Resposta da comunidade de vespas e abelhas solitárias (Insecta: Hymenoptera) ao uso da terra

Tropical landscapes are characterized by different land-use types such as agroecosystems and forest remnants with varying anthropization levels. Several species inhabiting forest remnants interact with agroecosystems, but their contribution to biodiversity conservation is unclear. Communities of solitary wasps and bees (Insecta: Hymenoptera) play a key role in agroecosystem functioning and these organisms have been used as bioindicators of environmental quality. Crops benefit from the ecosystem services of pest biological control and pollination provided by solitary wasps and bees, respectively. Additionally, forest fragments in the vicinity of crops can enhance natural biological control and pollination. We evaluated the species richness of solitary wasps and bees over time in a gradient of decreasing land use intensity formed by: pastures; alley croppings; young fallows (8 years old); and old fallows (20 years old). The similarity of wasps and bees‟ communities according to land-use was also evaluated. Moreover, the seasonal variation of the abundance of solitary wasps and bees as well as their communities‟ composition in the four land-use types was studied. The influence of the distance from a forest fragment to a maize field on species richness and abundance of predatory solitary wasps and its relation with the biological control of the fall armyworm (Spodoptera frugiperda Smith) was analysed. Old fallows had higher species richness of wasps and bees in comparison to the remaining land use types. Young fallows, alley croppings and pastures had similar communities of wasps and bees. Population densities of wasps and bees, in general, were not influenced by land use. However, the faunistic composition of some species of solitary wasps and bees varied with land use. The abundance of solitary wasps and bees varied throughout time with peeks in January (bees) and June and July (wasps). Moreover, the temporal variation of wasps‟ abundance (but not bees‟ abundance) was affected by land use. Predatory solitary wasps‟ abundance (but not species richness) decreased, while the fall armyworm abundance increased with increasing distance from the forest fragment. We conclude that solitary bees and wasps‟ species richness is higher in less anthropized environments such as old and young fallows, however, landscapes formed by different land use types including alley croppings and pastures, may provide important resources to maintaining populations of solitary wasps and bees in regions where the original vegetation was entirely removed. Additionally, crops may benefit from the proximity of forest fragments by reduced pest problemsFundação de Amparo à Pesquisa e ao Desenvolvimento Científico e Tecnológico do Maranhão - FAPEMAAs paisagens tropicais são caracterizadas por diversos tipos de uso da terra como agroecossistemas e remanescentes de florestas em diferentes graus de antropização. Diversos organismos que vivem em remanescentes florestais interagem com agroecossistemas, porém a contribuição destas áreas para a conservação da biodiversidade ainda é pouco conhecida. A comunidade de vespas e abelhas solitárias (Insecta: Hymenoptera) tem um papel chave no funcionamento de agroecossistemas e tem sido utilizada como bioindicadora da qualidade ambiental. Cultivos agrícolas se beneficiam dos serviços ambientais de controle biológico de pragas e polinização realizados por vespas e abelhas solitárias, respectivamente. Adicionalmente, fragmentos florestais próximos a cultivos agrícolas podem aumentar o controle biológico natural e a polinização. O presente trabalho avaliou a riqueza de espécies de vespas e abelhas solitárias que nidificam em ninhos-armadilha ao longo do tempo em um gradiente decrescente de intensidade do uso da terra formado por: pastagens; cultivos em aléias; capoeiras novas (8 anos de idade); e capoeiras velhas (20 anos de idade). A similaridade da comunidade de vespas e abelhas em função do uso da terra também foi avaliada. Ademais, a variação sazonal na abundância de vespas e abelhas solitárias bem como a composição da comunidade de tais himenópteros nos quatro tipos de uso da terra foram estudadas. A influência da distância entre um fragmento florestal e um cultivo de milho na riqueza e abundância de vespas predadoras e sua relação com o controle biológico da lagartado-cartucho (Spodoptera frugiperda Smith) foi analisada. Capoeiras velhas tiveram maior riqueza de espécies de vespas e abelhas em comparação com os demais tipos de uso da terra. Capoeiras novas, cultivos em aléias e pastagens apresentaram comunidades similares de vespas e abelhas. A densidade populacional de vespas e abelhas solitárias, de modo geral, não foi influenciada pelo uso da terra. No entanto, a composição faunística de algumas espécies de vespas e abelhas variou com o uso da terra. A densidade populacional de vespas e abelhas variou ao longo do tempo com picos populacionais nos meses de janeiro (abelhas) e junho e julho (vespas). Ademais, a variação temporal na abundância de vespas (mas não de abelhas) foi afetada pelo uso da terra. A abundância de vespas predadoras solitárias (mas não a riqueza de espécies) diminuiu enquanto que a abundância da lagarta-do-cartucho aumentou com a distância do fragmento florestal. Conclui-se que a riqueza de espécies de abelhas e vespas solitárias é maior em ambientes menos antropizados como capoeiras velhas e capoeiras novas, no entanto, paisagens formadas por diferentes tipos de uso da terra, inclusive cultivos em aléia e pastagens, podem aprovisionar recursos importantes para a manutenção de populações de vespas e abelhas solitárias em regiões onde a vegetação original foi completamente removida. Adicionalmente, cultivos agrícolas podem se beneficiar da proximidade de fragmentos florestais por meio da redução de problemas com pragas

