Research Article |
Corresponding author: Christophe Diagne ( chrisdiagne89@hotmail.fr ) Corresponding author: Anna J. Turbelin ( aturbelin@gmail.com ) Academic editor: Rafael Zenni
© 2021 Christophe Diagne, Anna J. Turbelin, Desika Moodley, Ana Novoa, Boris Leroy, Elena Angulo, Tasnime Adamjy, Cheikh A.K.M. Dia, Ahmed Taheri, Justice Tambo, Gauthier Dobigny, Franck Courchamp.
This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Citation:
Diagne C, Turbelin AJ, Moodley D, Novoa A, Leroy B, Angulo E, Adamjy T, Dia CA.K.M, Taheri A, Tambo J, Dobigny G, Courchamp F (2021) The economic costs of biological invasions in Africa: a growing but neglected threat? In: Zenni RD, McDermott S, García-Berthou E, Essl F (Eds) The economic costs of biological invasions around the world. NeoBiota 67: 11-51. https://doi.org/10.3897/neobiota.67.59132
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Biological invasions can dramatically impact natural ecosystems and human societies. However, although knowledge of the economic impacts of biological invasions provides crucial insights for efficient management and policy, reliable syntheses are still lacking. This is particularly true for low income countries where economic resources are insufficient to control the effects of invasions. In this study, we relied on the recently developed "InvaCost" database – the most comprehensive repository on the monetised impacts of invasive alien species worldwide – to produce the first synthesis of economic costs of biological invasions on the African continent. We found that the reported costs of invasions ranged between US$ 18.2 billion and US$ 78.9 billion between 1970 and 2020. This represents a massive, yet highly underestimated economic burden for African countries. More alarmingly, these costs are exponentially increasing over time, without any signs of abatement in the near future. The reported costs were mostly driven by damage caused by invaders rather than expenses incurred for management. This trend was highly skewed towards a few regions (i.e. Southern and Eastern Africa) and activity sectors (i.e. agriculture) and incurred by a small number of invasive taxa (i.e. mainly three insect pests: Chilo partellus, Tuta absoluta, Spodoptera frugiperda). We also highlight crucial, large gaps in current knowledge on the economic costs of invasions that still need to be bridged with more widespread research effort and management actions across the continent. Finally, our study provides support for developing and implementing preventive measures as well as integrated post-invasion management actions at both national and regional levels. Considering the complex societal and economic realities in African countries, the currently neglected problem of biological invasions should become a priority for sustainable development.
Die ekonomiese koste van uitheemse biologiese indringer spesies in Afrika: ‘n groeiende, maar verwaarloosde bedreiging? Kort titel: Verwaarloosde maar groeiende koste van indringer spesies in Afrika. Uitheemse indringer spesies kan natuurlike ekosisteme en menslike samelewings dramaties beïnvloed. Alhoewel kennis oor die ekonomiese gevolge van indringer spesies belangrike insigte bied vir doeltreffende bestuur en beleid, ontbreek betroubare sintese steeds. Dit geld veral in lande met lae inkomste waar ekonomiese hulpbronne onvoldoende is om die gevolge van indringer spesies te beheer. In hierdie studie het ons vertrou op die onlangs ontwikkelde InvaCost-databasis - die mees omvattende opslagplek vir die monetêre impak van indringer uitheemse spesies wêreldwyd - om die eerste sintese van ekonomiese koste van indringer spesies op die vasteland van Afrika te lewer. Ons het gevind dat die gerapporteerde koste van indringer spesies wissel tussen US $ 18,2 miljard en US $ 78,9 miljard gedurende 1970 tot 2020. Dit verteenwoordig ‘n massiewe, maar tog hoogs onderskatte, ekonomiese las vir Afrikalande. Meer kommerwekkend is dat hierdie koste mettertyd eksponensieel styg, sonder enige tekens van vermindering in die nabye toekoms. Die gerapporteerde koste is meestal weens skade van die indringer spesies eerder as uitgawes wat vir die bestuur daarva aangegaan is. Hierdie neiging was sterk skeefgetrek deur enkele streke (Suider- en Oos-Afrika) en aktiwiteitsektore (veral landbou) en is veroorsaak deur ‘n klein aantal indringer taksa (hoofsaaklik drie insekplae: Chilo partellus, Tuta absoluta, Spodoptera frugiperda). Ons beklemtoon ook belangrike groot leemtes in die huidige kennis oor die ekonomiese koste van indringer spesies wat nog oorbrug moet word met behulp van wyer navorsings en bestuursaksies op die vasteland. Ten slotte bied ons studie ondersteuning vir die ontwikkeling en implementering van voorkomende maatreëls, sowel as geïntegreerde bestuursaksies op beide nasionale en streeksvlak. Met inagneming van die komplekse samelewings- en ekonomiese realiteit in Afrikalande, moet die tans verwaarloosde probleem van indringer spesies ‘n prioriteit word vir volhoubare ontwikkeling.
የሥነ-ሕይወታዊ ወረራዎች ኢኮኖሚያዊ ወጪዎች - እየጨመረ የመጣ ግን ችላ የተባለ ስጋት?
አጭር ርዕስ፡ ቸል የተባለ ግን እየጨመረ የመጣ የሥነ-ሕይወታዊያን ወረራ በአፍሪካ ረቂቅ
