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1.
Environ Monit Assess ; 193(11): 696, 2021 Oct 07.
Artigo em Inglês | MEDLINE | ID: mdl-34618253

RESUMO

The Shapour River, with a catchment area of 4254 km2, is a major river system in southern Iran. While the upstream river flow (the upper Shapour River) is fresh, it becomes extremely salinized at the downstream confluence of the Shekastian saline tributary and the entering nearby Boushigan saline spring. Then, the river passes via the Khesht plain and finally discharges into Raeisali-Delvari storage dam which went into operation in 2009. Over the 2006-2019 period, reduced precipitation and over-utilization of freshwater resources resulted in ~ 72% streamflow reduction in the Shapour River. Due to not using the saline waters for irrigation, drinking, and industrial purposes, the ratio of saline-outflow of Shekastian tributary and Boushigan spring to fresh-outflow of upper Shapour River increased by ~ 3 times; consequently, river salinity fluctuation domain at the Khesht plain inlet dramatically increased from 2.1-4.0 dS m-1 to 3.7-26.0 dS m-1. It resulted in major economic damages to the agricultural sector of middle Shapour River. On the seasonal timescale, consecutive processes of salt accumulation during irrigation season of the Khesht plain date orchards and then salt drainage during the rainy season have adjusted salinity fluctuation domain from 3.7-26.0 dS m-1 at the plain inlet to 5.2-8.9 dS m-1 at the plain outlet. In the lower Shapour River, storage/mixing of fresh/saline inflow waters in the Raeisali-Delvari reservoir has adjusted strong river salinity fluctuation domain from 0.9-10.7 dS m-1 at the reservoir inlet to 3.6-5.5 dS m-1 at the reservoir outlet. The success of the Raeisali-Delvari reservoir for salinity adjustment is due to its suitable location on the Shapour River, by being situated downstream of all main river tributaries with natural saline/fresh sources of water.


Assuntos
Monitoramento Ambiental , Rios , Salinidade , Atividades Humanas , Irã (Geográfico) , Águas Salinas
2.
Sensors (Basel) ; 21(19)2021 Sep 26.
Artigo em Inglês | MEDLINE | ID: mdl-34640754

RESUMO

The study of human activity recognition (HAR) plays an important role in many areas such as healthcare, entertainment, sports, and smart homes. With the development of wearable electronics and wireless communication technologies, activity recognition using inertial sensors from ubiquitous smart mobile devices has drawn wide attention and become a research hotspot. Before recognition, the sensor signals are typically preprocessed and segmented, and then representative features are extracted and selected based on them. Considering the issues of limited resources of wearable devices and the curse of dimensionality, it is vital to generate the best feature combination which maximizes the performance and efficiency of the following mapping from feature subsets to activities. In this paper, we propose to integrate bee swarm optimization (BSO) with a deep Q-network to perform feature selection and present a hybrid feature selection methodology, BAROQUE, on basis of these two schemes. Following the wrapper approach, BAROQUE leverages the appealing properties from BSO and the multi-agent deep Q-network (DQN) to determine feature subsets and adopts a classifier to evaluate these solutions. In BAROQUE, the BSO is employed to strike a balance between exploitation and exploration for the search of feature space, while the DQN takes advantage of the merits of reinforcement learning to make the local search process more adaptive and more efficient. Extensive experiments were conducted on some benchmark datasets collected by smartphones or smartwatches, and the metrics were compared with those of BSO, DQN, and some other previously published methods. The results show that BAROQUE achieves an accuracy of 98.41% for the UCI-HAR dataset and takes less time to converge to a good solution than other methods, such as CFS, SFFS, and Relief-F, yielding quite promising results in terms of accuracy and efficiency.


