugged and steep mountainous areas, riverbanks, sandy lands, and a wide variety of plant communities (Figure 2). Field monitoring focused on locust density per square meter, sex ratio, maturity, and ovary development status within desert locust populations. At least five locust samples were collected from each sampling site. The sex ratio was estimated on a larger number of individuals collected by hand in the morning when the locusts were still inactive. The maturity status of the population was determined based on the appearance of ovaries and ovarioles (immature: white
ovarioles, small and not clearly visible; mature 1: white ovarioles, larger, oocytes starting to develop; mature 2: yellow ovarioles, larger with clearly developing oocytes; mature 3: ovarioles containing well-formed eggs, almost ready for laying; laying: distended, enlarged abdominal segments, no eggs in abdomen).

<figure><img src="image_36.png" /><figcaption>**Figure 1.** Desert locust records in Tibet in July 2020. Colored dots indicate the abundance of desert locust populations in Tibet: gray striped, none; green, very light/light; yellow, middle; red, heavy; black, very heavy. The gray dots in Nepal indicate desert locust reports (swarms or scattered populations) between June 27 and July 16 (modified from Shrestha et al., 2021). The box below the general map details the situation in southern Nyalam County where the degree of infestation was the most serious, along the G318 road (blue dotted line), for about 26 km along the Boqu River valley (1700 to 4200 m).</figcaption></figure>

Caging experiments were conducted in the field to observe and record daily life habits, mating, breeding, and longevity of desert locusts. They were conducted in two sites: Zhangmu (2300 m) and Nyalam (3800 m) (Figure 1). Gregarious locust adults were collected in the field for rearing in cages with *Lolium perenne* (Gramineae) leaves. The cages measured 70 × 70 × 70 cm, had a wooden frame, and were covered with wire mesh. In Zhangmu, 6 cages contained 10 adults each (5 males and 5 females) while in Nyalam, 2 cages were used, each containing 27 locusts (18 females and 9 males).

In addition, food preference tests were implemented. Healthy and active adult locusts were collected from Zhangmu in July 2020 and reared in cages under natural conditions. The tested locusts were starved for 12 h and then placed individually in a transparent plastic cylindrical bottle (60 mm diameter, 200 mm length). Leaves, freshly collected on the morning of the test, were inserted into the bottles, with all leaves of the same plant being the same size as far as possible. Thirty-four plant species, collected in the region, were
tested. At least 10 adults (5 males and 5 females) were fed each plant species. Leaves were removed after 24 h and the area consumed was measured to assess feeding preferences according to the following scale: high >1700 mm², medium >650 mm², low >100 mm²; very low <100 mm².

<figure><img src="image_37.png" /><figcaption>**Figure 2.** Some views of desert locusts in southern Tibet, in 2020, not far from the border with Nepal. (a) A valley having been invaded. (b,c) Light swarms of locusts flying in the clouds at about 3000 m. (d) The Boqu River valley at 3360 m. (e) Landscape at 4670 m altitude with lakes, sandy beaches, and a rare vegetation. (f) Desert locusts found dead in the field at an altitude of 3350 m. (g) basking in the sun on stones, or **(h1–h3)** feeding on wormwood and willow leaves.</figcaption></figure>

We used Graphpad Prism 8.0 for statistical analyses. For food preference tests, Graphpad Prism 8.0 was used to draw the leaf area consumed, imageJ 1.8.0 to measure this area, and unpaired *t*-test or one-way ANOVA for comparisons.

**3. Results**

**3.1. *Desert Locust Penetration in the Tibetan Highlands***

From July 1 to 9, 2020 at least 7 counties in Tibet (Chentang, Dingri, Nyalam, Jilong, Pulan, Saga, Zhongba) reported adult locusts near the border with Nepal. These counties are characterized by valleys opening at a lower altitude at the border with Nepal. The