ሥነ-ሕይወታዊ ወረራዎች በተፈጥሯዊ ሥነ-ምህዳር እና በሰው ማኅበረሰብ ላይ ከፍተኛ ተጽዕኖ ያሳድራሉ፡፡ ሆኖም ምንም እንኳን ስለ ሥነ-ሕይወታዊ ወረራዎች ኢኮኖሚያዊ ተጽዕኖ ያለው እውቀት ቀልጣፋ ቁጥጥርን እና ፖሊሲን በተመለከተ ወሳኝ ግንዛቤዎችን የሚሰጥ ቢሆንም፣ አስተማማኝ ውህደት (ቅንጅት) ግን አሁንም ይጎለዋል፡፡ ይህ ሁኔታ በተለይ የሥነ-ሕይወታዊ ወረራዎችን ተፅእኖ ለመቆጣጠር በቂ ኢኮኖሚያዊ ሀብት በሌላቸው አገሮች የሚታይ ሀቅ ነው፡፡ በዚህ ጥናት፣ እኛ በቅርቡ ኢንቫኮስት የተባለ የመረጃ ቋት (በዓለም ዙሪያ በወራሪ የውጭ ዝርያዎች የገንዘብ ተጽዕኖዎች ላይ እጅግ የተሟላ መረጃ ያለው የመረጃ ቋት) ባጠናቀረው መረጃ ላይ ተመርኩዘን የመጀመሪያዉን በአፍሪካ ውስጥ ሥነ-ሕይወታዊ ወረራዎች የሚያደርሱትን ኢኮኖሚያዊ ወጪዎች ማጠናቀር ችለናል፡፡ በዚህ መሰረት እ.ኤ.አ. ከ 1970 እስከ 2020 ባሉት ዓመታት የተዘገቡ የሥነ-ሕይወታዊ ወረራዎች ወጪዎች ድምር በ 18.2 ቢሊዮን እና 78.9 ቢሊዮን የአሜሪካ ዶላር መካከል መሆኑን ደርሰንበታል፡፡ ይህ አሃዝ በጣም ተቃሎ (ዝቅ ተደርጎ) የተገመተ ወጪ ቢሆንም ለአፍሪካ አገራት እጅግ ከፍተኛ ኢኮኖሚያዊ ሸክምን ይወክላሉ፡፡ በጣም በሚያስደነግጥ ሁኔታ እነዚህ ወጪዎች በቅርቡ ምንም የመቀነስ ምልክቶች ሳያሳዩ ከጊዜ ወደ ጊዜ በከፍተኛ ሁኔታ እየጨመሩ ይገኛሉ፡፡ ሪፖርት የተደረጉት ወጪዎችም ቢሆኑ በአብዛኛው ወራሪዎቹን ለመቆጣጠር ከሚወጡ ወጪዎች ይልቅ በወራሪዎቹ የሚደርሱ ጉዳቶች ላይ ያተኮሩ ናቸው፡፡ ይህም ሂደት ወደ ተወሰኑ የክፍለ አህጉሩ አከባቢዎች (ማለትም ወደ ደቡብ እና ምስራቅ አፍሪካ) እና የስራ ዘርፎች (ማለትም ግብርና) በጣም ያዘነበለ ሆኖ በጥቂት ወራሪ ዝርያዎች (ማለትም በዋናነት በሶስት ተባይ ነፍሳቶት፣ በሳይንስ ስማቸው ቺሎ ፓርቴሉስ፣ ቱታ አብሶሉታ እና ስፖዶፕፔራ ፍሩጂፔርዳ) የደረሰ ጥቃት ላይ ያተኮረ ነው፡፡ በተጨማሪም በዚህ ጥናት በአህጉር ደረጃ በተስፋፉ ጥናትና ምርምር ጥረቶች እና መቆጣጠሪያ እርምጃዎች ሊሞሉ የሚገቡ ወሳኝና ትላለቅ የሥነ-ሕይወታዊ ወረራዎች ኢኮኖሚያዊ ወጪዎችን በተመለከተ ያሉ ወቅታዊ የዕውቀት ክፍተቶችን እናሳያለን፡፡ በመጨረሻም ጥናታችን የወረራ መከላከያ እርምጃዎችን ለማዘጋጀትና ተግባራዊ ለማድረግ እንዲሁም በብሔራዊም ሆነ ክፍለ-አህጉር ደረጃ የሚተገበሩ የተቀናጁ የድህረ-ወረራ መቆጣጠሪያ እርምጃዎችን ይደግፋል፡፡ በአፍሪካ ሀገሮች ውስጥ ያሉትን ውስብስብ ማህበራዊ እና ኢኮኖሚያዊ እውነታዎች ከግምት ውስጥ በማስገባት በአሁኑ ጊዜ ትኩረት ያልተሰጠው የሥነ-ሕይወታዊ ወረራዎች ችግር ለዘላቂ ልማት ጥቅም ቅድሚያ ሊሰጠዉ የሚገባ ጉዳይ ሊሆን ይገባል፡፡
التكاليف الاقتصادية للغزو البيولوجي في أفريقيا: تهديد متنامٍ، لكن متجاهل؟ يؤثر الغزو البيولوجي بشكل كبير على النظم البيئية الطبيعية، وعلى المجتمعات البشرية. وعلى الرغم من أن المعرفة بالآثار الاقتصادية للغز البيولوجي توفر معلومات بالغة الأهمية من أجل تدبير ناجع وسياسات فعالة، إلا أن التوليفات الموثوقة لا تزال غير متوفرة. وينطبق هذا بشكل خاص على البلدان ذات الدخل المنخفض، حيث الموارد الاقتصادية غير كافية للسيطرة على آثار الغزو. اعتمدنا في هذه الدراسة على قاعدة بياناتInvaCost التي تم تطويرها مؤخرًا - وهي المستودع الأكثر شمولاً للتأثيرات المالية للأنواع الغريبة الغازية في جميع أنحاء العالم – من أجل إنتاج أول توليفة للتكاليف الاقتصادية للغزو البيولوجية في القارة الإفريقية. ولقد تبين أن التكاليف المبلغ عنها للغزو البيولوجي تراوحت بين 18,2 مليار دولار أمريكي و 78,9 مليار دولار أمريكي ما بين عامي 1970 و 2020 ، ويمثل هذا عبئا اقتصاديا هائلا على البلدان الإفريقية التي لازالت تقلل من شأنه. كما أن المقلق في الأمر هو أن هذه التكاليف تتزايد بشكل كبير مع مرور الوقت، دون أي علامات على التراجع في المستقبل القريب. وكانت معظم التكاليف المبلغ عنها ناجمة عن الأضرار الناتجة عن الأنواع الغازية بدلاً من المصاريف المتكبدة من أجل التدبير. وتجدر الإشارة إلى أن الاتجاه هم بشدة مناطق قليلة (أي جنوب وشرق أفريقيا) وبعض قطاعات الأنشطة (أي الزراعة) وكبدها عدد قليل من الأصناف الغازية (أي ثلاث آفات حشرية بشكل أساسي: Chilo partellusو Tuta absolutaو Spodoptera frugiperda). كما نسلط الضوء أيضًا في هذه الدراسة على الفجوات الكبيرة والحاسمة في المعرفة الحالية حول التكاليف الاقتصادية للغزو البيولوجي التي لا تزال بحاجة إلى سدها من خلال المزيد من الجهود البحثية الواسعة النطاق والإجراءات الإدارية في جميع أنحاء القارة. وفي الأخير، تقدم دراستنا الدعم لوضع وتنفيذ تدابير وقائية فضلا عن إجراءات إدارية متكاملة بعد الغزو على الصعيدين الوطني والإقليمي.
Ɲanamaya finkuraw bɛsekakɛ nɔdyateminɛ taye kungo lahalaw ni sigida lahalaw kan. Alini ayasɔrɔ dɔniya minu bɛ talikɛ ɲanamaya finkuraw cyarili musakakola, ka kunafoni nafamaw jira, ka kɛɲɛ ni maralifɛrɛw ani gilancyoko jonjonw ye, alisa tɔbujɛ. o sɛbɛtyaledo dyamana kono minu ka sɔrɔ ka dɔkɔn, ani u ka nafasɔrɔ finw dɔkɔya kaman, utese ka ɲanamaya finkuraw cyarili kɔlɔsi ani ka dansigi u yɛlɛma cyogola. Nin ɲinini kɔnɔnana anyan sinsin kunafoniwkan min bɔra “InvaCost” la. U ka kunafoniw ye finyé min ni mɔgɔ beseka isinsin akan ɲanamaya finw sɔrɔko kunkan ani finsukuya wɛrɛ min bɛ bɔ dunya fanwɛrɛ fɛ ka dunya minɛ. Kunafoni minu bɛ talikɛ “IvanCost” la u sɛbɛntyala wakati labanw na. Nin bara nunu kɛra sababuye ka dyabi fɔlɔ sinsinlenw sɔrɔ minu bɛ talikɛ ɲanamaya finkuraw musaka kola farafina marabolokan. An ya dyateminɛ ko musaka minuw dantikɛla kakɛɲɛ ni fin nunu yariliye, o ba daminɛ Ameriki wari dolari milyari 18,2 ka ta bila 78,9 ka bɔ san 1970 ka na bila 2020. Nin bɛ musaka cyanma kofɔ, ŋa minuw dyatelentɛ farafina dyamanaw bolo. Dabaliban kowɛrɛ tuguni, nin musakanunuw bɛtaka cyokoyala min ka telin, ka kɛɲɛ ni wagatiye, kasɔrɔ u jigini fɛrɛ foyi yiralentɛ. Musaka minu bɔrama, okun dɛnendo bakurubala minu bɛ talikɛ kɔlɔlɔwla minu bɛ talikɛ fin nunuw cyaribawla katɛmɛ musakako min dyalatikɛlendo labarali kama. Sika kun kɛlendo o famuyali bolonina marayɔrɔ damadɔ fanfɛ minu bɛ farafinakɔnɔ ani cyakɛda bolowla (sɛnɛ bo la) min bara tun geleyalendo fɛnɲanamanw fɛ (inafo finsaba hakɛ: Chilo partellus, Tuta absoluta, Spodoptera frugiperda). An ba yira fana ka fɔ ko dɔnya minunw beye sisan ɲanamaya finw cyarili musaka kowkan, ko belebele be u dyɛ minuw kakan ka dafa ni ɲinininw ani waleya waralenw ye farafina fantyama na. Kuntyɛlila anka ni ɲinini bɛnakɛ dɛmɛ ye fɛrɛkunbɛnanw ani waleya minuw bɛ talikɛ kɔlɔlɔwla minuw dɛn nendo fin nunuw cyarilila ka kɛɲɛ ni marayɔrɔw ye. Ka da farafina jamanaw sigida ni a musakakow gɛlɛyakan, gɛlɛya min bɛyen bi na jatelen tɛ kakɛɲɛ ni ɲanamaya finw cyarili ye, o kan ka kɛ bɛ kunkelena ye, yiriwa badabada kama.