Advancing fishery-independent stock assessments for the Norway lobster (Nephrops norvegicus) with new monitoring technologies

The Norway lobster, Nephrops norvegicus, supports a key European fishery. Stock assessments for this species are mostly based on trawling and UnderWater TeleVision (UWTV) surveys. However, N. norvegicus are burrowing organisms and these survey methods are unable to sample or observe individuals in their burrows. To account for this, UWTV surveys generally assume that "1 burrow system = 1 animal", due to the territorial behavior of N. norvegicus. Nevertheless, this assumption still requires in-situ validation. Here, we outline how to improve the accuracy of current stock assessments for N. norvegicus with novel ecological monitoring technologies, including: robotic fixed and mobile camera-platforms, telemetry, environmental DNA (eDNA), and Artificial Intelligence (AI). First, we outline the present status and threat for overexploitation in N. norvegicus stocks. Then, we discuss how the burrowing behavior of N. norvegicus biases current stock assessment methods. We propose that state-of-the-art stationary and mobile robotic platforms endowed with innovative sensors and complemented with AI tools could be used to count both animals and burrows systems in-situ, as well as to provide key insights into burrowing behavior. Next, we illustrate how multiparametric monitoring can be incorporated into assessments of physiology and burrowing behavior. Finally, we develop a flowchart for the appropriate treatment of multiparametric biological and environmental data required to improve current stock assessment methods

Advancing fishery-independent stock assessments for the Norway lobster (Nephrops norvegicus) with new monitoring technologies

The Norway lobster, Nephrops norvegicus, supports a key European fishery. Stock assessments for this species are mostly based on trawling and UnderWater TeleVision (UWTV) surveys. However, N. norvegicus are burrowing organisms and these survey methods are unable to sample or observe individuals in their burrows. To account for this, UWTV surveys generally assume that “1 burrow system = 1 animal”, due to the territorial behavior of N. norvegicus. Nevertheless, this assumption still requires in-situ validation. Here, we outline how to improve the accuracy of current stock assessments for N. norvegicus with novel ecological monitoring technologies, including: robotic fixed and mobile camera-platforms, telemetry, environmental DNA (eDNA), and Artificial Intelligence (AI). First, we outline the present status and threat for overexploitation in N. norvegicus stocks. Then, we discuss how the burrowing behavior of N. norvegicus biases current stock assessment methods. We propose that state-of-the-art stationary and mobile robotic platforms endowed with innovative sensors and complemented with AI tools could be used to count both animals and burrows systems in-situ, as well as to provide key insights into burrowing behavior. Next, we illustrate how multiparametric monitoring can be incorporated into assessments of physiology and burrowing behavior. Finally, we develop a flowchart for the appropriate treatment of multiparametric biological and environmental data required to improve current stock assessment methods

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Search for dark matter produced in association with bottom or top quarks in √s = 13 TeV pp collisions with the ATLAS detector

A search for weakly interacting massive particle dark matter produced in association with bottom or top quarks is presented. Final states containing third-generation quarks and miss- ing transverse momentum are considered. The analysis uses 36.1 fb−1 of proton–proton collision data recorded by the ATLAS experiment at √s = 13 TeV in 2015 and 2016. No significant excess of events above the estimated backgrounds is observed. The results are in- terpreted in the framework of simplified models of spin-0 dark-matter mediators. For colour- neutral spin-0 mediators produced in association with top quarks and decaying into a pair of dark-matter particles, mediator masses below 50 GeV are excluded assuming a dark-matter candidate mass of 1 GeV and unitary couplings. For scalar and pseudoscalar mediators produced in association with bottom quarks, the search sets limits on the production cross- section of 300 times the predicted rate for mediators with masses between 10 and 50 GeV and assuming a dark-matter mass of 1 GeV and unitary coupling. Constraints on colour- charged scalar simplified models are also presented. Assuming a dark-matter particle mass of 35 GeV, mediator particles with mass below 1.1 TeV are excluded for couplings yielding a dark-matter relic density consistent with measurements

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost