Gotowe bibliografie tematyczne / Habitat predictive model

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Gotowa bibliografia na temat „Habitat predictive model”

Autor: Grafiati
Data publikacji: 4 czerwca 2021
Data aktualizacji: 1 lutego 2022

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Artykuły w czasopismach na temat "Habitat predictive model"

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Reisinger, Ryan R., Ari S. Friedlaender, Alexandre N. Zerbini, Daniel M. Palacios, Virginia Andrews-Goff, Luciano Dalla Rosa, Mike Double, et al. "Combining Regional Habitat Selection Models for Large-Scale Prediction: Circumpolar Habitat Selection of Southern Ocean Humpback Whales." Remote Sensing 13, no. 11 (May 25, 2021): 2074. http://dx.doi.org/10.3390/rs13112074.

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Machine learning algorithms are often used to model and predict animal habitat selection—the relationships between animal occurrences and habitat characteristics. For broadly distributed species, habitat selection often varies among populations and regions; thus, it would seem preferable to fit region- or population-specific models of habitat selection for more accurate inference and prediction, rather than fitting large-scale models using pooled data. However, where the aim is to make range-wide predictions, including areas for which there are no existing data or models of habitat selection,
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Meißner, Karin, and Alexander Darr. "Distribution of Magelona species (Polychaeta: Magelonidae) in the German Bight (North Sea): a modeling approach." Zoosymposia 2, no. 1 (August 31, 2009): 567–86. http://dx.doi.org/10.11646/zoosymposia.2.1.39.

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The aim of the present study was the development of species-habitat models for four Magelona species (Polychaeta: Magelonidae) found in the German Bight in the SE North Sea. Analyses were based on field data and data obtained from reexamination of material deposited in museum collections. In addition, data on environmental variables were retrieved from the sediment map by Figge (1981) and from long-term monitoring data sets. The statistical modeling technique applied was multivariate adaptive regression splines (MARS). Predictive accuracy measures were calculated for each model. The candidate
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Enwright, Nicholas M., Lei Wang, Hongqing Wang, Michael J. Osland, Laura C. Feher, Sinéad M. Borchert, and Richard H. Day. "Modeling Barrier Island Habitats Using Landscape Position Information." Remote Sensing 11, no. 8 (April 24, 2019): 976. http://dx.doi.org/10.3390/rs11080976.

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Barrier islands are dynamic environments because of their position along the marine–estuarine interface. Geomorphology influences habitat distribution on barrier islands by regulating exposure to harsh abiotic conditions. Researchers have identified linkages between habitat and landscape position, such as elevation and distance from shore, yet these linkages have not been fully leveraged to develop predictive models. Our aim was to evaluate the performance of commonly used machine learning algorithms, including K-nearest neighbor, support vector machine, and random forest, for predicting barri
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Rice, M. B., A. D. Apa, and L. A. Wiechman. "The importance of seasonal resource selection when managing a threatened species: targeting conservation actions within critical habitat designations for the Gunnison sage-grouse." Wildlife Research 44, no. 5 (2017): 407. http://dx.doi.org/10.1071/wr17027.

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Context The ability to identify priority habitat is critical for species of conservation concern. The designation of critical habitat under the US Endangered Species Act 1973 identifies areas occupied by the species that are important for conservation and may need special management or protection. However, relatively few species’ critical habitats designations incorporate habitat suitability models or seasonal specificity, even when that information exists. Gunnison sage-grouse (GUSG) have declined substantially from their historical range and were listed as threatened by the US Fish and Wildl
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Haxton, Tim J., C. Scott Findlay, and R. W. Threader. "Predictive Value of a Lake Sturgeon Habitat Suitability Model." North American Journal of Fisheries Management 28, no. 5 (October 2008): 1373–83. http://dx.doi.org/10.1577/m07-146.1.

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Street, Garrett M., Lucas M. Vander Vennen, Tal Avgar, Anna Mosser, Morgan L. Anderson, Arthur R. Rodgers, and John M. Fryxell. "Habitat selection following recent disturbance: model transferability with implications for management and conservation of moose (Alces alces)." Canadian Journal of Zoology 93, no. 11 (November 2015): 813–21. http://dx.doi.org/10.1139/cjz-2015-0005.