Les coûts économiques des invasions biologiques en Afrique: une menace croissante mais négligée ? Les invasions biologiques peuvent avoir un impact considérable sur les écosystèmes naturels et les sociétés humaines. Cependant, bien que les connaissances sur les impacts économiques des invasions biologiques fournissent des informations cruciales en termes de gestion, des synthèses récentes et fiables font encore défaut. Cela est particulièrement vrai pour les pays à faible revenu où les ressources économiques sont insuffisantes pour contrôler les effets des invasions. Dans cette étude, nous nous sommes appuyés sur la base de données "InvaCost" développée récemment - le référentiel le plus complet sur les impacts monétaires des espèces exotiques envahissantes dans le monde - pour produire la première synthèse des coûts économiques des invasions biologiques sur le continent africain. Nous avons constaté que les coûts déclarés des invasions varient entre 18,2 milliards de dollars américains (USD) et 78,9 milliards USD entre 1970 et 2020. Cela représente une charge économique énorme, mais encore très sous-estimée, pour les pays africains. Plus alarmant encore, ces coûts augmentent de façon exponentielle au fil du temps, sans aucun signe de réduction pour les années à venir. Les coûts reportés étaient principalement (i) dus aux dommages causés par les envahisseurs plutôt qu’aux dépenses engagées pour lutter contre leurs invasions, (ii) fortement biaisés vers quelques régions (Afrique australe et orientale) et secteurs d’activité (agriculture) et (iii) associés à un nombre restreint de taxons envahissants (essentiellement trois insectes ravageurs: Chilo partellus, Tuta absoluta, Spodoptera frugiperda). Notre étude met également en lumière de cruciales lacunes dans les connaissances actuelles sur les coûts économiques des invasions qui doivent encore être comblées par des efforts de recherche et des actions de gestion plus importants et étendus à travers le continent. Enfin, notre travail souligne la nécessité de l’élaboration et la mise en œuvre de mesures préventives pour empêcher l’introduction des espèces envahissantes, ainsi que l’intégration des actions de gestion aux niveaux national et régional. Compte tenu des réalités sociétales et économiques complexes des pays africains, le problème actuellement négligé des invasions biologiques devrait être une priorité pour le développement durable.
Ɗaukar nauyin mamayar ƙwayoyin halittu a Afirka : wata barazana mai yaɗuwa amma da aka yi wa kamun sakainar kashi ? Yaɗuwar ƙwayoyin halittu (tsirai ko ƙwari) na iya samun babban tasiri a kan muhalli da al’umomi. Sai dai, ko da yake ilimi da ake da shi a kan tasirin yaɗuwar ƙwayoyin halittun a kan tattalin arziki na bayar da muhimman bayanai don ingantuwar siyasa da gudanarwa, amma amintattun bayanai sun faskara. An fi ganin zahirin hakan musamman a ƙasashe masu raunin tattalin arziki da ba su iya fuskantar lamarin. Cikin wannan binciken, mun yi amfani da rumbun bayanai na InvaCost da aka bunƙasa kwanan nan- kafa mafi cika da inganci a kan tasirin kuɗaɗen da akan kashe don fuskantar zaukakkin ire-iren ƙwayoyin halittu masu mamaya a faɗin duniya- domin samar da amitattun bayanai a kan kuɗin da ake kashewa ta fuskar mamayar kwayoyin halittu a nahiyar Afirka. Mun gano cewa kuɗaɗen da aka bayyana cewa an kashe da ga shekara ta 1970 sun kai biliyan (miliyar) 18,2 dalar Amerika zuwa biliyan 78,9 a shekara ta 2020.
Wannan manufar ta sami komabaya sosai a wasu sassan Afirka (wato sashen kudu na Afirka da gabashin Afirka) da wasu ɓangarorin aiki (wato noma) kuma hakan na da nasaba da wasu ƴan tsirarun irin ƙwayoyin halittu masu mamaya (wato musamman ƙwaro uku maɓarnata albarkatun noma : Chilopartellus, Tuta absoluta, Spodoptera frugiperda).
Muna kuma jan hankali a kan manyan kura-kurai cikin bayanan da ake da su a halin yanzu da suka shafi ɗaukar nauyin mamayar ƙwayoyin halittu, da ya kamata a magance su ta hanyar ƙoƙarin bincike da faɗaɗa gudanar da ayyuka ko’ina cikin nahiyar.
A ƙarshe, bincikenmu na goyon bayan ɗaukar matakan riga kafi da kuma na gudanar da aiki bayan wanzuwar mamaya a matakin ƙasa da ma na ƙasa da ƙasa. Da la’akari da zahirin yanayin tattalin arziki da rayuwar al’umar ƙasashen Afirka mai sarƙaƙƙiya, ya kamata matsalar mamayar ƙwayoyin halittu da ke gudana a halin yanzu, ta zama a sahun gaba don cimma cigaba mai ɗorewa.
Ny totalim-bidy ara-toekarena noho ny fananiham-bohitra biolojika ao Afrika : tsindry tsy mitsaha-mitombo nefa atao tsirambina ? Ny fananiham-bohitra biolojika dia mety hisy fiatraikany lehibe amin’ny tontolo iainana voajanahary sy ny fiarahamonin’ny olombelona. Na dia manome fahalalana betsaka momba ny politika sy ny fitantanana mahomby ny fampahalalana ny voka-dratsy ara-toekarena noho ny fananiham-bohitra biolojika, dia mbola tsy ampy ireo fandravonana azo antoka. Hita taratra izany eo amin’ireo firenena ambany fidiram-bola izay tsy manana ny ampy hifehezana ny vokadratsin’ny fananiham-bohitra. Amin’ity fandinihana ity dia mifototra amin’ny angon-drakitra InvaCost vao novolavolaina tsy ela - ny firaiketana feno kokoa momba ny fiantraika ara-bola ny vokatry ny karazan-javamananaina vahiny mpandrakotra manerantany – mba hamokarana ny fandravonana dingana voalohany ny vidim-piainana noho ny fanafihana biolojika ao amin’ny kaontinanta afrikanina. Tsikaritray fa ny vola lany tamin’ny fananiham-bohitra biologika dia 18,2 miliara $ ka hatramin’ny 78,9 miliara $ teo anelanelan’ny 1970 sy 2020. Fahavoazana lehibe ho an’ny toe-karena izany, nefa dia ambany ny tombatombana ho an’ny firenena afrikanina. Mbola anisan’ny mampatahotra ihany koa ny amin’ireo totalim-bidy ireo izay tsy mitsaha-mitombo hatrany ary tsy misy ny fambara ny amin’ny fihenany. Ny totalim-bidy voalaza dia miompana indrindra amin’ny fahasimbana naterak’ireo mpandrakotra fa tsy ny fandaniana amin’ny fitantanana. Ity fironana voalaza ity dia nitanila tamin’ny faritra vitsivitsy (izany hoe aty amin’ny faritra Afrika atsinanana sy atsimo) sy seha-pikatrohana manokana (izany hoe ny fambolena) ary eo ihany koa ny havitsian’ny karazana mpandrakotra (izany hoe niompana kokoa amin’ireo bibikely mpandrava: Chilo partellus, Tuta absoluta, Spodoptera frugiperda). Tianay ho marihina ihany koa ny tsy fahampiana lehibe eo amin’ny fahalalana momba ny vola lany amin’ny fananiham-bohitra amin’izao fotoana izao izay mbola mila jerena akaiky amin’ny alàlan’ny ezaky ny fikarohana bebe kokoa sy ny hetsika fitantanana manerana ny kaontinanta. Ary farany, ny fandinihanay dia manohana ny famolavolana sy ny fampiharana ny fepetra fisorohana ary koa ny hetsika fitantanana aorian’ny fananiham-bohitra amin’ny sehatra nasionaly sy isam-paritra. Raha ny zava-misy eo amin’ny fahasarotan’ny fiainana ara-piaraha-monina sy ara-toekarena eo amin’ny firenena afrikanina, dia tokony hatao laharam-pahamehana amin’ny fampandrosoana maharitra ny olan’ny fanafihana biolojika amin’izao fotoana izao.