Assuntos
Atividades Humanas , Dispositivos Eletrônicos Vestíveis , Animais , Abelhas , Smartphone
3.
Sensors (Basel) ; 21(19)2021 Sep 30.
Artigo em Inglês | MEDLINE | ID: mdl-34640886

RESUMO

Wearable sensors are widely used in activity recognition (AR) tasks with broad applicability in health and well-being, sports, geriatric care, etc. Deep learning (DL) has been at the forefront of progress in activity classification with wearable sensors. However, most state-of-the-art DL models used for AR are trained to discriminate different activity classes at high accuracy, not considering the confidence calibration of predictive output of those models. This results in probabilistic estimates that might not capture the true likelihood and is thus unreliable. In practice, it tends to produce overconfident estimates. In this paper, the problem is addressed by proposing deep time ensembles, a novel ensembling method capable of producing calibrated confidence estimates from neural network architectures. In particular, the method trains an ensemble of network models with temporal sequences extracted by varying the window size over the input time series and averaging the predictive output. The method is evaluated on four different benchmark HAR datasets and three different neural network architectures. Across all the datasets and architectures, our method shows an improvement in calibration by reducing the expected calibration error (ECE)by at least 40%, thereby providing superior likelihood estimates. In addition to providing reliable predictions our method also outperforms the state-of-the-art classification results in the WISDM, UCI HAR, and PAMAP2 datasets and performs as good as the state-of-the-art in the Skoda dataset.


Assuntos
Atividades Humanas , Redes Neurais de Computação , Idoso , Humanos , Probabilidade , Reconhecimento Psicológico , Projetos de Pesquisa
4.
Nature ; 598(7880): 257, 2021 10.
Artigo em Inglês | MEDLINE | ID: mdl-34642473
5.
Nature ; 598(7879): 82-85, 2021 10.
Artigo em Inglês | MEDLINE | ID: mdl-34616056

RESUMO

New Zealand was among the last habitable places on earth to be colonized by humans1. Charcoal records indicate that wildfires were rare prior to colonization and widespread following the 13th- to 14th-century Maori settlement2, but the precise timing and magnitude of associated biomass-burning emissions are unknown1,3, as are effects on light-absorbing black carbon aerosol concentrations over the pristine Southern Ocean and Antarctica4. Here we used an array of well-dated Antarctic ice-core records to show that while black carbon deposition rates were stable over continental Antarctica during the past two millennia, they were approximately threefold higher over the northern Antarctic Peninsula during the past 700 years. Aerosol modelling5 demonstrates that the observed deposition could result only from increased emissions poleward of 40° S-implicating fires in Tasmania, New Zealand and Patagonia-but only New Zealand palaeofire records indicate coincident increases. Rapid deposition increases started in 1297 (±30 s.d.) in the northern Antarctic Peninsula, consistent with the late 13th-century Maori settlement and New Zealand black carbon emissions of 36 (±21 2 s.d.) Gg y-1 during peak deposition in the 16th century. While charcoal and pollen records suggest earlier, climate-modulated burning in Tasmania and southern Patagonia6,7, deposition in Antarctica shows that black carbon emissions from burning in New Zealand dwarfed other preindustrial emissions in these regions during the past 2,000 years, providing clear evidence of large-scale environmental effects associated with early human activities across the remote Southern Hemisphere.


Assuntos
Incêndios/história , Atividades Humanas/história , Grupo com Ancestrais Oceânicos/história , Fuligem/análise , Atmosfera/química , Biomassa , História do Século XV , História do Século XVI , História Medieval , Humanos , Nova Zelândia , Tasmânia
6.
Ying Yong Sheng Tai Xue Bao ; 32(8): 2895-2905, 2021 Aug.
Artigo em Chinês | MEDLINE | ID: mdl-34664463

