scholarly journals Spatial variability of Chondrichthyes in the northern Mediterranean

2020 ◽  
Vol 83 (S1) ◽  
pp. 81 ◽  
Author(s):  
Maria C. Follesa ◽  
Martina F. Marongiu ◽  
Walter Zupa ◽  
Andrea Bellodi ◽  
Alessandro Cau ◽  
...  
Keyword(s):  
Continental Shelf ◽  
Generalized Additive Models ◽  
Regional Scale ◽  
Additive Models ◽  
Environmental Drivers ◽  
Shark Species ◽  
Fishing Pressure ◽  
The Mediterranean ◽  
High Level ◽  
Management And Conservation

Thanks to the availability of the MEDITS survey data, a standardized picture of the occurrence and abundance of demersal Chondrichthyes in the northern Mediterranean has been obtained. During the spring-summer period between 2012 and 2015, 41 Chondrichthyes, including 18 sharks (5 orders and 11 families), 22 batoids (3 orders and 4 families) and 1 chimaera, were detected from several geographical sub-areas (GSAs) established by the General Fisheries Commission for the Mediterranean. Batoids had a preferential distribution on the continental shelf (10-200 m depth), while shark species were more frequent on the slope (200-800 m depth). Only three species, the Carcharhiniformes Galeus melastomus and Scyliorhinus canicula and the Torpediniformes Torpedo marmorata were caught in all GSAs studied. On the continental shelf, the Rajidae family was the most abundant, being represented in primis by Raja clavata and then by R. miraletus, R. polystigma and R. asterias. The slope was characterized by the prevalence of G. melastomus in all GSAs, followed by S. canicula, E. spinax and Squalus blainville. Areas under higher fishing pressure, such as the Adriatic Sea and the Spanish coast (with the exception of the Balearic Islands), show a low abundance of chondrichthyans, but other areas with a high level of fishing pressure, such as southwestern Sicily, show a high abundance, suggesting that other environmental drivers work together with fishing pressure to shape their distribution. Results of generalized additive models highlighted that depth is one of the most important environmental drivers influencing the distribution of both batoid and shark species, although temperature also showed a significant influence on their distribution. The approach explored in this work shows the possibility of producing maps modelling the distribution of demersal chondrichthyans in the Mediterranean that are useful for the management and conservation of these species at a regional scale. However, because of the vulnerability of these species to fishing exploitation, fishing pressure should be further incorporated in these models in addition to these environmental drivers.

scholarly journals Present and LGM permafrost from climate simulations: contribution of statistical downscaling

2010 ◽  
Vol 4 (4) ◽  
pp. 2233-2275 ◽  
Author(s):  
G. Levavasseur ◽  
M. Vrac ◽  
D. M. Roche ◽  
D. Paillard ◽  
A. Martin ◽  
...  
Keyword(s):  
Statistical Downscaling ◽  
Large Scale ◽  
Climate Models ◽  
Multinomial Logistic Regression ◽  
Generalized Additive Models ◽  
Regional Scale ◽  
Surface Air Temperature ◽  
Additive Models ◽  
Simple Relationship ◽  
Time Period

Abstract. We quantify the agreement between permafrost distributions from PMIP2 (Paleoclimate Modeling Intercomparison Project) climate models and permafrost data. We evaluate the ability of several climate models to represent permafrost and assess the inter-variability between them. Studying an heterogeneous variable such as permafrost implies to conduct analysis at a smaller spatial scale compared with climate models resolution. Our approach consists in applying statistical downscaling methods (SDMs) on large- or regional-scale atmospheric variables provided by climate models, leading to local permafrost modelling. Among the SDMs, we first choose a transfer function approach based on Generalized Additive Models (GAMs) to produce high-resolution climatology of surface air temperature (SAT). Then, we define permafrost distribution over Eurasia by SAT conditions. In a first validation step on present climate (CTRL period), GAM shows some limitations with non-systemic improvements in comparison with the large-scale fields. So, we develop an alternative method of statistical downscaling based on a stochastic generator approach through a Multinomial Logistic Regression (MLR), which directly models the probabilities of local permafrost indices. The obtained permafrost distributions appear in a better agreement with data. In both cases, the provided local information reduces the inter-variability between climate models. Nevertheless, this also proves that a simple relationship between permafrost and the SAT only is not always sufficient to represent local permafrost. Finally, we apply each method on a very different climate, the Last Glacial Maximum (LGM) time period, in order to quantify the ability of climate models to represent LGM permafrost. Our SDMs do not significantly improve permafrost distribution and do not reduce the inter-variability between climate models, at this period. We show that LGM permafrost distribution from climate models strongly depends on large-scale SAT. The differences with LGM data, larger than in the CTRL period, reduce the contribution of downscaling and depend on several factors deserving further studies.