Os custos econômicos das invasões biológicas na África: uma crescente, mas negligenciada ameaça? Invasões biológicas podem impactar ambientes naturais e sociedades humanas dramaticamente. No entanto, embora o conhecimento dos impactos econômicos das invasões biológicas forneça uma visão crucial para gestão e políticas eficientes, ainda faltam sínteses confiáveis. Isso é particularmente importante para países com pouca renda, onde recursos econômicos são insuficientes para controlar os efeitos das invasões biológicas. Nesse estudo, nós contamos com o banco de dados recentemente desenvolvido InvaCost – o repositório mais abrangente sobre os impactos financeiros das espécies invasoras em todo o mundo – para produzir a primeira síntese dos custos das invasões biológicas no continente Africano. Nós encontramos que o custo reportado das invasões variou entre 18,2 bilhões de dólares e 78,9 bilhões de dólares, dados de 1970 a 2020. Esse valor representa uma enorme, apesar de subestimada, carga econômica para os países Africanos. Ainda mais alarmante, esses custos crescem exponencialmente com o tempo e sem nenhum sinal de redução no futuro próximo. Os custos reportados foram direcionados principalmente por danos causados pelas espécies invasoras, mais que pelas despesas devido ao manejo. Essa tendência foi altamente enviesada para algumas regiões (tais como, África Austral e Oriental) e setores de atividade (tal como, agricultura), e gerada por um pequeno número de taxa invasores (tais como, três insetos-pragas: Chilo partellus, Tuta absoluta, Spodoptera frugiperda). Nós também destacamos grandes lacunas no atual conhecimento sobre os custos econômicos das invasões biológicas, que ainda precisam ser superados com mais esforços de pesquisa e ações de manejo em todo o continente. Finalmente, nosso estudo fornece suporte para o desenvolvimento e implementação de medidas de prevenção, assim como ações de manejo integrado pós-invasão em escala nacional e regional. Considerando a complexa realidade social e econômica do continente Africano, o problema atualmente negligenciado das invasões biológicas deve se tornar uma prioridade para o desenvolvimento sustentável.
Ko ruudooji mbarakoñ ngarkoñ e Afrik ngardata : bonere mawnde nde reentaaka ? Ruudooji mbarakoñ na mbaawi adude bonere mawnde e kala windere e nguurndam yimbe. Kono, ko gonggo gandal nowoodi faade ko deen bonne ngadorta ko hadatapolitik e jogogal peewngal. Duumdoon tengtikoyleydeele pamdude doole de koomkoomeeji mum en njonaani ngam reentude bonere diin ruudooji. E ndeer nde windere, baariden koy ligeey « InvaCost » tiaraado ko booyaani - liggeey burdo timmu faade e bonere jawdi leyyi niembaadi jaaknudi aduna – ngam yaltinde fibre idiinde holliroore ko diin ruudooji ngadata e leydi Afrik. En njii wonde diin ruudooji edi mbonna hakkunde 182, miliaaruji dolaar e 78,9 dolaar ko fuddori hitande 1970 faade hitannde 2020. Duumko baasal mangal, kono ngal limaaka, e ndeer Afrik. Ko buriko hulbinaade woni bonere ndeni besdo no feewi niande fof, ko adata ustaare yiaaka… Ko adiiko bonere nde no fawondira umiiko e ruudooji he wona e ko wadaako e ngaynaaka.Duumdoon firti no feewi ko e woon nokkuuji e woon ligiyaaji (woni ndemri) tawa adi dum ko seeda e woon ruudoooji : “Chilo partellus, Tuta absoluta, Spodoptera frugiperda”. Min kolira kadi waasde gandal nofeewi faade e ko di ruudooji mbonanta e danialmen adanimen fotde tinnaade e witto e neende diawdimen e ndeer Afrik.
Ko watindiiko, jangde men teengtini fotde tinnaade ardinde peeje et bade tiagal ruudooji e ndeer leydimen e ko taariindi ko. Si en ndaari aadaaji men e koomkoom mettudo mbo leydeele afrik, itude tiadeele hande umiidi e ruudooji potden ardinde ngam ndanien ngartam booydam.
Los costos económicos de las invasiones biológicas en África: ¿una amenaza creciente pero desatendida? Las invasiones biológicas pueden impactar dramáticamente los ecosistemas naturales y las sociedades humanas. Sin embargo, aunque el conocimiento de los impactos económicos de las invasiones biológicas proporciona información crucial para una gestión y política eficientes, todavía faltan síntesis fiables. Esto es particularmente cierto para países de bajos ingresos donde los recursos económicos son insuficientes para controlar los impactos de las invasiones. En este estudio nos basamos en la base de datos InvaCost, la cual ha sido desarrollada recientemente y constituye el repositorio más completo sobre los impactos económicos de las especies exóticas invasoras a nivel mundial, para producir la primera síntesis de los costos económicos de las invasiones biológicas en el continente Africano. Descubrimos que los costos reportados de las invasiones oscilaron entre US$ 18.2 mil millones y US$ 78.9 mil millones entre 1970 y 2020. Esto representa una carga económica masiva, pero muy subestimada, para los países africanos. Lo que es más alarmante es que estos costos están aumentando exponencialmente con el tiempo, sin mostrar signos de disminución en el futuro cercano. Los costos reportados corresponden principalmente a daños causados por las invasiones, en lugar de a gastos de gestión. Esta tendencia está sesgada hacia unas pocas regiones (África meridional y oriental) y sectores de actividad (agricultura) y resulta de un pequeño número de taxones invasores (principalmente de tres plagas de insectos: Chilo partellus, Tuta absoluta y Spodoptera frugiperda). También destacamos grandes lagunas en el conocimiento actual sobre los costos económicos de las invasiones que deben superarse con esfuerzos de investigación y acciones de gestión más generalizadas en todo el continente. Finalmente, nuestro estudio brinda apoyo para el desarrollo e implementación de medidas preventivas, así como acciones integradas de manejo post-invasión a nivel nacional y regional. Teniendo en cuenta las complejas realidades sociales y económicas de los países africanos, el problema actualmente desatendido de las invasiones biológicas debería convertirse en una prioridad para el desarrollo sostenible.
Alquyuman daɣ awa deqalan emel in almissibaten tin-issudar daɣ Afrik: almissibat tǝtiiwaḍat mušen war-hin nitawajrah? Almiṣṣibaten tin - issudar adobatnat ad- ilanat takmo maqqorat fil awad eqalan ahinzazaɣ d- timuzdoq n-adinat. Hakid-ijja awendaɣ, kud daɣass imiyiišãn d-musnaten idaqqalnen terk-erché tad-d- tirǝwnát almaṣṣibaten ti ikmanen usudar d-ahinzazaɣ, harat wendaɣ kudaɣ amoss ayihakan issalan assoxatnen yi manaɣafan hakid daɣ adabara iwir sarho, hakid ijjawendaɣ wirid inšeš har harwa ayinfan harat.
Harat wendaɣ eqal tidit hulen y-iduwilan wi arkamnén id filas iduwilan windaɣ ibraran hulen daɣ awadeqalan aššujiš in azrǝf iškam diš ad- ajjin iniyat yi haratan wi did tiruwnat almaṣṣibaten.
Daɣ taɣare tadaɣ nasihatal fil issalan id išreynen hanaɣid ifalnen awass itawan Inva Cost- ɣas teɣare ten tǝmoss almintal assoxen daɣ awadeqalan tikmawen meɣ tinfawen in izirfan fil mudaran wi taqalnen almiṣṣibaten daɣ udunia- tǝ mušɣult ten daɣ kul wir tǝga ar-yadid tissaɣsil meɣ adid tišinšiš alquyuman daɣ azrǝf n almaṣṣibaten ti ikmanen issudar daɣ afriq.