RESUMO

Based on the MODIS NDVI data from 2000 to 2018, we estimated the fractional vegetation cover (FVC) using the dimidiate pixel model and analyzed the spatiotemporal characteristics of FVC in the Beijing-Tianjin sand source region (BTSSR). The geographical detector model was used to estimate the impacts of natural and human factors on FVC spatial distribution at the regional scale. The results showed that the FVC of the BBTSR showed an increasing trend from 2000 to 2018, with an annual growth rate of 0.013·(10 a)-1 and a vegetation increase rate of 8.2%. The area with high FVC was concentrated in the Yanshan Mountain water source protection area, followed by the pastoral transitional zone desertified land control area and the Otindag sandy land area. The area with poor FVC was concentrated in the northern arid grassland area. The explanatory power of driving factors to FVC varied across different regions. Among the natural factors, annual precipitation was the main driving factor for the spatial distribution of FVC in the northern arid grassland area, the Otindag sandy land area and the Yanshan Mountain water source protection area. Slope was the main driving factor for the spatial distribution of FVC in the pastoral transitional zone desertified land control area. Among different human activities, the number of large livestock at the year-end was the main driving factor controlling the spatial distribution of FVC in the northern arid grassland area and the pastoral transitional zone desertified land control area, while population density was the main driving factor controlling the spatial distribution of FVC in the Otindag sandy land area and the Yanshan Mountain water source protection area. There were regional differences in the influen-ce of other factors on FVC spatial distribution. The results of the interaction detector showed that the two-factor interactions were mainly the double-synergy and nonlinear synergy. The interaction of human activities with annual precipitation and slope could more fully explain the spatial variations of FVC. The range of suitable vegetation growth identified by the risk detector was the area with annual precipitation of 316.4-486.0 mm, average relative humidity of 48.4%-57.6%, and average annual temperature of 2.5-7.9 ℃, while other driving factors were different in different zones.


Assuntos
Ecossistema , Areia , Pequim , China , Atividades Humanas , Humanos
7.
Ying Yong Sheng Tai Xue Bao ; 32(8): 2906-2914, 2021 Aug.
Artigo em Chinês | MEDLINE | ID: mdl-34664464

RESUMO

In order to clarify the eco-environmental quality and its evolution characteristics of Keluke Lake basin, we selected 15 factors of physical geography, meteorology, land use/cover and social economy using comprehensive investigation, remote sensing interpretation and inversion, statistical analysis and other technical means, based on the relevant theories of environmental ecology. We used factor analysis and entropy method to calculate the index weight, constructed watershed soil quality model and ecological environment quality diagnosis model, and analyzed the changes of soil and eco-environmental quality in the Keluke Lake basin in 2000, 2005, 2010 and 2015. The results showed that the average eco-environmental quality in four periods was 21, 47, 54, and 72, showing a stable upward trend. The eco-environmental quality level changed from poor to good, while soil quality was at the middle level. Spatially, the eco-environmental quality of the northern mountainous area, the downstream wetland and the surrounding area of the river improved significantly. The change of eco-environmental quality was a result of human activities and natural factors. Soil quality and lake area were key factors indicating the eco-environment of the Keluke Lake basin. The minimum ecological water demand of the Keluke Lake was the basic guarantee to maintain the benign development of the eco-environment of the lake basin.


Assuntos
Lagos , Rios , China , Atividades Humanas , Humanos , Solo
8.
Ying Yong Sheng Tai Xue Bao ; 32(8): 2915-2922, 2021 Aug.
Artigo em Chinês | MEDLINE | ID: mdl-34664465