scholarly journals Sea pens in the Mediterranean Sea: habitat suitability and opportunities for ecosystem recovery

2018 ◽  
Vol 75 (5) ◽  
pp. 1722-1732 ◽  
Author(s):  
Azzurra Bastari ◽  
Daniela Pica ◽  
Francesco Ferretti ◽  
Fiorenza Micheli ◽  
Carlo Cerrano
Keyword(s):  
Mediterranean Sea ◽  
Habitat Suitability ◽  
Generalized Additive Models ◽  
Additive Models ◽  
Suitable Habitat ◽  
Ecosystem Recovery ◽  
The Mediterranean ◽  
Suitable Habitats ◽  
Vulnerable Habitats ◽  
Available Information

Abstract The aim of this study is to synthesize available information on sea pens in the Mediterranean Sea and fill existing knowledge gaps through modelling of suitable habitat, with the overarching goal of informing strategies for protecting sea pen habitats from trawling impacts and facilitating their recovery. A review spanning the last 30 years was conducted to map the distribution of Mediterranean sea pen species. In the Adriatic Sea, presence–absence data were modelled with generalized additive models (GAMs) to identify potentially suitable habitats for Funiculina quadrangularis, Virgularia mirabilis, and Pennatula spp. Results show that sea pen distribution in the Mediterranean is mainly limited to continental northern shelves. Six species have been recorded throughout the Adriatic basin, where habitat suitability models confirm that its soft bottoms yield favourable conditions for sea pen assemblages. This information can help guide strategies for diminishing and reversing the impacts of bottom trawling on these vulnerable habitats.

scholarly journals Regulation of carbon dioxide and methane in small agricultural reservoirs: Optimizing potential for greenhouse gas uptake

2019 ◽  
Author(s):  
Jackie R. Webb ◽  
Peter R. Leavitt ◽  
Gavin L. Simpson ◽  
Helen Baulch ◽  
Heather A. Haig ◽  
...  
Keyword(s):  
Carbon Dioxide ◽  
Greenhouse Gas ◽  
Generalized Additive Models ◽  
Regional Scale ◽  
Positive Association ◽  
Co2 Concentration ◽  
Agricultural Landscapes ◽  
Additive Models ◽  
Small Farm ◽  
Environmental Controls

Abstract. Small farm reservoirs are abundant in many agricultural regions across the globe and have the potential to be large contributing sources of carbon dioxide (CO2) and methane (CH4) to agricultural landscapes. Compared to natural ponds, these artificial waterbodies remain overlooked in both agricultural greenhouse gas (GHG) inventories and inland water global carbon (C) budgets. Improved understanding of the environmental controls of C emissions from farm reservoirs is required to address and manage their potential importance. Here, we conducted a regional scale survey (~ 235,000 km2) to measure CO2 and CH4 concentrations and diffusive fluxes across 101 small farm reservoirs in Canada's largest agricultural area. A combination of abiotic, biotic, hydromorphologic, and landscape variables were modelled using generalized additive models (GAMs) to identify regulatory mechanisms. We found that CO2 concentration was best estimated by a combination of internal metabolism and groundwater-derived alkalinity (65.7 % deviance explained), while multiple lines of evidence support a positive association between eutrophication and CH4 production (74.1 % deviance explained). Fluxes ranged from −21 to 466 and 0.14 to 92 mmol m−2 d−1 for CO2 and CH4, respectively, with CH4 contributing an average of 74% of CO2-equivalent (CO2-e) emissions. Approximately 19 % farm reservoirs were found to be net CO2-e sinks. From our models, we show that the GHG impact of farm reservoirs can be greatly minimised through overall improvements in water quality and the construction and maintenance of deeper reservoirs.