Nijrahin as alquyuman witawassaneen n- almaṣṣibaten ilanant jǝr 18,2 in milliards USD d- 78,9 milliards USD jǝr awattay wan 1970 har wan 2020. Awen eqal aẓuk maqqoran daɣ azzruf, hakid- ijjawendaɣ aẓuk wendaɣ atiwalka y i iduwilan win Afriq. Hakid-ijja awendaɣ awa assharahaɣan harwa as alquyuman windaɣ tiwaḍan hak awattay, sas wartila aššamol nas fanzan. Ilquyuman windaɣ attwana ijjan daɣ šaɣšadan ɣas widid orawan almaṣṣibaten issilmadan as waden amašal ayija azrǝf daɣ ijjin n-ewatel y almaṣṣibaten waladaɣ ikanan n ayinfan harat. Harat wadaɣ išrayan n iban kanan n išaɣil sǝmǝk olaɣan ijja hulen daɣ kalan iyaḍ n afrik (ilmintal ikalan n afrik wi ahanen teje tan agala d- win dinig) d išarajan n tǝmašɣolen (šund issuduma) hakid ijja awendaɣ marsalan windaɣ erawtanid harat in ilmissibaten in mudaran (imudaran windaɣ amosnen ilmissibaten ijjan daɣ karad šarajan n magadan witajinen šaɣšad : (Chilo partellus, Tuta absoluta, Spodoptera frugiperda). Nissilmad as ilant marsallan ajjotnen daɣ awadeqalan masnat in alquyuman in awaytajašan tabilant tajjat d- ilmissibaten, ɣas anihaja ad- ittiwitir adabara ijjan daɣ umaɣ d- ǝssimil in tǝmušɣult tolaɣat tǝlssat Afrik. Nissilmad ḍarat awen as teɣare tadaɣ tidhal assǝmil d- ijji n adabaratan ǝmosnen ewatel hakidaɣ tǝmušɣult ibdadnen daɣ ijji n adabara dat assa n- ilmiṣṣibaten daɣ iddiwil hakid daɣ iddiwilan wiyaḍnen. Filas attiwassan attarex d timizdoq n addinant taẓikanẓarat daɣ kalan win afrik, almušaqat ta ti tǝlat ɣas warhin titawajraj ašilidaɣ tǝmoss tabilant d- almiṣṣibaten ti ikmanen issudar, tabilant ten-daɣ ǝntass as anhaja ad taqqal itus yǝssǝmil in effes illan taɣrist.
Tënk. Ruurum ndundat yi lu aay la ci yàq kéew-kéewaan yu bindare yi ak dundug nit ñi. Ci loolu nag, doonte xam-xam am na ci njeexital i ruurum ndundat ci wàllug koom-koom ba aw yoon tijjiku ngir man a saytu caytu gu am solo ak teg i polotig, waaye jarabu yu doy, yu amul benn laam-laame ; àggaguñu ca ba leegi. Loolu ci réew yu néew doole yi la rawatee nag ndax ñàkk njumtukaay ak alal ju ñu waggaree ruur moomu. Jarabu gii, ñi ngi ko sukkandikoo ci dayu InvaCost bi ñu defar bu yàggul – ndàmb li gën a yomb a nànd ngir xam jeexital i ray-donni doxandéem yi ci àdduna wërngal kapp ci wàllug koppar – ngir jëmmal jarabu gi njëkk ci kembaaru Afrig ñeel li ruurum ndundat di jur ci alal ci koom-koom mi. Gis nanu ne ruur mi, li ko dale 1970 ba 2020, bees ko nattee cig njëg ; toll na ci diggante 18.2 ba 78.9 tamndareet i US$. Alal ju bari jii di naaxsaay, luy nasaxal koomu réewi Afrig yi la. Li ci gën a doy waar, koom mu bari moomu ñuy ñàkk day yokk saa yu ne, te amul luy nuru ab dogal bees jël ngir saafara ko ci ëllag ju jampal. Alal jii ñu fésal nag mooy li ruur mi yàq waaye du lu ñu génne ngir saytug ruur mi. Yàqu-yàquy ruur moomu nag tane na ci yenn tund yi (i.e. Penku ak Bëj-déexu Afrig) ak ci yenn aaneer yi (i.e. mbay mi) boole ci lim bu néew ciy ndundat (taxa) ñoo fay ruur (i.e. ñatti gunóori ruurkat yi gën a ràññiku: Chilo partellus, Tuta absoluta, Spodoptera frugiperda). Gendiku nanu itam lu am solo, fi xam-xamu ruur mi tollandi ak alal ji miy laatul ci koom-koom, soxla na bu baax a baax ñu gën a yaatal góor-góorlu gi ci luññtu yi ak yokk jéego yi ci saytug ruur mi ci kembaar gi. Ngir teeral, sunu jarabu gii jur na cëslaay ngir samp ak fànnoo ay dogal yoo xam ne dinañu sóor nees di saytoo bir yi ginnaaw bu am ruur amee moo xam ci biir réew mi la mbaa ci tundi kembaar gi. Bees bàyyee xel ci ni dundiin wi nosoo ak tolluwaayi koom-koomu réewi Afrig yi, soobantal gii tembe ñu soobantal ruurum ndundat yi lu war a dakk la tey yitte ju jamp ngir ug suqliku gu sont te sax dàkk.
Izindleko ezidalwa izimila nezilwanyana zokufika kwizwekazi laseAfrika: Ingozi ekhulayo kodwa enganakiwe. Isihloko esifingqiwe: Izindleko ezikhulayo kodwa ezingakaniwe zemizila nezilwanyana zokufika ezweni-kazi laseAfrika.
Izimila nezilwanyana zokufika zinomthelela omkhulu kwimvelo kanye nenhlalonhle yemiphakathi. Nakuba ukuqondisisa kahle imithelela yezimila nezilwanyana zokufika emnothweni kunikeza imininingwane ebalulekile ikakhulu uma kuzoliwa nokubhebhetheka kwazo kanye nokuhlaziya inqubomgomo yezomthetho, ulwazi olusemqoka noluthembekile lusashoda. Lokhu kuyiqiniso ikakhulukazi emazweni antulayo lapho umnotho ungenele ukuthi kubhekwane nemithelela yezimila nezilwanyana zokufika. Kulolucwaningo, sisebenzise isigcinalwazi iInvaCost esanda kusungulwa- lapho kugcinwe khona ucwaningo olubanzi noluphelele mayelana nemithelela yezimila nezilwanyana zokufika uma kukhulunywa ngomnotho emhlabeni jikelele- lesi sigcina lwazi sizosiza ukudalula izindleko ezivela ngenxa yokubhebhetheka kwezimila nezilwanyana zokufika ezwenikazi lase Afrika. Ngokusebenzisa lesi sigcina lwazi sithole ukuthi izindleko zokumelana nezimila nezilwanyana zokufika zilinganiselwa phakathi kuka $18.2 kuya ku $78.9 wezigidigidi zamadola aseMelika kusekela eminyakeni yo1970 kuya ku2020. Lokhu kutshengisa umthwalo omkhulu, kepha ongazakaze wacwaningwa ngokwanele wezomnotho emazweni ase-Afrika. Okuthusa kakhulu ukuthi lezi zindleko ziyakhula ngokuhamba kwesikhathi kanti futhi azikho izimpawu ezikhomba ukwehla kwazo esikhathini esizayo. Izindleko ezibikiwe zincike kakhulu kumonakalo owenziwa izimila nezilwanyana zokufika kunezindleko zokulwisana nokubhebhetheka kwazo. Lombiko ubususelwe kakhulu ezifundeni ezimbalwa (esizeNingizumu naseMpumalanga yeAfrika) kanye nasemikhakheni embalwa (isb. ezolimo) nakhona kubhekwe izibonelo ezimbalwa (ikakhulukazi izinambuzane ezintathu eziyinkathazo: Chilo partellus, Tuta absoluta, Spodoptera frugiperda). Siphinde siveze ukuntuleka kolwazi olwanele uma kukhulunywa ngezindleko zezomnotho ezidalwa yizo izimila nezilwanyana zokufika, nokusafanele kwenziwe ucwaningo olunzulu ukuze sizoqonda izindlela zokulwisana nokubhebhetheka kwazo ezwenikazi lonke lase Africa. Okokugcina, lolu cwaningo luhlanganiswe ngendlela yokuthi lukwazi ukweseka imizamo yokusungula izindlela ezizokuvimbela ukubhebhetheka kwezimila nezilwanyana zokufika emazingeni amazwe kanye nawezifunda.