RESUMO

Human activity intensity is mostly used to quantify the degree of human influence on natural systems, with obvious spatial variability. With Lashihai watershed in Yunnan Province as an example, we used SPOT remote sensing images to update land use data, and obtained a comprehensive index of land use intensity after gridding by assigning weights to different land types, which was considered as the basic human activity intensity. The local tourism activities (horseback riding and boating) were also included. The horseback riding and boating were spatially quantified according to the location of horse farms and the abundance of horses and boats which were superimposed with the basic human activity intensity on the spatial scale of 100 m×100 m to obtain a more accurate comprehensive human activity intensity and to analyze the spatial variations. The results showed that the gridding and the kernel density analysis improved the accuracy of spatial analysis and reflected the spatial superposition and diffusion effects. In the comprehensive human activity intensity map of Lashihai watershed, the highest intensity value of water area was at the mouth of the sea, the lowest intensity value was at the center of the sea, and the overall trend of intensity gradually decreased from the surrounding to the middle. The land settlement had the highest intensity, the intensity value of the agricultural land gathering area was at the middle level, and the intensity of human activities in the forestry area of higher altitude was lower. The comprehensive human activity intensity in the water area of the Lashihai watershed varied most obviously, and differed greatly from the basic human activity intensity. Although there were many local characteristic tourism activities in Yunnan-Guizhou Plateau Wetland scenic area, but their land use types did not change. We need to take them into account when quantifying the intensity of human activities.


Assuntos
Monitoramento Ambiental , Áreas Alagadas , Animais , China , Cavalos , Atividades Humanas , Análise Espacial
9.
Sensors (Basel) ; 21(18)2021 Sep 09.
Artigo em Inglês | MEDLINE | ID: mdl-34577243

RESUMO

Recent advances in Internet of Things (IoT) technologies and the reduction in the cost of sensors have encouraged the development of smart environments, such as smart homes. Smart homes can offer home assistance services to improve the quality of life, autonomy, and health of their residents, especially for the elderly and dependent. To provide such services, a smart home must be able to understand the daily activities of its residents. Techniques for recognizing human activity in smart homes are advancing daily. However, new challenges are emerging every day. In this paper, we present recent algorithms, works, challenges, and taxonomy of the field of human activity recognition in a smart home through ambient sensors. Moreover, since activity recognition in smart homes is a young field, we raise specific problems, as well as missing and needed contributions. However, we also propose directions, research opportunities, and solutions to accelerate advances in this field.


Assuntos
Aprendizado Profundo , Internet das Coisas , Idoso , Algoritmos , Atividades Humanas , Humanos , Qualidade de Vida
10.
Sensors (Basel) ; 21(18)2021 Sep 16.
Artigo em Inglês | MEDLINE | ID: mdl-34577418

RESUMO

The number of smart homes is rapidly increasing. Smart homes typically feature functions such as voice-activated functions, automation, monitoring, and tracking events. Besides comfort and convenience, the integration of smart home functionality with data processing methods can provide valuable information about the well-being of the smart home residence. This study is aimed at taking the data analysis within smart homes beyond occupancy monitoring and fall detection. This work uses a multilayer perceptron neural network to recognize multiple human activities from wrist- and ankle-worn devices. The developed models show very high recognition accuracy across all activity classes. The cross-validation results indicate accuracy levels above 98% across all models, and scoring evaluation methods only resulted in an average accuracy reduction of 10%.


Assuntos
Atividades Humanas , Redes Neurais de Computação , Automação , Habitação , Humanos , Punho
11.
Sensors (Basel) ; 21(18)2021 Sep 21.
Artigo em Inglês | MEDLINE | ID: mdl-34577515

RESUMO

Human activity recognition is an extensively researched topic in the last decade. Recent methods employ supervised and unsupervised deep learning techniques in which spatial and temporal dependency is modeled. This paper proposes a novel approach for human activity recognition using skeleton data. The method combines supervised and unsupervised learning algorithms in order to provide qualitative results and performance in real time. The proposed method involves a two-stage framework: the first stage applies an unsupervised clustering technique to group up activities based on their similarity, while the second stage classifies data assigned to each group using graph convolutional networks. Different clustering techniques and data augmentation strategies are explored for improving the training process. The results were compared against the state of the art methods and the proposed model achieved 90.22% Top-1 accuracy performance for NTU-RGB+D dataset (the performance was increased by approximately 9% compared with the baseline graph convolutional method). Moreover, inference time and total number of parameters stay within the same magnitude order. Extending the initial set of activities with additional classes is fast and robust, since there is no required retraining of the entire architecture but only to retrain the cluster to which the activity is assigned.