scholarly journals Defining trends and thresholds in responses of ecological indicators to fishing and environmental pressures

2013 ◽  
Vol 70 (4) ◽  
pp. 755-767 ◽  
Author(s):  
Scott I. Large ◽  
Gavin Fay ◽  
Kevin D. Friedland ◽  
Jason S. Link
Keyword(s):  
Generalized Additive Models ◽  
Marine Ecosystem ◽  
Parametric Bootstrap ◽  
Ecological Indicators ◽  
Marine Ecosystems ◽  
Additive Models ◽  
Ecosystem Change ◽  
Fishing Pressure ◽  
Reference Levels ◽  
Environmental Pressures

Abstract Large, S. I., Fay, G., Friedland, K. D., and Link, J. S. 2013. Defining trends and thresholds in responses of ecological indicators to fishing and environmental pressures. – ICES Journal of Marine Science, 70: 755–767. Both fishing and environmental forces can influence the structure of marine ecosystems. To further understand marine ecosystems and to implement ecosystem-based fisheries management (EBFM), an evaluation of ecosystem indicators is warranted. In this context, it is particularly important to identify thresholds where fishing and environmental pressures significantly influence ecological indicators. We empirically determined numerical values of environmental forces and fishing pressure that significantly altered the response of ecological indicators for the Northeast Shelf Large Marine Ecosystem. Generalized additive models predicted a non-linear relationship for each pressure–response pairing. With this smoother, 95% confidence intervals (CI) for estimated first and second derivatives for each relationship were determined via parametric bootstrap. A significant trend or threshold was noted when the CI for the first or second derivative was greater or less than zero, delineating the level at which pressure variables influence the rate and direction of ecosystem indicator responses. We identify reference levels where environmental forces and fishing pressure result in ecosystem change by collectively examining the responses of multiple ecological indicators. Individual indicators showed unique responses to pressures, however, similar values for the pressures were associated with significant changes for multiple indicators. These reference levels establish a foundation for implementation of EBFM.

Comparative species abundance modeling of Capitellidae (Annelida) in Tampa Bay, Florida, USA

2020 ◽  
Vol 653 ◽  
pp. 105-119
Author(s):  
J Hilliard ◽  
D Karlen ◽  
T Dix ◽  
S Markham ◽  
A Schulze
Keyword(s):  
Negative Binomial ◽  
Generalized Additive Models ◽  
Species Abundance ◽  
Single Species ◽  
Tampa Bay ◽  
Additive Models ◽  
Boosted Regression Trees ◽  
Environmental Drivers ◽  
Hurdle Models ◽  
Abundance Modeling

Capitellid polychaetes are ubiquitous throughout the world’s oceans and are often encountered in high abundance. We used an extensive dataset of species abundance and distribution records of the Capitella capitata complex, C. aciculata, C. jonesi, Heteromastus filiformis, Mediomastus ambiseta, and M. californiensis from Tampa Bay, Florida, USA, as a model system of closely related species filling a similar ecological niche. We sought to (1) characterize the spatial distribution of each species, (2) determine if a single species abundance modeling strategy could be applied to them all, and (3) assess environmental drivers of species distribution and abundance. We found that all species had a zero-inflated abundance distribution and there was spatial autocorrelation by bay regions. Lorenz curves were an effective tool to assess spatial patterns of species abundance across large areas. Bay segment, depth, and dissolved oxygen were the most important environmental drivers. Modeling was accomplished by comparing 6 different approaches: 4 generalized additive models (GAMs: Poisson, negative binomial, Tweedie, and zero-inflated Poisson distributions), hurdle models, and boosted regression trees. There was no single model with top performance for every species. However, GAM-Tweedie and hurdle models performed well overall and may be useful for studies of other benthic marine invertebrates.

scholarly journals Present and LGM permafrost from climate simulations: contribution of statistical downscaling