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Site-specific variation in relative habitat abundance and disturbance regimes may produce differences in habitat preferences of associated populations. An evaluation of the predictive power of habitat selection models across space would benefit our understanding of the reliability of models of selection and space use in predicting animal occurrence. We used presence–absence data from winter surveys of moose (Alces alces (L., 1758)) to estimate resource selection functions (RSFs) across two study sites using Far North Land Cover updated with recent disturbance from fire and timber harvest. Moos
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TAKEMURA, Shion, Yoshihisa AKAMATSU, and Mahito KAMADA. "Evaluation of vulnerability of mangrove habitats using predictive habitat distribution model in Palau Islands." Journal of Japan Society of Civil Engineers, Ser. G (Environmental Research) 68, no. 5 (2012): I_105—I_110. http://dx.doi.org/10.2208/jscejer.68.i_105.

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Buechling, Arne, and Claudine Tobalske. "Predictive Habitat Modeling of Rare Plant Species in Pacific Northwest Forests." Western Journal of Applied Forestry 26, no. 2 (April 1, 2011): 71–81. http://dx.doi.org/10.1093/wjaf/26.2.71.

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Abstract Certification requirements associated with the Sustainable Forestry Initiative include efforts to identify and protect occurrences of endangered plant species. Habitat models were constructed in this study using maximum entropy and random forest algorithms to generate independent predictions for four selected rare plants, Castilleja chambersii, Erythronium elegans, Filipendula occidentalis, and Sidalcea nelsoniana, associated with divergent physical environments. Explanatory variables used to model rare plant occurrence included Landsat Enhanced Thematic Mapper Plus spectral imagery,
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Alabia, Irene D., Sei-Ichi Saitoh, Hiromichi Igarashi, Yoichi Ishikawa, Norihisa Usui, Masafumi Kamachi, Toshiyuki Awaji, and Masaki Seito. "Ensemble squid habitat model using three-dimensional ocean data." ICES Journal of Marine Science 73, no. 7 (May 6, 2016): 1863–74. http://dx.doi.org/10.1093/icesjms/fsw075.

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Abstract Neon flying squid (Ommastrephes bartramii) is a large pelagic squid internationally harvested in the North Pacific. Here, we examined its potential habitat in the central North Pacific using an ensemble modelling approach. Initially, ten statistical models were constructed by combining the squid fishing points, selected vertical layers of the sea temperature and salinity, sea surface height (SSH), and SSH gradient from the multi-variate ocean variational estimation system for the western North Pacific from June to July 1999–2011. The variable selection analyses have captured the impor
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Socolar, Jacob B., and David S. Wilcove. "Forest-type specialization strongly predicts avian responses to tropical agriculture." Proceedings of the Royal Society B: Biological Sciences 286, no. 1913 (October 23, 2019): 20191724. http://dx.doi.org/10.1098/rspb.2019.1724.

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Species’ traits influence how populations respond to land-use change. However, even in well-characterized groups such as birds, widely studied traits explain only a modest proportion of the variance in response across species. Here, we show that associations with particular forest types strongly predict the sensitivity of forest-dwelling Amazonian birds to agriculture. Incorporating these fine-scale habitat associations into models of population response dramatically improves predictive performance and markedly outperforms the functional traits that commonly appear in similar analyses. Moreove
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Rozprawy doktorskie na temat "Habitat predictive model"

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Machemer, Ethan G. P. "A Predictive Habitat Model for Rainbow Parrotfish Scarus guacamaia." NSUWorks, 2010. http://nsuworks.nova.edu/occ_stuetd/212.

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The rainbow parrotfish Scarus guacamaia is a prominent herbivore in the coastal waters of southeastern Florida whose life history is strongly linked to a dependence on both mangrove and coral reef habitats. Rainbow parrotfish in turn serve in maintaining the health of coral reefs by keeping algal populations in check. This study used NOAA Fisheries data from the Mangrove Visual Census and the Reef Visual Census in Biscayne Bay and Upper Florida Bay. Observations of abiotic factors at individual sites were used to correlate and predict presence and absence of this species. This was done to visu
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Alizadeh, Shabani Afshin, and afshin alizadeh@rmit edu au. "Identifying bird species as biodiversity indicators for terrestrial ecosystem management." RMIT University. Mathematical and Geospatial Sciences, 2006. http://adt.lib.rmit.edu.au/adt/public/adt-VIT20061116.161912.

