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fireworks.bib
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@article{kilambi_simple_2018,
title = {A {Simple} and {Effective} {Method} for {Separating} {Meteorological} from {Nonmeteorological} {Targets} {Using} {Dual}-{Polarization} {Data}},
volume = {35},
issn = {0739-0572, 1520-0426},
url = {http://journals.ametsoc.org/doi/10.1175/JTECH-D-17-0175.1},
doi = {10.1175/JTECH-D-17-0175.1},
abstract = {To satisfy the needs of the meteorological and aeroecological communities wanting a simple but effective way of flagging each other’s unwanted echo for a variety of different operational radar systems, we evaluated the ability of an estimate of depolarization ratio (DR) based on differential reflectivity ( Z
DR
) and copolar correlation coefficient ( ρ
HV
) measurements to separate both types of echoes. The method was tested with data collected by S- and C-band radars used in the United States and Canada. The DR-based method that does not require training achieved 96\% separation between weather and biological echoes. Since the misclassifications are typically caused by isolated pixels in the melting layer or at the edge of echo patterns, the addition of a despeckling algorithm considerably reduces further these false alarms, resulting in an increase in correct identification approaching 99\% on test cases.},
language = {en},
number = {7},
urldate = {2019-12-26},
journal = {Journal of Atmospheric and Oceanic Technology},
author = {Kilambi, Alamelu and Fabry, Frédéric and Meunier, Véronique},
month = jul,
year = {2018},
pages = {1415--1424}
}
@article{dokter_bird_2011,
title = {Bird migration flight altitudes studied by a network of operational weather radars},
volume = {8},
issn = {1742-5689, 1742-5662},
url = {https://royalsocietypublishing.org/doi/10.1098/rsif.2010.0116},
doi = {10.1098/rsif.2010.0116},
language = {en},
number = {54},
urldate = {2019-12-28},
journal = {Journal of The Royal Society Interface},
author = {Dokter, Adriaan M. and Liechti, Felix and Stark, Herbert and Delobbe, Laurent and Tabary, Pierre and Holleman, Iwan},
month = jan,
year = {2011},
pages = {30--43},
file = {Full Text:/Users/barthoekstra/Zotero/storage/QATWGDZF/Dokter et al. - 2011 - Bird migration flight altitudes studied by a netwo.pdf:application/pdf}
}
@article{dokter_biorad_2019,
title = {{bioRad}: biological analysis and visualization of weather radar data},
volume = {42},
issn = {0906-7590, 1600-0587},
shorttitle = {{bioRad}},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/ecog.04028},
doi = {10.1111/ecog.04028},
language = {en},
number = {5},
urldate = {2019-12-31},
journal = {Ecography},
author = {Dokter, Adriaan M. and Desmet, Peter and Spaaks, Jurriaan H. and van Hoey, Stijn and Veen, Lourens and Verlinden, Liesbeth and Nilsson, Cecilia and Haase, Günther and Leijnse, Hidde and Farnsworth, Andrew and Bouten, Willem and Shamoun‐Baranes, Judy},
month = may,
year = {2019},
pages = {852--860},
file = {Full Text:/Users/barthoekstra/Zotero/storage/ZLYKT2UG/Dokter et al. - 2019 - bioRad biological analysis and visualization of w.pdf:application/pdf}
}
@article{storchova_2018,
author = {Storchová, Lenka and Hořák, David},
title = {Life-history characteristics of European birds},
journal = {Global Ecology and Biogeography},
volume = {27},
number = {4},
pages = {400-406},
keywords = {behaviour, birds, dietary preferences, Europe, habitat preferences, life history, migration, morphology, reproduction, traits},
doi = {10.1111/geb.12709},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/geb.12709},
eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1111/geb.12709},
abstract = {Abstract Motivation Birds are an extremely diverse group in terms of adaptations to environmental conditions, which is reflected in their life histories and ecological traits. Recently, functional aspects of avian diversity have been used frequently in comparative analyses as well as in community ecology studies; thus, open access to complete datasets of traits will be valuable. We focused on European bird species and compiled information about crucial ecological traits. This dataset is thus useful for research investigating large-scale patterns in European avifauna and can be used in various analyses in evolutionary ecology, macroecology or conservation biology. Main types of variables contained We chose several types of avian traits, such as morphological (e.g., weight, wing, bill or tarsus length), reproductive (e.g., clutch size, egg mass, incubation period or type of young) and behavioural traits (type of nest, mating system or type of parental care), dietary (e.g., folivore, granivore, insectivore or carnivore) and habitat preferences (e.g., deciduous/coniferous forest, reed or grassland). Spatial location and grain Europe; all breeding bird species (n = 499). Major taxa and level of measurement In total, we created a dataset for 499 bird species breeding in Europe and 34 key life-history traits represented in 85 variables. As a primary source of information we used the comprehensive handbook The birds of the Western Palearctic. The traits provide information about species-specific mean values. We did not record values for different geographical subspecies (i.e., the trait value always represents the average for the whole breeding area of a particular species). Software format The data file is in ASCII text, tab delimited, not compressed.},
year = {2018}
}
@article{kranstauber2020,
title={High-Resolution Spatial Distribution of Bird Movements Estimated from a Weather Radar Network},
author={Kranstauber, Bart and Bouten, Willem and Leijnse, Hidde and Wijers, Berend-Christiaan and Verlinden, Liesbeth and Shamoun-Baranes, Judy and Dokter, Adriaan M},
journal={Remote Sensing},
volume={12},
number={4},
pages={635},
year={2020},
publisher={Multidisciplinary Digital Publishing Institute},
doi = {10.3390/rs12040635},
url = {https://www.mdpi.com/2072-4292/12/4/635}
}
@article{crase2012,
author = {Crase, Beth and Liedloff, Adam C. and Wintle, Brendan A.},
title = {A new method for dealing with residual spatial autocorrelation in species distribution models},
journal = {Ecography},
volume = {35},
number = {10},
pages = {879-888},
doi = {https://doi.org/10.1111/j.1600-0587.2011.07138.x},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1600-0587.2011.07138.x},
eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1600-0587.2011.07138.x},
abstract = {Species distribution modelling (SDM) is a widely used tool and has many applications in ecology and conservation biology. Spatial autocorrelation (SAC), a pattern in which observations are related to one another by their geographic distance, is common in georeferenced ecological data. SAC in the residuals of SDMs violates the ‘independent errors’ assumption required to justify the use of statistical models in modelling species’ distributions. The autologistic modelling approach accounts for SAC by including an additional term (the autocovariate) representing the similarity between the value of the response variable at a location and neighbouring locations. However, autologistic models have been found to introduce bias in the estimation of parameters describing the influence of explanatory variables on habitat occupancy. To address this problem we developed an extension to the autologistic approach by calculating the autocovariate on SAC in residuals (the RAC approach). Performance of the new approach was tested on simulated data with a known spatial structure and on strongly autocorrelated mangrove species’ distribution data collected in northern Australia. The RAC approach was implemented as generalized linear models (GLMs) and boosted regression tree (BRT) models. We found that the BRT models with only environmental explanatory variables can account for some SAC, but applying the standard autologistic or RAC approaches further reduced SAC in model residuals and substantially improved model predictive performance. The RAC approach showed stronger inferential performance than the standard autologistic approach, as parameter estimates were more accurate and statistically significant variables were accurately identified. The new RAC approach presented here has the potential to account for spatial autocorrelation while maintaining strong predictive and inferential performance, and can be implemented across a range of modelling approaches.},
year = {2012}
}