Uma kubhekwa inhlalonhle kanye nezomnotho emazweni wonke ase-Afrika, lokhu kuntuleka kolwazi uma kukhulunywa ngezimila zokufika kumele kube yinto ebhekisiswayo ikakhulu uma kukhulunywa ngezentuthuko.
Africa, agriculture, biological invasions, damage, economic costs, InvaCost, management
África, agricultura, invasiones biológicas, daños, costes económicos, InvaCost, gestión
Afrika, landbou, indringer spesies, skade, ekonomiese koste, InvaCost, bestuur
አፍሪካ, ግብርና, ሥነ-ሕይወታዊ ወረራዎች, ጉዳት, ኢኮኖሚያዊ ወጪዎች, ኢንቫኮስት, ቁጥጥር፡፡
Farafina, sɛnɛ, Ɲanamaya finkuraw cyarili, kɔlɔlɔ, musaka, InvaCost, labarali
Afrique, agriculture, invasions biologiques, pertes et dommages, coûts économiques, InvaCost, gestion
Afrig, mbay, ruuri ndundat yi, yàqu-yàyu, njeexital ci koom-koom, InvaCost, saytu gi
Afrika, ezolimo, izimila nezilwanyana zokufika, umonakalo, izindleko zezomnotho, InvaCost, ukubhekana
Biological invasions have become a worldwide problem because of the accelerating rate of globalization, particularly since the end of the 20th century due to increasing modern travel, trade and technology, and these factors are likely to intensify the spread of invasive alien species (IAS) (Seebens 2015; Seebens et al. 2019). Within the context of Africa, the increased threat and spread of IAS will be no exception given the continent’s evolving travel and trade (
Some of these IAS can become invasive after their intentional introduction by humans. For example, the tree Prosopis juliflora was introduced in the Afar region (Ethiopia) for water and soil conservation, shade and wind protection, and as firewood, fencing and building material. P. juliflora soon invaded croplands, grasslands, riverbanks and roadsides in the area, reducing native biodiversity, grazing potential and water supply (
These invasions do not show any signs of abatement in the near future (
Economic aspects are critical in this context, especially regarding the limited economic capacity of most African countries to counteract invasions. Indeed, information on the economic impacts of biological invasions is important at several levels, especially for (i) increasing societal awareness on the substantial losses caused by invasions and compelling policymakers to act on the short- and long-terms against the introduction, proliferation and spread of harmful invaders, (ii) designing efficient policies and implementing evidence-based decisions through both prioritization of targeted IAS and/or susceptible areas as well as pre-evaluation of measures (e.g. cost-efficiency analyses) and (iii) ensuring sustainable management actions according to the economic capacities of countries/regions (
The recent advent of the "InvaCost" database (
We relied on cost data recorded in the "InvaCost" database, which is the most up-to-date, comprehensive, and harmonized compilation and description of economic cost estimates associated with biological invasions worldwide (
To get the most complete and up-to-date dataset of the reported economic costs attributable to biological invasions in Africa for the last fifty years (1970–2020), we used the most recent version of the "InvaCost" database (version 3.0;
We homogenized our "starting dataset" so that each cost entry – realized over a single year, a period of less than a year, or a cost reoccurring over a series of years – corresponds to a single-year estimate, which is repeated over the number of years during which the cost occurred. For this purpose, we used the "expandYearlyCosts" function from the "invacost" package (
To ensure a realistic and conservative synthesis of cost estimates reported for Africa, we applied two successive filters to this "starting dataset" (Suppl. material
We categorized the cost data according to different descriptive fields (hereafter called “descriptors”) in our datasets. First, we grouped countries into the five geographical regions defined by the United Nations geoscheme for Africa (available at https://unstats.un.org/): “Western Africa”, “Southern Africa”, “Northern Africa”, “Middle Africa”, and “Eastern Africa” (the latter also includes countries in the Indian Ocean) (Suppl. material
Our purpose was to draw a complete, as well as a robust picture of the cost of biological invasions throughout the African continent. We used the following R packages - ggplot2 (v.3.3.2,
First, we used the "starting dataset" to describe the full cost information that was available. To do this, we investigated how individual cost estimates and their source materials (i.e. peer-reviewed articles and grey literature) were distributed over time. We focused on both the number of cost estimates and the total costs accumulated between 1970 and 2020. The latter was obtained by summing all cost estimates provided in the "cost estimate per year 2017 exchange rate" column of the expanded version of the "starting dataset" (Suppl. material
Second, we used the "conservative subset" to investigate how the cost amounts were distributed across geographic regions, types of costs, impacted sectors and taxonomic groups for the period 1970–2019. Finally, we investigated the trend of costs over time using two strategies.
The first strategy included an estimation of both the cumulative costs incurred between 1970 and 2020 (i.e. the sum of all cost estimates provided in the "cost estimate per year 2017 exchange rate" column of the expanded subset; Suppl. material
The second strategy consisted of modelling the long-term trends in economic costs of invasions by fitting models of annual costs as a function of time. Indeed, a reliable estimation of the average annual costs over time should take into account (i) the dynamic nature of costs, (ii) the time lags between the real occurrence of the costs and their reporting in the literature (called ‘publication delay’ hereafter), (iii) the heteroscedastic and temporally auto-correlated nature of cost data, and (iv) the effects of potential outliers in the cost estimates. For this purpose, we implemented the "costTrendOverTime" function ("invacost" package;
During the 1970–2020 period, economic costs associated with biological invasions in Africa were obtained separately for 33 countries (i.e. 4 from Middle Africa, 3 from Northern Africa, 3 from Southern Africa, 10 from Western Africa, and 13 from Eastern Africa; see Suppl. material
Distribution of cost estimates over time represented by a the cumulative cost amounts and b the number of cost entries per year between 1970 and 2019. We considered the expanded version of the starting dataset. In a the dashed line corresponds to the total amounts over the complete period, while the other lines correspond to the amounts of damage losses, management expenditures and mixed costs (i.e. when costs could not be exclusively associated with ‘damage’ or ‘management’ type).