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It is widely known that the world is losing biodiversity and primarily it is thought to be caused by anthropogenic activities. Many of these activities have been identified. However, we still lack a clear understanding of the causal relationships between human activities and the pressures they place on the environment and biodiversity. We need to know how ecosystems and individual species respond to changes in human activities and therefore how best to moderate our actions and reduce the rate of loss of biodiversity. One of the ways to detect these changes is to use indicators of e
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Embling, Clare B. "Predictive models of cetacean distributions off the west coast of Scotland." Thesis, University of St Andrews, 2008. http://hdl.handle.net/10023/640.

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The main purpose of this study was to produce and test the reliability of predictive models of cetacean distributions off the west coast of Scotland. Passive acoustic and visual surveys were carried out from platforms of opportunity between 2003 and 2005. Acoustic identifications were made primarily of harbour porpoises (Phocoena phocoena), delphinids, and sperm whales (Physeter macrocephalus). Generalised Additive Models (GAMs) were used to relate species’ distributions to a range of environmental variables over a range of temporal and spatial scales. Predictive models of delphinid distributi
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Morris, Charisa Maria. "Building a Predictive Model of Delmarva Fox Squirrel (Sciurus niger cinereus) Occurrence Using Infrared Photomonitors." Thesis, Virginia Tech, 2006. http://hdl.handle.net/10919/35356.

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Habitat modeling can assist in managing potentially widespread but poorly known biological resources such as the federally endangered Delmarva fox squirrel (DFS; Sciurus niger cinereus). The ability to predict or identify suitable habitat is a necessary component of this species' recovery. Habitat identification is also an important consideration when evaluating impacts of land development on this species distribution, which is limited to the Delmarva Peninsula. The goal of this study was to build a predictive model of DFS occurrence that can be used towards the effective management of this
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Amey, Katherine Springer. "Hydrology And Predictive Model Of Headwater Streams And The Groundwater/Surface Water Interactions Supporting Brook Trout Habitat In Northeast Ohio." Kent State University / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=kent1301618586.

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González-Andrés, Cristina. "The role of marine offshore protected areas in protecting large pelagics. Practical case: Cocos Island National Park (Costa Rica)." Doctoral thesis, Universidad de Alicante, 2020. http://hdl.handle.net/10045/115291.

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Wickert, Claudia. "Breeding white storks in former East Prussia : comparing predicted relative occurrences across scales and time using a stochastic gradient boosting method (TreeNet), GIS and public data." Master's thesis, Universität Potsdam, 2007. http://opus.kobv.de/ubp/volltexte/2007/1353/.

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In dieser Arbeit wurden verschiedene GIS-basierte Habitatmodelle für den Weißstorch (Ciconia ciconia) im Gebiet der ehemaligen deutschen Provinz Ostpreußen (ca. Gebiet der russischen Exklave Kaliningrad und der polnischen Woiwodschaft Ermland-Masuren) erstellt. Zur Charakterisierung der Beziehung zwischen dem Weißstorch und der Beschaffenheit seiner Umwelt wurden verschiedene historische Datensätze über den Bestand des Weißstorches in den 1930er Jahren sowie ausgewählte Variablen zur Habitat-Beschreibung genutzt. Die Aufbereitung und Modellierung der verwendeten Datensätze erfolgte mit Hilfe e
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Cross, Cheryl L. "Predictive Habitat Models for Four Cetaceans in the Mid-Atlantic Bight." NSUWorks, 2010. http://nsuworks.nova.edu/occ_stuetd/221.

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This study focuses on the habitats of cetaceans in the Mid-Atlantic Bight, a region characterized by bathymetric diversity and the presence of distinct water masses (i.e. the shelf water, slope water, and Gulf Stream). The combination of these features contributes to the hydrographic complexity of the area, which furthermore influences biological productivity and potential prey available for cetaceans. The collection of cetacean sighting data together with physical oceanographic data can be used to examine cetacean habitat associations. Cetacean habitat modeling is a mechanism for predicting c
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Wright, Amanda. "Predicting the distribution of Eurasian badger (Meles meles) setts." Thesis, Manchester Metropolitan University, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.364059.

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Krause, Colin William. "Evaluation and Use of Stream Temperature Prediction Models for Instream Flow and Fish Habitat Management." Thesis, Virginia Tech, 2002. http://hdl.handle.net/10919/31229.

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The SNTEMP (U.S. Fish and Wildlife Service), QUAL2E (U.S. Environmental Protection Agency), and RQUAL (Tennessee Valley Authority) stream temperature prediction models were evaluated. All models had high predictive ability with the majority of predictions, >80% for Back Creek (Roanoke County, VA) and >90% for the Smith River tailwater (SRT) (Patrick County, VA), within 3°C of the measured water temperature. Sensitivity of model input parameters was found to differ between model, stream system, and season. The most sensitive of assessed parameters, dependent on model and stream, were latera
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Książki na temat "Habitat predictive model"

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Canada. Natural Resources Canada. Canadian Forest Service. Great Lakes Forestry Centre. Predicting canopy closure for habitat modeling. Ottawa: Natural Resources Canada., 1995.

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Drew, C. Ashton. Predictive species and habitat modeling in landscape ecology: Concepts and applications. New York: Springer, 2011.

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Contor, Craig R. Assessment of COWFISH for predicting trout populations in grazed watersheds of the Intermountain West. Ogden, Utah: U.S. Dept. of Agriculture, Forest Service, Intermountain Research Station, 1991.

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Zorn, Troy G. Utility of species-specific, multiple linear regression models for prediction of fish assemblages in rivers of Michigan's lower peninsula. Lansing, MI: Michigan Dept. of Natural Resources, Fisheries Division, 2004.

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Evaluation of the Predictive Ecological Model for the Edwards Aquifer Habitat Conservation Plan. Washington, D.C.: National Academies Press, 2016. http://dx.doi.org/10.17226/23557.

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Ltd, Dendron Resource Surveys, Great Lakes Forestry Centre, Canada-Ontario Subsidiary Agreement on Northern Ontario Development., and Northern Forestry Program (Canada), eds. Predicting canopy closure for habitat modeling. Sault Ste. Marie, Ont: Great Lakes Forestry Centre, 1995.

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Inc, Dendron Resource Surveys, and Great Lakes Forest Research Centre., eds. Predicting canopy closure for habitat modeling. Sault Ste. Marie, Ont: Great Lakes Forestry Centre, 1995.

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Railsback, Steven F., and Bret C. Harvey. Modeling Populations of Adaptive Individuals. Princeton University Press, 2020. http://dx.doi.org/10.23943/princeton/9780691195285.001.0001.

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Ecologists now recognize that the dynamics of populations, communities, and ecosystems are strongly affected by adaptive individual behaviors. Yet until now, we have lacked effective and flexible methods for modeling such dynamics. Traditional ecological models become impractical with the inclusion of behavior, and the optimization approaches of behavioral ecology cannot be used when future conditions are unpredictable due to feedbacks from the behavior of other individuals. This book provides a comprehensive introduction to state- and prediction-based theory, or SPT, a powerful new approach t
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Jappelli, Tullio, and Luigi Pistaferri. The Response of Consumption to Anticipated Changes in Income. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780199383146.003.0008.

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The intertemporal models studied so far postulate that people use savings in order to smooth income fluctuations, and that unless there are liquidity constraints, consumption responds little if at all to changes in income that were expected. When this major theoretical prediction is violated, researchers conclude that consumption is excessively sensitive to anticipated income changes. In this chapter we review some of the empirical approaches researchers have taken to estimate the response of consumption to anticipated income changes. We point out that empirically it is very hard to identify s
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1945-, Silander John August, Civco Daniel L, and United States. National Aeronautics and Space Administration., eds. Landscape dynamics of northeastern forests: First year annual report. [Washington, DC: National Aeronautics and Space Administration, 1994.

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Części książek na temat "Habitat predictive model"

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Huettmann, Falk, and Thomas Gottschalk. "Simplicity, Model Fit, Complexity and Uncertainty in Spatial Prediction Models Applied Over Time: We Are Quite Sure, Aren’t We?" In Predictive Species and Habitat Modeling in Landscape Ecology, 189–208. New York, NY: Springer New York, 2010. http://dx.doi.org/10.1007/978-1-4419-7390-0_10.

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Drew, C. Ashton, and Ajith H. Perera. "Expert Knowledge as a Basis for Landscape Ecological Predictive Models." In Predictive Species and Habitat Modeling in Landscape Ecology, 229–48. New York, NY: Springer New York, 2010. http://dx.doi.org/10.1007/978-1-4419-7390-0_12.

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Lawler, Josh J., Yolanda F. Wiersma, and Falk Huettmann. "Using Species Distribution Models for Conservation Planning and Ecological Forecasting." In Predictive Species and Habitat Modeling in Landscape Ecology, 271–90. New York, NY: Springer New York, 2010. http://dx.doi.org/10.1007/978-1-4419-7390-0_14.

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Wiersma, Yolanda F. "Variation, Use, and Misuse of Statistical Models: A Review of the Effects on the Interpretation of Research Results." In Predictive Species and Habitat Modeling in Landscape Ecology, 209–27. New York, NY: Springer New York, 2010. http://dx.doi.org/10.1007/978-1-4419-7390-0_11.

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Veech, Joseph A. "Post-analysis Procedures." In Habitat Ecology and Analysis, 175–92. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780198829287.003.0010.

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There are several additional statistical procedures that can be conducted after a habitat analysis. The statistical model produced by a habitat analysis can be assessed for fit to the data. Model fit describes how well the predictor variables explain the variance in the response variable, typically species presence–absence or abundance. When more than one statistical model has been produced by the habitat analysis, these can be compared by a formal procedure called model comparison. This usually involves identifying the model with the lowest Akaike information criterion (AIC) value. If the statistical model is considered a predictive tool then its predictive accuracy needs to be assessed. There are many metrics for assessing the predictive performance of a model and quantifying rates of correct and incorrect classification; the latter are error rates. Many of these metrics are based on the numbers of true positive, true negative, false positive, and false negative observations in an independent dataset. “True” and “false” refer to whether species presence–absence was correctly predicted or not. Predictive performance can also be assessed by constructing a receiver operating characteristic (ROC) curve and calculating area under the curve (AUC) values. High AUC values approaching 1 indicate good predictive performance, whereas a value near 0.5 indicates a poor model that predicts species presence–absence no better than a random guess.
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"Fish Habitat: Essential Fish Habitat and Rehabilitation." In Fish Habitat: Essential Fish Habitat and Rehabilitation, edited by Peter J. Auster. American Fisheries Society, 1999. http://dx.doi.org/10.47886/9781888569124.ch13.

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<em>Abstract.—</em> The 1996 Magnuson–Stevens Fishery Conservation and Management Act mandates that regional fishery management councils must designate essential fish habitat (EFH) for each managed species, assess the effects of fishing on EFH, and develop conservation measures for EFH where needed. This synthesis of fishing effects on habitat was produced to aid the fishery management councils in assessing the impacts of fishing activities. A wide range of studies was reviewed that reported effects of fishing on habitat (i.e., structural habitat components, community structure, and ecosystem processes) for a diversity of habitats and fishing gear types. Commonalities of all studies included immediate effects on species composition and diversity and a reduction in habitat complexity. Studies of acute effects were found to be a good predictor of chronic effects. Recovery after fishing was more variable depending on habitat type, life history strategy of component species, and the natural disturbance regime. The ultimate goal of gear impact studies should not be to retrospectively analyze environmental impacts but ultimately to develop the ability to predict outcomes of particular management regimes. Synthesizing the results of these studies into predictive numerical models is not currently possible. However, conceptual models can coalesce the patterns found over the range of observations and can be used to predict effects of gear impacts within the framework of current ecological theory. Initially, it is useful to consider fishes’ use of habitats along a gradient of habitat complexity and environmental variability. Such considerations can be facilitated by a model of gear impacts on a range of seafloor types based on changes in structural habitat values. Disturbance theory provides the framework for predicting effects of habitat change based on spatial patterns of disturbance. Alternative community state models and type 1–type 2 disturbance patterns may be used to predict the general outcome of habitat management. Primary data are lacking on the spatial extent of fishing-induced disturbance, the effects of specific gear types along a gradient of fishing effort, and the linkages between habitat characteristics and the population dynamics of fishes. Adaptive and precautionary management practices will therefore be required until empirical data become available for validating model predictions.
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"Fish Habitat: Essential Fish Habitat and Rehabilitation." In Fish Habitat: Essential Fish Habitat and Rehabilitation, edited by Peter J. Auster. American Fisheries Society, 1999. http://dx.doi.org/10.47886/9781888569124.ch13.

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<em>Abstract.—</em> The 1996 Magnuson–Stevens Fishery Conservation and Management Act mandates that regional fishery management councils must designate essential fish habitat (EFH) for each managed species, assess the effects of fishing on EFH, and develop conservation measures for EFH where needed. This synthesis of fishing effects on habitat was produced to aid the fishery management councils in assessing the impacts of fishing activities. A wide range of studies was reviewed that reported effects of fishing on habitat (i.e., structural habitat components, community structure, and ecosystem processes) for a diversity of habitats and fishing gear types. Commonalities of all studies included immediate effects on species composition and diversity and a reduction in habitat complexity. Studies of acute effects were found to be a good predictor of chronic effects. Recovery after fishing was more variable depending on habitat type, life history strategy of component species, and the natural disturbance regime. The ultimate goal of gear impact studies should not be to retrospectively analyze environmental impacts but ultimately to develop the ability to predict outcomes of particular management regimes. Synthesizing the results of these studies into predictive numerical models is not currently possible. However, conceptual models can coalesce the patterns found over the range of observations and can be used to predict effects of gear impacts within the framework of current ecological theory. Initially, it is useful to consider fishes’ use of habitats along a gradient of habitat complexity and environmental variability. Such considerations can be facilitated by a model of gear impacts on a range of seafloor types based on changes in structural habitat values. Disturbance theory provides the framework for predicting effects of habitat change based on spatial patterns of disturbance. Alternative community state models and type 1–type 2 disturbance patterns may be used to predict the general outcome of habitat management. Primary data are lacking on the spatial extent of fishing-induced disturbance, the effects of specific gear types along a gradient of fishing effort, and the linkages between habitat characteristics and the population dynamics of fishes. Adaptive and precautionary management practices will therefore be required until empirical data become available for validating model predictions.
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"Landscape Influences on Stream Habitats and Biological Assemblages." In Landscape Influences on Stream Habitats and Biological Assemblages, edited by James E. McKenna, Richard P. McDonald, Chris Castiglione, Sandy S. Morrison, Kurt P. Kowalski, and Dora R. Passino-Reader. American Fisheries Society, 2006. http://dx.doi.org/10.47886/9781888569766.ch26.

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<em>Abstract.</em>—We describe a methodology for developing species–habitat models using available fish and stream habitat data from New York State, focusing on the Genesee basin. Electrofishing data from the New York Department of Environmental Conservation were standardized and used for model development and testing. Four types of predictive models (multiple linear regression, stepwise multiple linear regression, linear discriminant analysis, and neural network) were developed and compared for 11 fish species. Predictive models used as many as 25 habitat variables and explained 35–91% of observed species abundance variability. Omission rates were generally low, but commission rates varied widely. Neural network models performed best for all species, except for rainbow trout <em>Oncorhynchus mykiss</em>, gizzard shad <em>Dorosoma cepedianum</em>, and brown trout <em>Salmo trutta</em>. Linear discriminant functions generally performed poorly. The species–environment models we constructed performed well and have potential applications to management issues.
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"Landscape Influences on Stream Habitats and Biological Assemblages." In Landscape Influences on Stream Habitats and Biological Assemblages, edited by Les W. Stanfield, Scott F. Gibson, and Jason A. Borwick. American Fisheries Society, 2006. http://dx.doi.org/10.47886/9781888569766.ch29.

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<em>Abstract.</em>—Effective management of salmonid populations in the Great Lakes basin requires understanding how their distribution and density vary spatially. We used a hierarchical approach to evaluate the predictive capabilities of landscape conditions, local habitat features, and potential effects from coinhabiting salmonids on the distribution and densities of rainbow trout <em>Oncorhynchus mykiss</em>, brook trout <em>Salvelinus fontinalis, </em>brown trout <em>Salmo trutta</em>, and coho salmon <em>O. kisutch </em>within the majority of the Canadian tributaries of Lake Ontario. We collected fish assemblage, instream habitat, and water temperature data from 416 wadeable stream sites. Landscape characteristics were obtained for each site’s catchment and summarized into six key attributes (drainage area, base flow index, percent impervious cover (PIC), reach slope, elevation, and location with respect to permanent fish barriers). Classification trees indicated that PIC in a catchment was a critical predictor of salmonid distribution, in that beyond a threshold of 6.6–9 PIC, all salmonids were predicted to be absent. Base flow index and barriers were also important predictors of the distribution of salmonids. Models generally provided higher classification success at predicting absence (86–98%) than predicting presence (63–87%). Landscape features were the best predictors of densities of rainbow and brook trout (adjusted <em>r</em><sup>2</sup> = 0.49 and 0.30 respectively), although the local habitat features were almost as effective for predicting brook trout (<em>r</em><sup>2</sup> = 0.23). Local habitat features (proportion of riffles and pools, substrate, cover, and stream temperature), and presence of other salmonids produced the best predictive model for brown trout. Coho salmon was only locally distributed in the basin, and the derived model was driven by spatial characteristics rather than ecological processes. Our models estimate 653,000 juvenile rainbow trout and 231,000 brook trout (all age-classes) in our study streams. Finally, we estimate that current brook trout distribution in our study area is only 21% of its historic range.
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"Landscape Influences on Stream Habitats and Biological Assemblages." In Landscape Influences on Stream Habitats and Biological Assemblages, edited by Keith B. Gido, Jeffrey A. Falke, Robert M. Oakes, and Kristen J. Hase. American Fisheries Society, 2006. http://dx.doi.org/10.47886/9781888569766.ch12.

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<em>Abstract.</em>—Habitat data collected at three spatial scales (catchments, reaches, and sites) were used to predict individual fish species occurrences and assemblage structure at 150 sites in the Kansas River basin. Habitat measurements for the catchments and reaches of each sample site were derived from available geographic information system (GIS) data layers. Habitat measurements at the sample sites were collected at the time of fish sampling. Because habitat measurements are typically more difficult to collect as the spatial scale of sampling decreases (i.e., field measurement versus a GIS analysis), our objective was to quantify the relative increase in predictive ability as we added habitat measurements from increasingly finer spatial scales. Although the addition of site-scale habitat variables increased the predictive performance of models, the relative magnitude of these increases was small. This was largely due to the general association of species occurrences with measurements of catchment area and soil factors, both of which could be quantified with a GIS. Habitat measurements taken at different spatial scales were often correlated; however, a partial canonical correspondence analysis showed that catchment- scale habitat measurements accounted for a slightly higher percent of the variation in fish-assemblage structure across the 150 sample sites than reach- or site-scale habitat measurements. We concluded that field habitat measurements were less informative for predicting species occurrences within the Kansas River basin than catchment data. However, because of the hierarchical nature of the geomorphological processes that form stream habitats, a refined understanding of the relationship between catchment-, reach- and site-scale habitats provides a mechanistic understanding of fish–habitat relations across spatial scales.
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Streszczenia konferencji na temat "Habitat predictive model"

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Chen, Di, Yexiang Xue, Daniel Fink, Shuo Chen, and Carla P. Gomes. "Deep Multi-species Embedding." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/509.

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Understanding how species are distributed across landscapes over time is a fundamental question in biodiversity research. Unfortunately, most species distribution models only target a single species at a time, despite strong ecological evidence that species are not independently distributed. We propose Deep Multi-Species Embedding (DMSE), which jointly embeds vectors corresponding to multiple species as well as vectors representing environmental covariates into a common high-dimensional feature space via a deep neural network. Applied to bird observational data from the citizen science project
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Ahsan, Nasir, Stefan B. Williams, Michael Jakuba, Oscar Pizarro, and Ben Radford. "Predictive habitat models from AUV-based multibeam and optical imagery." In 2010 OCEANS MTS/IEEE SEATTLE. IEEE, 2010. http://dx.doi.org/10.1109/oceans.2010.5663809.

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KUMADA, Takayuki, Takaaki UDA, and Masumi SERIZAWA. "MODEL FOR PREDICTING THE EXTENSION OF HABITAT OF JAPANESE HARD CLAM MERETRIX LAMARCKII." In Proceedings of the 31st International Conference. World Scientific Publishing Company, 2009. http://dx.doi.org/10.1142/9789814277426_0378.

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Garg, Priya, and Deepti Aggarwal. "Application of Swarm-Based Feature Selection and Extreme Learning Machines in Lung Cancer Risk Prediction." In Intelligent Computing and Technologies Conference. AIJR Publisher, 2021. http://dx.doi.org/10.21467/proceedings.115.1.

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Lung cancer risk prediction models help in identifying high-risk individuals for early CT screening tests. These predictive models can play a pivotal role in healthcare by decreasing lung cancer's mortality rate and saving many lives. Although many predictive models have been developed that use various features, no specific guidelines have been provided regarding the crucial features in lung cancer risk prediction. This study proposes novel risk prediction models using bio-inspired swarm-based techniques for feature selection and extreme learning machines for classification. The proposed model
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Uenaka, Takashi, Naohisa Sakamoto, and Koji Koyamada. "Visual Analysis of Habitat Suitability Index Model for Predicting the Locations of Fishing Grounds." In 2014 IEEE Pacific Visualization Symposium (PacificVis). IEEE, 2014. http://dx.doi.org/10.1109/pacificvis.2014.33.

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Piacenza, Joseph, Salvador Mayoral, Bahaa Albarhami, and Sean Lin. "Understanding the Importance of Post Occupancy Usage Trends During Concept-Stage Sustainable Building Design." In ASME 2017 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/detc2017-67461.

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As sustainable building mandates become more prevalent in new commercial and mixed use buildings, it is a challenge to create a broad, one-size-fits-all certification process. While designers can estimate energy usage with computational tools such as model based design, anticipating the post occupancy usage is more challenging. Understanding and predicting energy usage trends is especially complicated in unique mixed use building applications, such as university student housing buildings, where occupancy varies significantly as a function of enrollment, course scheduling, and student study hab
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Long, Keyu, and Zaiyue Yang. "Model predictive control for household energy management based on individual habit." In 2013 25th Chinese Control and Decision Conference (CCDC). IEEE, 2013. http://dx.doi.org/10.1109/ccdc.2013.6561587.

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Wang, Tianyi, Xiaohan Mei, J. Alex Thomasson, Xiongzhe Han, and Pappu Kumar Yadav. "<i>Volunteer Cotton Habitat Prediction Model and Detection with UAV Remote Sensing</i>." In 2020 ASABE Annual International Virtual Meeting, July 13-15, 2020. St. Joseph, MI: American Society of Agricultural and Biological Engineers, 2020. http://dx.doi.org/10.13031/aim.202000219.

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Lazar, Alina, Alexandra Ballow, Ling Jin, C. Anna Spurlock, Alexander Sim, and Kesheng Wu. "Machine Learning for Prediction of Mid to Long Term Habitual Transportation Mode Use." In 2019 IEEE International Conference on Big Data (Big Data). IEEE, 2019. http://dx.doi.org/10.1109/bigdata47090.2019.9006411.

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Jeon, Soonil, Jang-Moo Lee, and Yeong-Il Park. "Advanced Multi-Mode Control Strategy for a Parallel Hybrid Electric Vehicle Based on Driving Pattern Recognition." In ASME 2003 International Mechanical Engineering Congress and Exposition. ASMEDC, 2003. http://dx.doi.org/10.1115/imece2003-41857.

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The adaptive multi-mode control strategy (AMMCS) is defined as the control strategy that switches control parameters for the purpose of adjusting vehicles to diverse traffic conditions and driver’s habits. This strategy is composed of off-line and on-line procedures. In the off-line procedure, several sets of control parameters are optimized under representative driving patterns (RDP). In the on-line procedure, the control parameter switching or interpolation is periodically activated based on the driving pattern recognition (DPR) algorithm, assuming that the driving pattern during the future
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