2011 ◽  
Vol 7 (4) ◽  
pp. 1225-1246 ◽  
Author(s):  
G. Levavasseur ◽  
M. Vrac ◽  
D. M. Roche ◽  
D. Paillard ◽  
A. Martin ◽  
...  
Keyword(s):  
Air Temperature ◽  
Statistical Downscaling ◽  
Large Scale ◽  
Climate Models ◽  
Generalized Additive Models ◽  
Regional Scale ◽  
Local Scale ◽  
Additive Models ◽  
Simple Relationship ◽  
Temperature Conditions

Abstract. We quantify the agreement between permafrost distributions from PMIP2 (Paleoclimate Modeling Intercomparison Project) climate models and permafrost data. We evaluate the ability of several climate models to represent permafrost and assess the variability between their results. Studying a heterogeneous variable such as permafrost implies conducting analysis at a smaller spatial scale compared with climate models resolution. Our approach consists of applying statistical downscaling methods (SDMs) on large- or regional-scale atmospheric variables provided by climate models, leading to local-scale permafrost modelling. Among the SDMs, we first choose a transfer function approach based on Generalized Additive Models (GAMs) to produce high-resolution climatology of air temperature at the surface. Then we define permafrost distribution over Eurasia by air temperature conditions. In a first validation step on present climate (CTRL period), this method shows some limitations with non-systematic improvements in comparison with the large-scale fields. So, we develop an alternative method of statistical downscaling based on a Multinomial Logistic GAM (ML-GAM), which directly predicts the occurrence probabilities of local-scale permafrost. The obtained permafrost distributions appear in a better agreement with CTRL data. In average for the nine PMIP2 models, we measure a global agreement with CTRL permafrost data that is better when using ML-GAM than when applying the GAM method with air temperature conditions. In both cases, the provided local information reduces the variability between climate models results. This also confirms that a simple relationship between permafrost and the air temperature only is not always sufficient to represent local-scale permafrost. Finally, we apply each method on a very different climate, the Last Glacial Maximum (LGM) time period, in order to quantify the ability of climate models to represent LGM permafrost. The prediction of the SDMs (GAM and ML-GAM) is not significantly in better agreement with LGM permafrost data than large-scale fields. At the LGM, both methods do not reduce the variability between climate models results. We show that LGM permafrost distribution from climate models strongly depends on large-scale air temperature at the surface. LGM simulations from climate models lead to larger differences with LGM data than in the CTRL period. These differences reduce the contribution of downscaling.

scholarly journals Environmental drivers of plant assemblages: are there differences between palustrine and lacustrine wetlands? A case study from the northern Apennines (Italy)

2019 ◽  
pp. 34 ◽  
Author(s):  
Claudia Angiolini ◽  
Daniele Viciani ◽  
Gianmaria Bonari ◽  
Antonio Zoccola ◽  
Alessandro Bottacci ◽  
...  
Keyword(s):  
Plant Species ◽  
Environmental Control ◽  
Generalized Additive Models ◽  
Central Italy ◽  
Floristic Composition ◽  
Northern Apennines ◽  
Additive Models ◽  
Environmental Drivers ◽  
Species Assemblages ◽  
Indirect Ordination

Mountain wetlands are among the most vulnerable habitats in the Mediterranean basin. Their conservation requires knowledge of plant species assemblages and their environmental drivers. In this study, we investigated what the main environmental factors driving species composition in mountain wetlands are. Differences in environmental control and floristic composition between palustrine and lacustrine wetlands were explored. We used a dataset of 168 vegetation plots (relevés), sampled at 45 mountain wetlands in the northern Apennines (central Italy). Direct ordination showed that water depth, geology type and altitude were the main factors responsible for species distribution. The most important gradient was linked to soil moisture, with hygrophilous species increasing with moisture levels. Indicator Species Analysis underlined a clear distinction in the distribution of aquatic plants between wetland subsystems. Geology and rainfall affected species assemblages in lacustrine and palustrine subsystems. Indirect ordination and Generalized Additive Models revealed that plant species and their attributes significantly changed in the wetland subsystems with an increase in hydrophytes with increasing rainfall in palustrine wetlands and a decrease in thermophilous species along an altitudinal gradient in lacustrine wetlands. Management and conservation guidelines for northern Apennines wetlands are suggested.