Assuntos
Redes Neurais de Computação , Aprendizado de Máquina não Supervisionado , Algoritmos , Análise por Conglomerados , Atividades Humanas , Humanos
12.
Sensors (Basel) ; 21(18)2021 Sep 21.
Artigo em Inglês | MEDLINE | ID: mdl-34577526

RESUMO

With the fast increase in the demand for location-based services and the proliferation of smartphones, the topic of indoor localization is attracting great interest. In indoor environments, users' performed activities carry useful semantic information. These activities can then be used by indoor localization systems to confirm users' current relative locations in a building. In this paper, we propose a deep-learning model based on a Convolutional Long Short-Term Memory (ConvLSTM) network to classify human activities within the indoor localization scenario using smartphone inertial sensor data. Results show that the proposed human activity recognition (HAR) model accurately identifies nine types of activities: not moving, walking, running, going up in an elevator, going down in an elevator, walking upstairs, walking downstairs, or going up and down a ramp. Moreover, predicted human activities were integrated within an existing indoor positioning system and evaluated in a multi-story building across several testing routes, with an average positioning error of 2.4 m. The results show that the inclusion of human activity information can reduce the overall localization error of the system and actively contribute to the better identification of floor transitions within a building. The conducted experiments demonstrated promising results and verified the effectiveness of using human activity-related information for indoor localization.


Assuntos
Corrida , Smartphone , Atividades Humanas , Humanos , Caminhada
13.
Sensors (Basel) ; 21(17)2021 Aug 25.
Artigo em Inglês | MEDLINE | ID: mdl-34502609

RESUMO

The emergence of various types of commercial cameras (compact, high resolution, high angle of view, high speed, and high dynamic range, etc.) has contributed significantly to the understanding of human activities. By taking advantage of the characteristic of a high angle of view, this paper demonstrates a system that recognizes micro-behaviors and a small group discussion with a single 360 degree camera towards quantified meeting analysis. We propose a method that recognizes speaking and nodding, which have often been overlooked in existing research, from a video stream of face images and a random forest classifier. The proposed approach was evaluated on our three datasets. In order to create the first and the second datasets, we asked participants to meet physically: 16 sets of five minutes data from 21 unique participants and seven sets of 10 min meeting data from 12 unique participants. The experimental results showed that our approach could detect speaking and nodding with a macro average f1-score of 67.9% in a 10-fold random split cross-validation and a macro average f1-score of 62.5% in a leave-one-participant-out cross-validation. By considering the increased demand for an online meeting due to the COVID-19 pandemic, we also record faces on a screen that are captured by web cameras as the third dataset and discussed the potential and challenges of applying our ideas to virtual video conferences.


Assuntos
Atividades Humanas , Fotografação , COVID-19 , Humanos , Pandemias
15.
Sensors (Basel) ; 21(17)2021 Aug 24.
Artigo em Inglês | MEDLINE | ID: mdl-34502592

RESUMO

Human action recognition in videos has become a popular research area in artificial intelligence (AI) technology. In the past few years, this research has accelerated in areas such as sports, daily activities, kitchen activities, etc., due to developments in the benchmarks proposed for human action recognition datasets in these areas. However, there is little research in the benchmarking datasets for human activity recognition in educational environments. Therefore, we developed a dataset of teacher and student activities to expand the research in the education domain. This paper proposes a new dataset, called EduNet, for a novel approach towards developing human action recognition datasets in classroom environments. EduNet has 20 action classes, containing around 7851 manually annotated clips extracted from YouTube videos, and recorded in an actual classroom environment. Each action category has a minimum of 200 clips, and the total duration is approximately 12 h. To the best of our knowledge, EduNet is the first dataset specially prepared for classroom monitoring for both teacher and student activities. It is also a challenging dataset of actions as it has many clips (and due to the unconstrained nature of the clips). We compared the performance of the EduNet dataset with benchmark video datasets UCF101 and HMDB51 on a standard I3D-ResNet-50 model, which resulted in 72.3% accuracy. The development of a new benchmark dataset for the education domain will benefit future research concerning classroom monitoring systems. The EduNet dataset is a collection of classroom activities from 1 to 12 standard schools.


Assuntos
Algoritmos , Inteligência Artificial , Benchmarking , Atividades Humanas , Humanos
16.
J Environ Manage ; 299: 113449, 2021 Dec 01.
Artigo em Inglês | MEDLINE | ID: mdl-34450301

RESUMO

Biodiversity is declining at an unprecedented rate, and conservation is needed in many places including human-dominated landscapes. Evaluation of conflict risk between biodiversity conservation and human activities is a prerequisite for countries to develop strategies to achieve better conservation outcomes. However, quantitative methods to measure the conflict risk in large-scale areas are still lacking. Here we put forward a quantitative model in large-scale areas and produce the first continuum map of conflict risk in China. Our results show that conflict risk hotspots take up 32.86 % of China's terrestrial area, which may affect 42.98 % of China's population and more than 98 % of threaten vertebrates. Although species richness is high in these hotspot regions, only 10.69 % of them are covered by protected areas. Therefore, alternative conservation measures and proactive spatial planning are needed, especially in regions along the coastlines and around the Sichuan Basin. Especially, extraordinary attentions should be paid to urban agglomerations such as the Pearl River Delta and Yangtze River Delta. Compared to previous studies, our study quantifies the conflict risk of every gird cell, enabling the comparison among any locations. The analysis of 500 times generations shows a low sensitivity of the model as the maximum standard deviation is only 0.017. Furthermore, our model can be applied in other countries or at global scale to provide strategies for conflict governance and biodiversity conservation.


Assuntos
Biodiversidade , Conservação dos Recursos Naturais , Animais , China , Atividades Humanas , Humanos , Rios
17.
Toxins (Basel) ; 13(7)2021 07 08.
Artigo em Inglês | MEDLINE | ID: mdl-34357943

RESUMO

Cyanobacteria are ubiquitous photosynthetic microorganisms considered as important contributors to the formation of Earth's atmosphere and to the process of nitrogen fixation. However, they are also frequently associated with toxic blooms, named cyanobacterial harmful algal blooms (cyanoHABs). This paper reports on an unusual out-of-season cyanoHAB and its dynamics during the COVID-19 pandemic, in Lake Avernus, South Italy. Fast detection strategy (FDS) was used to assess this phenomenon, through the integration of satellite imagery and biomolecular investigation of the environmental samples. Data obtained unveiled a widespread Microcystis sp. bloom in February 2020 (i.e., winter season in Italy), which completely disappeared at the end of the following COVID-19 lockdown, when almost all urban activities were suspended. Due to potential harmfulness of cyanoHABs, crude extracts from the "winter bloom" were evaluated for their cytotoxicity in two different human cell lines, namely normal dermal fibroblasts (NHDF) and breast adenocarcinoma cells (MCF-7). The chloroform extract was shown to exert the highest cytotoxic activity, which has been correlated to the presence of cyanotoxins, i.e., microcystins, micropeptins, anabaenopeptins, and aeruginopeptins, detected by molecular networking analysis of liquid chromatography tandem mass spectrometry (LC-MS/MS) data.