About 86% of the cost entries (n = 3,653) collated were only incurred in Southern Africa (Table
Quantitative summary of the cost data and estimates considered in this study for the African continent and each geographic region. Total cumulative costs (between 1970 and 2020) are provided in 2017-equivalent US$ million using both the starting dataset (‘Full’ cost) and conservative subset (‘Robust’ cost). Impacted sectors and type of cost are defined in the Suppl. material
Descriptive field | Category | Africa | Eastern Africa | Middle Africa | Northern Africa | Southern Africa | Western Africa | Mixed | |||||||
Full’ cost (N= 4,259) | Robust’ cost (N= 2,302) | Full’ cost (N=287) | Robust’ cost (N=169) | Full’ cost (N=6) | Robust’ cost (N=3) | Full’ cost (N=12) | Robust’ cost (N=5) | Full’ cost (N=3,653) | Robust’ cost (N=1,980) | Full’ cost (N=155) | Robust’ cost (N=98) | Full’ cost (N=146) | Robust’ cost (N=47) | ||
Impacted sector | Agriculture | 34209,41 | 8772,82 | 7189,47 | 6771,03 | 267,96 | 267,96 | 147,16 | 0,00 | 24,00 | – | 1740,11 | 1733,83 | 24840,72 | – |
Authorities-Stakeholders | 20710,11 | 4406,07 | 50,50 | 28,64 | 0,10 | 0,01 | 593,04 | 196,53 | 19028,77 | 3701,59 | 460,45 | 401,74 | 577,24 | 77,56 | |
Environment | 17029,18 | 3277,03 | – | – | – | – | – | – | 17029,18 | 3277,03 | – | – | – | – | |
Fishery | 0,36 | 0,36 | – | – | – | – | – | – | – | – | 0,36 | 0,36 | – | – | |
Forestry | 22,29 | 0,10 | – | – | – | – | – | – | 0,11 | 0,10 | – | – | 22,19 | – | |
Health | 2,20 | 2,20 | 2,20 | 2,20 | – | – | – | – | – | – | – | – | – | – | |
Mixed | 6974,88 | 1744,35 | 613,83 | 10,09 | 1,80 | – | – | – | 1149,93 | 856,69 | – | – | 5209,33 | 877,56 | |
Public and social welfare | 0,43 | 0,14 | 0,42 | 0,14 | – | – | – | – | – | – | 0,00 | 0,00 | – | – | |
Type of cost | Damage_costs | 56739,68 | 12418,47 | 7797,90 | 6773,37 | 269,76 | 267,96 | 147,16 | 0,00 | 17245,33 | 3216,81 | 2122,70 | 2116,41 | 29156,84 | 43,93 |
Management_costs | 21280,73 | 4937,90 | 45,47 | 25,68 | 0,10 | 0,01 | 593,04 | 196,53 | 19986,66 | 4618,62 | 78,23 | 19,52 | 577,24 | 77,56 | |
Mixed_costs | 928,45 | 846,69 | 13,05 | 13,05 | – | – | – | – | – | – | – | – | 915,40 | 833,63 | |
Taxon | Animalia | 39016,50 | 7954,63 | 5666,22 | 4829,41 | 269,76 | 267,96 | 740,19 | 196,53 | 2,56 | – | 1813,99 | 1749,55 | 30523,78 | 911,19 |
Plantae | 38093,54 | 8636,73 | 435,20 | 373,05 | 0,10 | 0,01 | – | – | 37227,38 | 7833,37 | 386,94 | 386,38 | 43,93 | 43,93 | |
Viruses | 1754,93 | 1609,57 | 1754,93 | 1609,57 | – | – | – | – | – | – | – | – | – | – | |
Diverse/Unspecified | 83,89 | 2,13 | 0,07 | 0,07 | – | – | – | – | 2,05 | 2,05 | – | – | 81,76 | – | |
Total costs for Africa and each region | 78948,86 | 18203,06 | 7856,42 | 6812,10 | 269,86 | 267,96 | 740,19 | 196,53 | 37231,99 | 7835,42 | 2200,92 | 2135,93 | 30649,48 | 955,12 |
Typology and distribution of costs (number and estimates) recorded in the starting dataset according to their reliability (“high” versus “low”) and their implementation (“potential” versus “observed”). We present both cost figures (total cumulative costs in 2017-equivalent US$ million for 1970–2019) and number of expanded cost entries as well as their specific proportion for each official region. Implementation states — at the time of the estimation — whether the reported cost was actually “observed” (i.e., cost actually incurred) or “potential” (i.e. not incurred but expected cost). Method reliability assesses the methodological approach used for cost estimation as of (i) “high” reliability if either provided by officially pre-assessed materials (peer-reviewed articles and official reports) or the estimation method was documented, repeatable and/or traceable if provided by other grey literature, or (ii) “low” reliability if not.
Except for Southern Africa and “diverse/unspecified” regions, more than two thirds of the recorded cost estimates were considered as having been empirically observed in each region (Figure
Considering all cost entries in our "starting dataset", the accumulated cost of IAS in Africa reached a total of US$ 78.9 billion between 1970 and 2020 (see Table
Biological invasions were estimated to cost a minimum of US$ 18.2 billion in Africa over the period 1970–2019 (Figure
Distribution of reliable observed costs (using the conservative subset) following the impacted sectors and type of cost for each geographic region. For both impacted sectors and type of cost, we considered the definition and categories detailed in the Suppl. material
Recorded economic costs were spread unevenly across regions, with Southern Africa and Eastern Africa exhibiting the largest estimates (i.e. US$ 7.8 billion and US$ 6.8 billion, respectively). Apart from these two regions, Western Africa was the only region for which total costs exceeded US$ 1 billion (i.e. US$ 2.1 billion). The lowest reported costs included Middle and Northern Africa with US$ 267 million and US$ 196 million, respectively. Again, these cost estimates were mostly driven by a limited number of reporting countries (Suppl. material
The majority of cost estimates reported throughout the continent were associated with “damage” costs (US$ 12.4 billion) rather than “management” costs (US$ 4.9 billion) (Table
Invasions had the greatest impacts on agriculture with, respectively, about 99% of the costs reported from Eastern and Middle Africa (Figure
Cost estimates were reported for various animals (n = 16 species; US$ 7.9 billion) and plants (n = 45; US$ 8.6 billion), and one virus (US$ 1.6 billion) (Figure
Distribution of the cost amounts (in 2017-equivalent US$ millions) among species recorded in the conservative subset. The species are successively grouped into kingdom, organism type and genus. The size of the bars (rectangles) is proportional to the cost value associated with either the kingdom, organism type or genus. For example, we can see that costs associated with the kingdom Animalia are equal to US$11.6 billion. Animalia comprises the organism groups insect, mammal and bird, so the combined height of the rectangles representing costs for insect, mammal and bird is equal to the height of the bar representing the Animalia Kingdom. Insects contribute the most to costs associated with Animalia and amongst insects, the genus Spodoptera sp. is the most costly. Icons are from (http://phylopic.org/).
The costs of biological invasions steadily increased over the period 1970–2019. During this period, invasions cost on average US$ 303 million per year and the mean cost exponentially increased over decades (Figure
Temporal trends (1970–2019) of costs (in 2017-equivalent US$ millions) a considering the actual distribution of the mean amounts provided for each decade in the conservative subset and b using model predictions (i.e. OLS: ordinary least-squares; GAM: generalized additive model; linear regression, quadratic regression, MARS: multiple adaptive regression splines) and quantile regressions. We considered models calibrated and fitted with at least 75% of cost data completeness from the dataset. We log10-tranformed cost estimates using information from the cost estimate per year 2017 USD exchange rate column in the conservative subset).
Our findings undoubtedly illustrate that invasions incur substantial costs to national African economies, most of them being vulnerable and already weak (Lekunze 2020). The reported financial burden accumulated to a conservative total of approximately US$ 18.9 billion (annual average of US$ 303 million) between 1970 and 2019, reaching an estimated annual average of US$ 2.6–8.6 billion in 2019. However, these costs could seem relatively low compared with those from other continents such as North America (Crystal-Ornelas et al., submitted in the current issue), Europe (Haubrock et al., submitted in the current issue) or Asia (Liu et al., submitted in the current issue). On the one hand, this discrepancy likely reflects the strong geographical imbalance in research intensity and financial capacities (
Worryingly, we found that the economic costs of IAS in Africa are steadily increasing over time without any signs of slowing down, reflecting the continuous increase in the number of IAS worldwide (
A number of logistical, methodological and cost-intrinsic factors may have prevented the capture of the complete diversity – and thus the full amount – of costs. Costs can remain hidden and/or underestimated due to (i) the unclear status of some invasive species (Jarić et al. 2019), (ii) inaccessible source materials (e.g. grey literature;
We showed that economic costs are widely but not evenly distributed across regions. Indeed, most cost estimates were associated with a single country (i.e. South Africa), which is internationally recognized as a pioneering and frontline country for research and management in invasion science (
Moreover, we may expect higher costs for the other regions than those reported here. For instance, Northern Africa has 13 cost entries recorded for only five species, while 157 species are listed in the GISD for this region. Also, Western African countries are historically and contemporarily threatened by a broad variety of biological invaders which is beyond insects and plants that were mostly reported for this region. Indeed, the succession of large international seaports along coastal cities (e.g. Abidjan, Cotonou, Lagos, Dakar) and the parallel development of the extensively urbanizing corridor from Côte d’Ivoire to Nigeria (i.e. the so-called Abidjan-Lagos corridor) may greatly facilitate the introduction of several vertebrate and aquatic invertebrate invaders (
Across the African continent, most of the reported costs were mainly driven by very few taxa, among which the costliest included three insect pests: the spotted stem borer (C. partellus), introduced in Eastern Africa in the 1930s, is suspected to be the most serious pest of maize and sorghum in Eastern and Southern Africa (
Focusing solely on major and well documented (and often mediatized) agricultural threats may have an ‘umbrella’ effect on other less visible but harmful invaders for which the costs may be unsuspected or neglected. Indeed, only a small spectrum of species (about 15%) from those recognized as invading Africa in the GISD were reported here. This strongly corroborates a previous assumption that only a small portion of invaders have been economically analyzed (
Therefore, it is evident that research intensity is closely connected with societal and economic realities in African countries. Hence, strong collaborations should be established and/or amplified between scientists, authorities, various sectoral stakeholders as well as local communities to understand and deal with the multidimensional issues raised by biological invasions.
Our results clearly highlight that IAS are a significant economic burden in Africa and the costs of these invasions are largely driven by damage induced by invaders. Monetary estimates associated with managing invasions were scarce and the amounts spent were essentially restricted to South Africa and North Africa. This pattern reflects a missed opportunity, since one of the rare examples we have for the entire African continent (i.e. the biological control of the cassava mealybug) suggests a benefit-cost ratio of management of 200 at minimum (
We argue that efficient strategies towards management require cross-disciplinary and cross-sectoral efforts within and between scientists, decision-makers, stakeholders and civil society (
The adoption and implementation of biosecurity measures appear particularly relevant for African countries where economic capacities are often limited. This is particularly true since many invaders introduced from other continents are also spreading within Africa in unpredictable directions (
Our study provides the first comprehensive overview of the reported economic costs of biological invasions in Africa over the last fifty years. We showed that invasions represent a massive, yet highly underestimated economic burden for African countries, and their reported costs are exponentially increasing over time. We also highlighted crucial, large gaps in the current knowledge on invasion costs that still need to be bridged with more active and widespread research and management across the continent. The cost figures presented in this paper should be seen as a snapshot of the cost information currently available in the updatable "InvaCost" database, rather than definitive cost values (and temporal/spatial distribution of costs). We consider this work a sound basis for improving further research on this topic and envision future updates for this first state-of-the-art synthesis of the economic costs of invasions in Africa. Finally, our study provides support for developing and implementing biosecurity measures as well as integrated post-invasion management actions at both national and regional levels. Taking into account the complex societal and economic realities of African countries, the currently neglected problem of invasions should be dealt with using holistic and sustainable approaches. Indeed, beyond their economic impacts, invasions also have substantial impacts on biodiversity, human health and food security. Therefore, we advocate for (i) an increase in societal awareness on biological invasions through improved science-society interactions on this topic and (ii) the systematic inclusion of invasion costs in the development of regulations and actions targeting invasive species in Africa.
We are extremely grateful to the whole team that contributed to organize the "InvaCost" workshop which allowed the genesis of this project. We are particularly indebted to the following people for translating the abstract to local languages: Solimane Ag-Atteynine (Bamanan kan and Tamasheq), Khalilou Bâ (Puular), Sjirk Geerts (Afrikaans), Gustavo Heringer (Portuguese), Karmadine Hima (Haussa), Voahangy Soarimalala (Malagasy), Yonas Meheretu (Amharic), Menzi Msizi Nxumalo and Ntombifuthi Shabalala (isiZulu). We express our gratitude to Liliana Ballesteros-Mejia for her invaluable help with data acquisition and consortium management.
The authors acknowledge the French National Research Agency (ANR-14-CE02-0021) and the BNP-Paribas Foundation Climate Initiative for funding the Invacost project which allowed the construction of the InvaCost database. This work was initiated following a workshop funded by the AXA Research Fund Chair of Invasion Biology and is part of the AlienScenario project funded by BiodivERsA and Belmont-Forum call 2018 on biodiversity scenarios. AN and DM were supported by the Czech Science Foundation (project no. 19–13142S, and EXPRO no. 19–28807X) and the Czech Academy of Sciences (long-term research development project RVO 67985939). EA contract comes from the AXA Research Fund Chair of Invasion Biology of University Paris Saclay. JT was supported by CABI with core financial support from its member countries and lead agencies (see: https://www.cabi.org/what-we-do/how-we-work/cabi-donors-and-partners/). CD was funded by the BiodivERsA-Belmont Forum Project “Alien Scenarios” (BMBF/PT DLR 01LC1807C).
All data used in this study were made fully accessible as suppl. materials (Suppl. material
Starting dataset considered in this study
Data type: database
Explanation note: This database results from the combination of data collated in the "InvaCost" database (
Data collection and filtering processes
Data type: database
Explanation note: (a) Collection of cost information from the version 3.0 of "InvaCost"; (b) extraction of relevant data using the "Geographic region" and "Country" fields to obtain the "starting dataset"; (c) homogenization of cost entries to cost estimates per year expanded over time and (d) selection of the most "conservative subset" using the "Implementation" and "Reliability" variables.
Summary of the descriptive columns of the database used in this study (from
Data type: database
Explanation note: The different columns (i.e. descriptive variables) are italicized and presented in alphabetical order. The categories used for each descriptive variable are put in bold. All fields actually considered in our study are marked with an asterisk.
Conservative subset obtained following specific filtering steps applied to the starting dataset
Data type: database
Explanation note: This dataset only contains estimates that are considered as actually realized and perceived as of high reliability (based on the type of publication and method of estimation). The first spreadsheet (called "Basic data") contains the complete subset focusing on cost data exclusively associated with the African continent. The second spreadsheet (called "Expanded data") contains the expanded version of the complete subset.
Quantitative summary of the cost data and estimates
Data type: database
Explanation note: Quantitative summary of the cost data and estimates for each African country recorded in the "starting dataset" and the "conservative subset" according to their perceived level of reliability (“high” versus “low”) and implementation (“observed” versus “potential”). We used the expanded version of both datasets to provide the total cumulative costs (between 1970 and 2020) in 2017-equivalent US$ billion. N represents the number of cost entries in the datasets. Details about the descriptive fields and their respective categories are provided in the Suppl. material
Categorization of recorded cost data into “damage” costs
Data type: database
Explanation note: Categorization of recorded cost data into “damage”, “management” or “mixed” costs according to criteria considered in
Relative weights of predictor categories in the linear robust regression between cost data and time period
Data type: database
Explanation note: ‘Cost data’ is the response variable: we considered information from the Cost estimate per year USD Exchange rate column in the expanded conservative subset (Suppl. material
Quantitative summary of the costs reported in each African region
Data type: database
Explanation note: Quantitative summary of the costs reported in each African region following the number of expanded cost entries (N), the "method reliability" (“High” or “Low”) and the cost "implementation" (“observed” or “potential”).
Distribution of the reliable observed costs
Data type: database
Explanation note: Distribution of the reliable observed costs (from the conservative subset) following the impacted sectors and type of cost for each reporting African country. The country names are coloured based on the geographical region they belong to as defined by United Nations geoscheme (available at https://unstats.un.org/): “Western Africa”, “Southern Africa”, “Northern Africa”, “Middle Africa”, and “Eastern Africa” (see continental map on the top left corner). For the impacted sectors, we considered the categories proposed by
List of species as well as their cost estimates recorded in our dataset
Data type: database
Explanation note: We provided the total cumulative costs in 2017-equivalent US$ million for 1970-2020 derived from the "starting dataset" (i.e. total cost) and "conservative subset" (i.e. robust cost).
Summary of the outputs from the different models used for analyzing the temporal trend of invasion costs in Africa between 1970 and 2019
Data type: database
Explanation note: Prediction was based on OLS: ordinary least-squares; GAM: generalized additive model; linear regression, quadratic regression, MARS: multiple adaptive regression splines. We considered models calibrated and fitted with at least 75% of cost data completeness from the dataset. Costs are estimated in 2017-equivalent US$ millions. We log10-tranformed cost estimates (from the ‘Cost estimate per year 2017 USD exchange rate’ column in the InvaCost database).