scholarly journals Large-scale distribution of a deep-sea megafauna community along Mediterranean trawlable grounds

2020 ◽  
Vol 83 (S1) ◽  
pp. 175
Author(s):  
Ulla Fernandez-Arcaya ◽  
Isabella Bitetto ◽  
Antonio Esteban ◽  
M. Teresa Farriols ◽  
Cristina García-Ruiz ◽  
...  
Keyword(s):  
Large Scale ◽  
Generalized Additive Models ◽  
General Pattern ◽  
Additive Models ◽  
Community Studies ◽  
Northern Spain ◽  
Significant Difference ◽  
Fishery Exploitation ◽  
The Mediterranean ◽  
Composition Structure

The large-scale distribution pattern of megafauna communities along the Mediterranean middle slope was explored. The study was conducted between 500 and 800 m depth where deep-water fishery occurs. Although community studies carried out deeper than 500 m are partly available for some geographic areas, few large-scale comparative studies have been carried out. Within the framework of the MEDITS survey programme, we compared the megafauna community structure in ten geographical sub-areas (GSAs) along the Mediterranean coasts. Additionally, the spatial distribution of fishing was analysed using vessel monitoring by satellite information. Overall, the community showed a significant difference between sub-areas, with a decreasing eastward pattern in abundance and biomass. Longitude was the main factor explaining variation among sub-areas (by generalized additive models). However, we found a region which did not follow the general pattern. GSA 6 (northern Spain) showed significantly lower abundance and a different composition structure to the adjacent areas. The decrease in community descriptors (i.e. abundance and biomass) in this area is probably a symptom of population changes induced by intense fishery exploitation. Overall, a combination of environmental variables and human-induced impacts appears to influence the bentho-pelagic communities along the slope areas of the Mediterranean.

scholarly journals Predicting Malaria Transmission Dynamics in Dangassa, Mali: A Novel Approach Using Functional Generalized Additive Models

2020 ◽  
Vol 17 (17) ◽  
pp. 6339
Author(s):  
François Freddy Ateba ◽  
Manuel Febrero-Bande ◽  
Issaka Sagara ◽  
Nafomon Sogoba ◽  
Mahamoudou Touré ◽  
...  
Keyword(s):  
Wind Speed ◽  
Malaria Incidence ◽  
Generalized Additive Models ◽  
Community Health Center ◽  
Additive Model ◽  
Additive Models ◽  
Functional Regression ◽  
Novel Approach ◽  
Malaria Incidence Rate ◽  
Mean Wind Speed

Mali aims to reach the pre-elimination stage of malaria by the next decade. This study used functional regression models to predict the incidence of malaria as a function of past meteorological patterns to better prevent and to act proactively against impending malaria outbreaks. All data were collected over a five-year period (2012–2017) from 1400 persons who sought treatment at Dangassa’s community health center. Rainfall, temperature, humidity, and wind speed variables were collected. Functional Generalized Spectral Additive Model (FGSAM), Functional Generalized Linear Model (FGLM), and Functional Generalized Kernel Additive Model (FGKAM) were used to predict malaria incidence as a function of the pattern of meteorological indicators over a continuum of the 18 weeks preceding the week of interest. Their respective outcomes were compared in terms of predictive abilities. The results showed that (1) the highest malaria incidence rate occurred in the village 10 to 12 weeks after we observed a pattern of air humidity levels >65%, combined with two or more consecutive rain episodes and a mean wind speed <1.8 m/s; (2) among the three models, the FGLM obtained the best results in terms of prediction; and (3) FGSAM was shown to be a good compromise between FGLM and FGKAM in terms of flexibility and simplicity. The models showed that some meteorological conditions may provide a basis for detection of future outbreaks of malaria. The models developed in this paper are useful for implementing preventive strategies using past meteorological and past malaria incidence.

Sign in / Sign up

Export Citation Format

Share Document