Assuntos
Cianobactérias , Proliferação Nociva de Algas , Lagos/microbiologia , Toxinas Bacterianas/análise , Toxinas Bacterianas/toxicidade , COVID-19/epidemiologia , Linhagem Celular , Sobrevivência Celular/efeitos dos fármacos , Cianobactérias/genética , DNA Bacteriano/análise , Monitoramento Ambiental , Atividades Humanas , Humanos , Itália/epidemiologia , Microcystis , Pandemias , SARS-CoV-2 , Imagens de Satélites
19.
Sci Total Environ ; 799: 149390, 2021 Dec 10.
Artigo em Inglês | MEDLINE | ID: mdl-34358746

RESUMO

As a new type of environmental pollutant, microplastics (MPs) are widely present in freshwater systems. The ecological risks of MPs pollution in nature reserves and the correlation between human activities and the abundance of MPs are still unclear. This is the first survey of MPs in freshwater systems in Northeast China. The content and composition of MPs in 19 water samples were investigated in Chagan lake and Xianghai. The abundance of MPs samples in Chagan Lake averages 3.61 ± 2.23 particles/L, and in Xianghai averages 0.29 ± 0.11 particles/L. The main types of MPs in Chagan Lake are PA (23.7%) and PS (53.2%); while in Xianghai are PP (56%) and PS (32.7%). Foam, white and <1 mm are the main shapes, colors and sizes of Chagan Lake MPs, while of Xianghai are film, transparent and <1 mm. This may be related to the well-developed tourism and fishing industry (foam and fishing line) in Chagan Lake and aquaculture in Xianghai (foam and plastic film). The hazard index (HI) indicated a Hazard Level III for MPs pollution in Chagan Lake and Xianghai. Pollution load index (PLI) and potential ecological risk index (RI) indicate that the pollution risk of MPs polymers in the two places is relatively small. The degree of human activity is quantified to analyze the correlation of MPs abundance. The quantified scores are positively correlated with the abundance of MPs at different sampling points (Chagan lake: P < 0.05, 95% Cl; Xianghai: P < 0.05, 95% Cl).


Assuntos
Microplásticos , Poluentes Químicos da Água , China , Monitoramento Ambiental , Sedimentos Geológicos , Atividades Humanas , Humanos , Lagos , Plásticos , Medição de Risco , Água , Poluentes Químicos da Água/análise
20.
Sci Total Environ ; 799: 149351, 2021 Dec 10.
Artigo em Inglês | MEDLINE | ID: mdl-34371417

RESUMO

Climate change and particularly warming are significantly impacting marine ecosystems and the services they provided. Temperature, as the main factor driving all biological processes, may influence ectotherms metabolism, thermal tolerance limits and distribution species patterns. The joining action of climate change and local stressors (including the increasing human marine use) may facilitate the spread of non-indigenous and native outbreak forming species, leading to associated economic consequences for marine coastal economies. Marine aquaculture is one among the most economic anthropogenic activities threatened by multiple stressors and in turn, by increasing hard artificial substrates at sea would facilitate the expansion of these problematic organisms and face negative consequences regarding facilities management and farmed organisms' welfare. Species Distribution Models (SDMs) are considered powerful tools for forecasting the future occurrences and distributions of problematic species used to preventively aware stakeholders. In the current study, we propose the use of combined correlative SDMs and mechanistic models, based on individual thermal performance curve models calculated through non-linear least squares regression and Bayesian statistics (functional-SDM), as an ecological relevant tool to increase our ability to investigate the potential indirect effect of climate change on the distributions of harmful species for human activities at sea, taking aquaculture as a food productive example and the benthic cnidarian Pennaria disticha (one of the most pernicious fouling species in aquaculture) as model species. Our combined approach was able to improve the prediction ability of both mechanistic and correlative models to get more ecologically informed "whole" niche of the studied species. Incorporating the mechanistic links between the organisms' functional traits and their environments into SDMs through the use of a Bayesian functional-SDM approach would be a useful and reliable tool in early warning ecological systems, risk assessment and management actions focused on important economic activities and natural ecosystems conservation.


Assuntos
Mudança Climática , Ecossistema , Teorema de Bayes , Atividades Humanas , Humanos , Temperatura
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