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@article{samways_orthoptera_1998,
title = {Orthoptera conservation: pests and paradoxes},
volume = {2},
shorttitle = {Orthoptera conservation},
number = {3},
journal = {Journal of Insect Conservation},
author = {Samways, M. J. and Lockwood, J. A.},
year = {1998},
pages = {143--149}
}
@article{massaro_experimental_1983,
title = {An {Experimental} {Test} of the {Effects} of {Predation} {Risk} on {Habitat} {Use} in {Fish}},
volume = {64},
issn = {00129658},
url = {http://www.jstor.org/stable/1937508},
number = {6},
urldate = {2010-03-29},
journal = {Ecology},
author = {Massaro, Dominic W. and Friedman, Daniel},
month = dec,
year = {1983},
note = {ArticleType: primary\_article / Full publication date: Dec., 1983 / Copyright © 1983 Ecological Society of America},
pages = {1540--1548}
}
@book{venables_modern_2002,
address = {New York},
edition = {Fourth},
title = {Modern {Applied} {Statistics} with {S}},
url = {http://www.stats.ox.ac.uk/pub/MASS4},
publisher = {Springer},
author = {Venables, W. N. and Ripley, B. D.},
year = {2002},
note = {ISBN 0-387-95457-0}
}
@article{antonovics_nature_1976,
title = {The nature of limits to natural selection},
volume = {63},
number = {2},
journal = {Annals of the Missouri Botanical Garden},
author = {Antonovics, J.},
year = {1976},
pages = {224--247}
}
@article{bergstra_random_2012,
title = {Random search for hyper-parameter optimization},
volume = {13},
number = {1},
journal = {The Journal of Machine Learning Research},
author = {Bergstra, James and Bengio, Yoshua},
year = {2012},
pages = {281--305}
}
@article{salakhutdinov_efficient_2012,
title = {An efficient learning procedure for deep {Boltzmann} machines},
volume = {24},
url = {http://www.mitpressjournals.org/doi/pdf/10.1162/NECO_a_00311},
number = {8},
urldate = {2013-04-25},
journal = {Neural Computation},
author = {Salakhutdinov, Ruslan and Hinton, Geoffrey},
year = {2012},
pages = {1967--2006}
}
@article{letters_perspectives_2005,
title = {{PERSPECTIVES} {Introduced} species as evolutionary traps},
volume = {8},
journal = {Ecology Letters},
author = {Letters, E.},
year = {2005},
pages = {241--246}
}
@article{de_waal_putting_2007,
title = {Putting the altruism back into altruism: the evolution of empathy},
shorttitle = {Putting the altruism back into altruism},
author = {De Waal, F. B.M},
year = {2007}
}
@article{harris_contact_1974,
title = {Contact {Interactions} on a {Lattice}},
volume = {2},
url = {http://dx.doi.org/10.1214/aop/1176996493},
doi = {10.1214/aop/1176996493},
number = {6},
journal = {The Annals of Probability},
author = {Harris, T. E.},
year = {1974},
pages = {969--988}
}
@book{murphy_machine_2012,
title = {Machine {Learning}: {A} {Probabilistic} {Perspective}},
isbn = {0262018020},
shorttitle = {Machine {Learning}},
publisher = {The MIT Press},
author = {Murphy, Kevin P.},
month = aug,
year = {2012}
}
@article{gelman_weakly_2008,
title = {A {Weakly} {Informative} {Default} {Prior} {Distribution} for {Logistic} and {Other} {Regression} {Models}},
volume = {2},
copyright = {Copyright © 2008 Institute of Mathematical Statistics},
issn = {1932-6157},
url = {http://www.jstor.org/stable/30245139},
abstract = {We propose a new prior distribution for classical (nonhierarchical) logistic regression models, constructed by first scaling all nonbinary variables to have mean 0 and standard deviation 0.5, and then placing independent Student-t prior distributions on the coefficients. As a default choice, we recommend the Cauchy distribution with center 0 and scale 2.5, which in the simplest setting is a longer-tailed version of the distribution attained by assuming one-half additional success and one-half additional failure in a logistic regression. Cross-validation on a corpus of datasets shows the Cauchy class of prior distributions to outperform existing implementations of Gaussian and Laplace priors. We recommend this prior distribution as a default choice for routine applied use. It has the advantage of always giving answers, even when there is complete separation in logistic regression (a common problem, even when the sample size is large and the number of predictors is small), and also automatically applying more shrinkage to higher-order interactions. This can be useful in routine data analysis as well as in automated procedures such as chained equations for missing-data imputation. We implement a procedure to fit generalized linear models in R with the Student-t prior distribution by incorporating an approximate EM algorithm into the usual iteratively weighted least squares. We illustrate with several applications, including a series of logistic regressions predicting voting preferences, a small bioassay experiment, and an imputation model for a public health data set.},
number = {4},
urldate = {2015-03-19},
journal = {The Annals of Applied Statistics},
author = {Gelman, Andrew and Jakulin, Aleks and Pittau, Maria Grazia and Su, Yu-Sung},
month = dec,
year = {2008},
pages = {1360--1383}
}
@article{hijmans_very_2005,
title = {Very high resolution interpolated climate surfaces for global land areas},
volume = {25},
copyright = {Copyright © 2005 Royal Meteorological Society},
issn = {1097-0088},
url = {http://onlinelibrary.wiley.com/doi/10.1002/joc.1276/abstract},
doi = {10.1002/joc.1276},
abstract = {We developed interpolated climate surfaces for global land areas (excluding Antarctica) at a spatial resolution of 30 arc s (often referred to as 1-km spatial resolution). The climate elements considered were monthly precipitation and mean, minimum, and maximum temperature. Input data were gathered from a variety of sources and, where possible, were restricted to records from the 1950–2000 period. We used the thin-plate smoothing spline algorithm implemented in the ANUSPLIN package for interpolation, using latitude, longitude, and elevation as independent variables. We quantified uncertainty arising from the input data and the interpolation by mapping weather station density, elevation bias in the weather stations, and elevation variation within grid cells and through data partitioning and cross validation. Elevation bias tended to be negative (stations lower than expected) at high latitudes but positive in the tropics. Uncertainty is highest in mountainous and in poorly sampled areas. Data partitioning showed high uncertainty of the surfaces on isolated islands, e.g. in the Pacific. Aggregating the elevation and climate data to 10 arc min resolution showed an enormous variation within grid cells, illustrating the value of high-resolution surfaces. A comparison with an existing data set at 10 arc min resolution showed overall agreement, but with significant variation in some regions. A comparison with two high-resolution data sets for the United States also identified areas with large local differences, particularly in mountainous areas. Compared to previous global climatologies, ours has the following advantages: the data are at a higher spatial resolution (400 times greater or more); more weather station records were used; improved elevation data were used; and more information about spatial patterns of uncertainty in the data is available. Owing to the overall low density of available climate stations, our surfaces do not capture of all variation that may occur at a resolution of 1 km, particularly of precipitation in mountainous areas. In future work, such variation might be captured through knowledge-based methods and inclusion of additional co-variates, particularly layers obtained through remote sensing. Copyright © 2005 Royal Meteorological Society.},
language = {en},
number = {15},
urldate = {2013-05-29},
journal = {International Journal of Climatology},
author = {Hijmans, Robert J. and Cameron, Susan E. and Parra, Juan L. and Jones, Peter G. and Jarvis, Andy},
year = {2005},
keywords = {ANUSPLIN, climate, error, GIS, interpolation, precipitation, temperature, uncertainty},
pages = {1965--1978}
}
@article{wisz_role_2013,
title = {The role of biotic interactions in shaping distributions and realised assemblages of species: implications for species distribution modelling},
volume = {88},
copyright = {© 2012 The Authors. Biological Reviews © 2012 Cambridge Philosophical Society},
issn = {1469-185X},
shorttitle = {The role of biotic interactions in shaping distributions and realised assemblages of species},
url = {http://onlinelibrary.wiley.com/doi/10.1111/j.1469-185X.2012.00235.x/abstract},
doi = {10.1111/j.1469-185X.2012.00235.x},
abstract = {Predicting which species will occur together in the future, and where, remains one of the greatest challenges in ecology, and requires a sound understanding of how the abiotic and biotic environments interact with dispersal processes and history across scales. Biotic interactions and their dynamics influence species' relationships to climate, and this also has important implications for predicting future distributions of species. It is already well accepted that biotic interactions shape species' spatial distributions at local spatial extents, but the role of these interactions beyond local extents (e.g. 10 km2 to global extents) are usually dismissed as unimportant. In this review we consolidate evidence for how biotic interactions shape species distributions beyond local extents and review methods for integrating biotic interactions into species distribution modelling tools. Drawing upon evidence from contemporary and palaeoecological studies of individual species ranges, functional groups, and species richness patterns, we show that biotic interactions have clearly left their mark on species distributions and realised assemblages of species across all spatial extents. We demonstrate this with examples from within and across trophic groups. A range of species distribution modelling tools is available to quantify species environmental relationships and predict species occurrence, such as: (i) integrating pairwise dependencies, (ii) using integrative predictors, and (iii) hybridising species distribution models (SDMs) with dynamic models. These methods have typically only been applied to interacting pairs of species at a single time, require a priori ecological knowledge about which species interact, and due to data paucity must assume that biotic interactions are constant in space and time. To better inform the future development of these models across spatial scales, we call for accelerated collection of spatially and temporally explicit species data. Ideally, these data should be sampled to reflect variation in the underlying environment across large spatial extents, and at fine spatial resolution. Simplified ecosystems where there are relatively few interacting species and sometimes a wealth of existing ecosystem monitoring data (e.g. arctic, alpine or island habitats) offer settings where the development of modelling tools that account for biotic interactions may be less difficult than elsewhere.},
language = {en},
number = {1},
urldate = {2013-04-28},
journal = {Biological Reviews},
author = {Wisz, Mary Susanne and Pottier, Julien and Kissling, W. Daniel and Pellissier, Loïc and Lenoir, Jonathan and Damgaard, Christian F. and Dormann, Carsten F. and Forchhammer, Mads C. and Grytnes, John-Arvid and Guisan, Antoine and Heikkinen, Risto K. and Høye, Toke T. and Kühn, Ingolf and Luoto, Miska and Maiorano, Luigi and Nilsson, Marie-Charlotte and Normand, Signe and Öckinger, Erik and Schmidt, Niels M. and Termansen, Mette and Timmermann, Allan and Wardle, David A. and Aastrup, Peter and Svenning, Jens-Christian},
year = {2013},
keywords = {biotic interaction, climate, macroecology, prediction, sampling, scale, spatial extent, species assemblage, species distribution model},
pages = {15--30}
}
@book{ruxton_avoiding_2004,
address = {Oxford :},
title = {Avoiding attack the evolutionary ecology of crypsis, warning signals and mimicry},
isbn = {9780198528593},
publisher = {Oxford University Press,},
author = {Ruxton, Graeme},
year = {2004}
}
@article{pellissier_probabilistic_2013,
title = {A probabilistic approach to niche-based community models for spatial forecasts of assemblage properties and their uncertainties},
volume = {40},
issn = {1365-2699},
url = {http://dx.doi.org/10.1111/jbi.12140},
doi = {10.1111/jbi.12140},
number = {10},
journal = {Journal of Biogeography},
author = {Pellissier, Loïc and Espíndola, Anahí and Pradervand, Jean-Nicolas and Dubuis, Anne and Pottier, Julien and Ferrier, Simon and Guisan, Antoine},
year = {2013},
keywords = {Butterflies, elevation, phylogenetic diversity, species distribution models, species richness, stochasticity, Swiss Alps, uncertainty},
pages = {1939--1946}
}
@article{bruno_inclusion_2003,
title = {Inclusion of facilitation into ecological theory},
volume = {18},
issn = {0169-5347},
url = {http://www.cell.com/article/S0169534702000459/abstract},
doi = {10.1016/S0169-5347(02)00045-9},
abstract = {Investigations of the role of competition, predation and abiotic stress in shaping natural communities were a staple for previous generations of ecologists and are still popular themes. However, more recent experimental research has uncovered the largely unanticipated, yet striking influence of facilitation (i.e. positive species interactions) on the organization of terrestrial and aquatic communities. Modern ecological concepts and theories were well established a decade before the current renaissance of interest in facilitation began, and thus do not consider the importance of a wide variety of facilitative interactions. It is time to bring ecological theory up to date by including facilitation. This process will not be painless because it will fundamentally change many basic predictions and will challenge some of our most cherished paradigms. But, ultimately, revising ecological theory will lead to a more accurate and inclusive understanding of natural communities.},
language = {English},
number = {3},
urldate = {2015-03-19},
journal = {Trends in Ecology \& Evolution},
author = {Bruno, John F. and Stachowicz, John J. and Bertness, Mark D.},
month = jan,
year = {2003},
pages = {119--125},
file = {Snapshot:/Users/davidjharris/Library/Application Support/Firefox/Profiles/0iu9o3qs.default/zotero/storage/ZW38X25M/S0169-5347(02)00045-9.html:text/html}
}
@book{center_for_history_and_new_media_zotero_????,
title = {Zotero {Quick} {Start} {Guide}},
url = {http://zotero.org/support/quick_start_guide},
author = {{Center for History and New Media}}
}
@article{dormann_components_2008,
title = {Components of uncertainty in species distribution analysis: a case study of the great grey shrike},
volume = {89},
number = {12},
journal = {Ecology},
author = {Dormann, Carsten F and Purschke, Oliver and Márquez, Jaime R García and Lautenbach, Sven and Schröder, Boris},
year = {2008},
pages = {3371--3386}
}
@article{ovaskainen_making_2011,
title = {Making more out of sparse data: hierarchical modeling of species communities},
volume = {92},
shorttitle = {Making more out of sparse data},
url = {http://www.esajournals.org/doi/abs/10.1890/10-1251.1},
number = {2},
urldate = {2013-04-25},
journal = {Ecology},
author = {Ovaskainen, Otso and Soininen, Janne},
year = {2011},
pages = {289--295}
}
@article{kearney_mechanistic_2009,
title = {Mechanistic niche modelling: combining physiological and spatial data to predict species’ ranges},
volume = {12},
number = {4},
journal = {Ecology letters},
author = {Kearney, Michael and Porter, Warren},
year = {2009},
pages = {334--350}
}
@article{bell_value_2009,
title = {The value of constant surveillance in a risky environment},
volume = {276},
number = {1669},
journal = {Proceedings of the Royal Society of London, Series B: Biological Sciences},
author = {Bell, M. B. V. and Radford, A. N. and Rose, R. and Wade, H. M. and Ridley, A. R.},
year = {2009},
pages = {2997--3005}
}
@book{bickel_mathematical_1977,
title = {Mathematical {Statistics}: {Basic} {Ideas} and {Selected} {Topics}},
publisher = {San Francisco: Holden—Day},
author = {Bickel, PJ and Doksum, K},
year = {1977}
}
@article{kesavaraju_no_2009,
title = {No {Evolutionary} {Response} to {Four} {Generations} of {Laboratory} {Selection} on {Antipredator} {Behavior} of {Aedes} albopictus: {Potential} {Implications} for {Biotic} {Resistance} to {Invasion}},
volume = {46},
shorttitle = {No {Evolutionary} {Response} to {Four} {Generations} of {Laboratory} {Selection} on {Antipredator} {Behavior} of {Aedes} albopictus},
number = {4},
journal = {Journal of medical entomology},
author = {Kesavaraju, B. and Juliano, S. A},
year = {2009},
pages = {772}
}
@article{kamil_selective_2006,
title = {Selective attention, priming, and foraging behavior},
journal = {Comparative Cognition: Experimental Explorations Of Animal Intelligence},
author = {Kamil, A. C and Bond, A. B},
year = {2006}
}
@article{loh_structure_2013,
title = {Structure estimation for discrete graphical models: {Generalized} covariance matrices and their inverses},
volume = {41},
issn = {0090-5364},
shorttitle = {Structure estimation for discrete graphical models},
url = {http://arxiv.org/abs/1212.0478},
doi = {10.1214/13-AOS1162},
abstract = {We investigate the relationship between the structure of a discrete graphical model and the support of the inverse of a generalized covariance matrix. We show that for certain graph structures, the support of the inverse covariance matrix of indicator variables on the vertices of a graph reflects the conditional independence structure of the graph. Our work extends results that have previously been established only in the context of multivariate Gaussian graphical models, thereby addressing an open question about the significance of the inverse covariance matrix of a non-Gaussian distribution. The proof exploits a combination of ideas from the geometry of exponential families, junction tree theory and convex analysis. These population-level results have various consequences for graph selection methods, both known and novel, including a novel method for structure estimation for missing or corrupted observations. We provide nonasymptotic guarantees for such methods and illustrate the sharpness of these predictions via simulations.},
number = {6},
urldate = {2015-03-19},
journal = {The Annals of Statistics},
author = {Loh, Po-Ling and Wainwright, Martin J.},
month = dec,
year = {2013},
note = {arXiv: 1212.0478},
keywords = {Mathematics - Statistics Theory, Statistics - Machine Learning},
pages = {3022--3049},
annote = {Comment: Published in at http://dx.doi.org/10.1214/13-AOS1162 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org)},
file = {arXiv\:1212.0478 PDF:/Users/davidjharris/Library/Application Support/Firefox/Profiles/0iu9o3qs.default/zotero/storage/9R2AGZR5/Loh and Wainwright - 2013 - Structure estimation for discrete graphical models.pdf:application/pdf;arXiv.org Snapshot:/Users/davidjharris/Library/Application Support/Firefox/Profiles/0iu9o3qs.default/zotero/storage/UQMZIA3B/1212.html:text/html}
}
@book{orr_neural_1998,
title = {Neural {Networks}: {Tricks} of the {Trade}},
publisher = {Springer-Verlag},
author = {Orr, Genevieve B and Müller, Klaus-Robert},
year = {1998}
}
@article{ghalambor_adaptive_2007,
title = {Adaptive versus non-adaptive phenotypic plasticity and the potential for contemporary adaptation in new environments},
volume = {21},
issn = {0269-8463},
url = {http://www.blackwell-synergy.com/doi/abs/10.1111/j.1365-2435.2007.01283.x},
doi = {10.1111/j.1365-2435.2007.01283.x},
number = {3},
journal = {Functional Ecology},
author = {Ghalambor, C. K. and McKAY, J. K. and Carroll, S. P. and Reznick, D. N.},
month = jun,
year = {2007},
pages = {394--407}
}
@article{murray_mcmc_2012,
title = {{MCMC} for doubly-intractable distributions},
journal = {arXiv preprint arXiv:1206.6848},
author = {Murray, Iain and Ghahramani, Zoubin and MacKay, David},
year = {2012}
}
@book{harris_rosalia_2015,
title = {rosalia: {Exact} inference for small binary {Markov} networks. {R} package version 0.1.0},
url = {http://dx.doi.org/10.5281/zenodo.17808},
publisher = {Zenodo. http://dx.doi.org/10.5281/zenodo.17808},
author = {Harris, David J.},
year = {2015},
note = {R package version 0.1.0}
}
@article{leathwick_comparative_2006,
title = {Comparative performance of generalized additive models and multivariate adaptive regression splines for statistical modelling of species distributions},
volume = {199},
issn = {0304-3800},
url = {http://www.sciencedirect.com/science/article/pii/S0304380006002572},
doi = {http://dx.doi.org/10.1016/j.ecolmodel.2006.05.022},
number = {2},
journal = {Ecological Modelling},
author = {Leathwick, J. R. and Elith, J. and Hastie, T.},
year = {2006},
note = {Predicting Species Distributions Results from a Second Workshop on Advances in Predictive Species Distribution Models, held in Riederalp, Switzerland, 2004},
keywords = {adaptive, Multivariate, regression, splines},
pages = {188 -- 196}
}
@article{barrett_adaptation_2008,
title = {Adaptation from standing genetic variation},
volume = {23},
number = {1},
journal = {Trends in Ecology \& Evolution},
author = {Barrett, R. D.H and Schluter, D.},
year = {2008},
pages = {38--44}
}
@inproceedings{kakade_alternate_2002,
title = {An alternate objective function for {Markovian} fields},
booktitle = {{MACHINE} {LEARNING}-{INTERNATIONAL} {WORKSHOP} {THEN} {CONFERENCE}-},
author = {Kakade, Sham and Teh, Yee Whye and Roweis, Sam T},
year = {2002},
pages = {275--282}
}
@article{wang_mvabundr_2012,
title = {mvabund–an {R} package for model-based analysis of multivariate abundance data},
volume = {3},
number = {3},
journal = {Methods in Ecology and Evolution},
author = {Wang, Yi and Naumann, Ulrike and Wright, Stephen T and Warton, David I},
year = {2012},
pages = {471--474}
}
@article{blumstein_assessment_1996,
title = {Assessment and decision making in animals: a mechanistic model underlying behavioral flexibility can prevent ambiguity},
volume = {77},
shorttitle = {Assessment and decision making in animals},
number = {3},
journal = {Oikos},
author = {Blumstein, D. T and Bouskila, A.},
year = {1996},
pages = {569--576}
}
@article{veech_probabilistic_2013,
title = {A probabilistic model for analysing species co-occurrence},
volume = {22},
issn = {1466-8238},
url = {http://dx.doi.org/10.1111/j.1466-8238.2012.00789.x},
doi = {10.1111/j.1466-8238.2012.00789.x},
number = {2},
journal = {Global Ecology and Biogeography},
author = {Veech, Joseph A.},
year = {2013},
keywords = {Biogeography, combinatorics, community assembly, distribution, nestedness, null model, probability},
pages = {252--260}
}
@article{bahn_can_2007,
title = {Can niche-based distribution models outperform spatial interpolation?},
volume = {16},
issn = {1466-8238},
url = {http://onlinelibrary.wiley.com/doi/10.1111/j.1466-8238.2007.00331.x/abstract},
doi = {10.1111/j.1466-8238.2007.00331.x},
abstract = {Aim Distribution modelling relates sparse data on species occurrence or abundance to environmental information to predict the population of a species at any point in space. Recently, the importance of spatial autocorrelation in distributions has been recognized. Spatial autocorrelation can be categorized as exogenous (stemming from autocorrelation in the underlying variables) or endogenous (stemming from activities of the organism itself, such as dispersal). Typically, one asks whether spatial models explain additional variability (endogenous) in comparison to a fully specified habitat model. We turned this question around and asked: can habitat models explain additional variation when spatial structure is accounted for in a fully specified spatially explicit model? The aim was to find out to what degree habitat models may be inadvertently capturing spatial structure rather than true explanatory mechanisms.Location We used data from 190 species of the North American Breeding Bird Survey covering the conterminous United States and southern Canada.Methods We built 13 different models on 190 bird species using regression trees. Our habitat-based models used climate and landcover variables as independent variables. We also used random variables and simulated ranges to validate our results. The two spatially explicit models included only geographical coordinates or a contagion term as independent variables. As another angle on the question of mechanism vs. spatial structure we pitted a model using related bird species as predictors against a model using randomly selected bird species.Results The spatially explicit models outperformed the traditional habitat models and the random predictor species outperformed the related predictor species. In addition, environmental variables produced a substantial R2 in predicting artificial ranges.Main conclusions We conclude that many explanatory variables with suitable spatial structure can work well in species distribution models. The predictive power of environmental variables is not necessarily mechanistic, and spatial interpolation can outperform environmental explanatory variables.},
language = {en},
number = {6},
urldate = {2013-05-31},
journal = {Global Ecology and Biogeography},
author = {Bahn, Volker and McGill, Brian J.},
year = {2007},
keywords = {Birds, distribution modelling, habitat, macroecology, model evaluation, neighbourhood, niche, spatial autocorrelation, spatial interpolation, species distributions},
pages = {733--742}
}
@article{welsh_fallacy_1988,
title = {The {Fallacy} of {Averages}},
volume = {132},
issn = {00030147},
url = {http://www.jstor.org/stable/2461871},
abstract = {In the biological literature, the mean of the product of two or more random variables is frequently calculated from the product of their means. However, unless the variables are independent, an exceptional occurrence in biological systems, the two are not equivalent. Corresponding false assumptions commonly are made about ratios of means and various other functions of means. These assumptions are examples of perhaps the most common statistical fallacy in the biological literature, the fallacy of averages: the false assumption that the mean of a nonlinear function of several variables equals the function of the means of those variables. We provide the relationship between functions of means and means of functions for common functions of one variable (linear, reciprocal, and exponential functions), for two or more variables (product, ratio, sum), and for the product of allometric relationships.},
number = {2},
urldate = {2010-06-09},
journal = {The American Naturalist},
author = {Welsh, A. H. and Peterson, A. Townsend and Altmann, Stuart A.},
month = aug,
year = {1988},
note = {ArticleType: primary\_article / Full publication date: Aug., 1988 / Copyright © 1988 The University of Chicago Press},
pages = {277--288}
}
@inproceedings{salakhutdinov_restricted_2007,
address = {New York, NY, USA},
series = {{ICML} '07},
title = {Restricted {Boltzmann} {Machines} for {Collaborative} {Filtering}},
isbn = {978-1-59593-793-3},
url = {http://doi.acm.org/10.1145/1273496.1273596},
doi = {10.1145/1273496.1273596},
booktitle = {Proceedings of the 24th {International} {Conference} on {Machine} {Learning}},
publisher = {ACM},
author = {Salakhutdinov, Ruslan and Mnih, Andriy and Hinton, Geoffrey},
year = {2007},
pages = {791--798}
}
@article{hebets_complex_2004,
title = {Complex signal function: developing a framework of testable hypotheses},
volume = {57},
issn = {0340-5443},
shorttitle = {Complex signal function},
url = {http://www.springerlink.com/content/v2rtufcyv6geftpv/fulltext.html},
doi = {10.1007/s00265-004-0865-7},
number = {3},
urldate = {2010-04-05},
journal = {Behavioral Ecology and Sociobiology},
author = {Hebets, Eileen A. and Papaj, Daniel R.},
month = nov,
year = {2004},
pages = {197--214}
}
@inproceedings{nair_rectified_2010,
title = {Rectified linear units improve restricted boltzmann machines},
booktitle = {Proc. 27th {International} {Conference} on {Machine} {Learning}},
author = {Nair, Vinod and Hinton, Geoffrey E},
year = {2010},
pages = {807--814}
}
@book{mccune_analysis_2002,
title = {Analysis of ecological communities},
volume = {28},
publisher = {MjM software design Gleneden Beach, OR},
author = {McCune, Bruce and Grace, James B and Urban, Dean L},
year = {2002}
}
@article{hendry_human_2008,
title = {Human influences on rates of phenotypic change in wild animal populations},
volume = {17},
number = {1},
journal = {Molecular Ecology},
author = {HENDRY, A. P and FARRUGIA, T. J and KINNISON, M. T},
year = {2008},
pages = {20--29}
}
@article{stevens_predator_2007,
title = {Predator perception and the interrelation between different forms of protective coloration},
volume = {274},
number = {1617},
journal = {Proceedings of the Royal Society B},
author = {Stevens, M.},
year = {2007},
pages = {1457}
}
@article{blois_framework_2014,
title = {A framework for evaluating the influence of climate, dispersal limitation, and biotic interactions using fossil pollen associations across the late {Quaternary}},
volume = {37},
number = {11},
journal = {Ecography},
author = {Blois, Jessica L and Gotelli, Nicholas J and Behrensmeyer, Anna K and Faith, J Tyler and Lyons, S Kathleen and Williams, John W and Amatangelo, Kathryn L and Bercovici, Antoine and Du, Andrew and Eronen, Jussi T and {others}},
year = {2014},
pages = {1095--1108}
}
@article{guisan_sesam_2011,
title = {{SESAM} – a new framework integrating macroecological and species distribution models for predicting spatio-temporal patterns of species assemblages},
volume = {38},
copyright = {© 2011 Blackwell Publishing Ltd},
issn = {1365-2699},
url = {http://onlinelibrary.wiley.com/doi/10.1111/j.1365-2699.2011.02550.x/abstract},
doi = {10.1111/j.1365-2699.2011.02550.x},
abstract = {Two different approaches currently prevail for predicting spatial patterns of species assemblages. The first approach (macroecological modelling, MEM) focuses directly on realized properties of species assemblages, whereas the second approach (stacked species distribution modelling, S-SDM) starts with constituent species to approximate the properties of assemblages. Here, we propose to unify the two approaches in a single ‘spatially explicit species assemblage modelling’ (SESAM) framework. This framework uses relevant designations of initial species source pools for modelling, macroecological variables, and ecological assembly rules to constrain predictions of the richness and composition of species assemblages obtained by stacking predictions of individual species distributions. We believe that such a framework could prove useful in many theoretical and applied disciplines of ecology and evolution, both for improving our basic understanding of species assembly across spatio-temporal scales and for anticipating expected consequences of local, regional or global environmental changes. In this paper, we propose such a framework and call for further developments and testing across a broad range of community types in a variety of environments.},
language = {en},
number = {8},
urldate = {2013-05-30},
journal = {Journal of Biogeography},
author = {Guisan, Antoine and Rahbek, Carsten},
year = {2011},
keywords = {biodiversity, community properties, ecological assembly rules, ecological niche modelling, macroecological constraints, species richness, species sorting, species source pool, stacked species predictions},
pages = {1433--1444}
}
@article{hutchinson_penalized_2015,
title = {Penalized {Likelihood} {Methods} {Improve} {Parameter} {Estimates} in {Occupancy} {Models}},
copyright = {This article is protected by copyright. All rights reserved.},
issn = {2041-210X},
url = {http://onlinelibrary.wiley.com/doi/10.1111/2041-210X.12368/abstract},
doi = {10.1111/2041-210X.12368},
abstract = {1.Occupancy models are employed in species distribution modeling to account for imperfect detection during field surveys. While this approach is popular in the literature, problems can occur when estimating the model parameters. In particular, the maximum likelihood estimates can exhibit bias and large variance for datasets with small sample sizes, which can result in estimated occupancy probabilities near 0 and 1 (“boundary estimates”). 2.In this paper, we explore strategies for estimating parameters based on maximizing a penalized likelihood. Penalized likelihood methods augment the usual likelihood with a penalty function that encodes information about what parameter values are undesirable. We introduce penalties for occupancy models that have analogues in ridge regression and Bayesian approaches, and we compare them to a penalty developed for occupancy models in prior work. 3.We examine the bias, variance, and mean squared error of parameter estimates obtained from each method on synthetic data. Across all of the synthetic datasets, the penalized estimation methods had lower mean squared error than the maximum likelihood estimates. We also provide an example of the application of these methods to point counts of avian species. Penalized likelihood methods show similar improvements when tested using empirical bird point count data. 4.We discuss considerations for choosing among these methods when modeling occupancy. We conclude that penalized methods may be of practical utility for fitting occupancy models with small sample sizes, and we are releasing R code that implements these methods. This article is protected by copyright. All rights reserved.},
language = {en},
urldate = {2015-03-19},
journal = {Methods in Ecology and Evolution},
author = {Hutchinson, Rebecca A. and Valente, Jonathon J. and Emerson, Sarah C. and Betts, Matthew G. and Dietterich, Thomas G.},
month = mar,
year = {2015},
keywords = {boundary estimates, detection probability, maximum likelihood, occupancy modeling, parameter estimation, penalized likelihood},
pages = {n/a--n/a},
file = {Snapshot:/Users/davidjharris/Library/Application Support/Firefox/Profiles/0iu9o3qs.default/zotero/storage/B7ABZ4HT/abstract.html:text/html}
}
@article{hastings_can_1987,
title = {Can competition be detected using species co-occurrence data?},
journal = {Ecology},
author = {Hastings, Alan},
year = {1987},
pages = {117--123}
}
@article{harris_occupancy_2011,
title = {Occupancy is nine-tenths of the law: occupancy rates determine the homogenizing and differentiating effects of exotic species.},
volume = {177},
shorttitle = {Occupancy is nine-tenths of the law},
url = {http://www.ncbi.nlm.nih.gov/pubmed/21460574},
number = {4},
urldate = {2015-03-19},
journal = {The American naturalist},
author = {Harris, D. J. and Smith, K. G. and Hanly, P. J.},
year = {2011},
pages = {535},
file = {Snapshot:/Users/davidjharris/Library/Application Support/Firefox/Profiles/0iu9o3qs.default/zotero/storage/3MU683CR/21460574.html:text/html}
}
@article{getty_lagging_1985,
title = {Lagging {Partial} {Preferences} for {Cryptic} {Prey}: {A} {Signal} {Detection} {Analysis} of {Great} {Tit} {Foraging}},
volume = {125},
issn = {00030147},
shorttitle = {Lagging {Partial} {Preferences} for {Cryptic} {Prey}},
url = {http://www.jstor.org/stable/2461607},
abstract = {Most prey species are cryptic or mimetic, and as a result, they provide variable sensory cues to predators that are only statistically correlated with resource quality. We develop an analogy between foraging for cryptic prey and basic signal detection problems. An optimization model based on this analogy predicts partial preferences for cryptic prey, with the probability of attack varying between 0 and 1 as a function of the relative frequency of prey. Experiments with great tits show that they respond to unpredictable changes in prey relative frequency by adjusting their probabilities of attack in the predicted directions, but the responses lag behind. In our experiments, the birds' behavior is most appropriate to long-term average conditions (over a span of hours, extending into the preceding day. Problems associated with assessing changing resource distributions are discussed.},
number = {1},
urldate = {2010-04-05},
journal = {The American Naturalist},
author = {Getty, Thomas and Krebs, J. R.},
month = jan,
year = {1985},
note = {ArticleType: primary\_article / Full publication date: Jan., 1985 / Copyright © 1985 The University of Chicago Press},
pages = {39--60}
}
@book{schafer_corpcor_2014,
title = {corpcor: {Efficient} {Estimation} of {Covariance} and ({Partial}) {Correlation}},
url = {http://CRAN.R-project.org/package=corpcor},
author = {Schäfer, Juliane and Opgen-Rhein, Rainer and Zuber, Verena and Ahdesmäki, Miika and Silva, A. Pedro Duarte and Strimmer, Korbinian},
year = {2014},
note = {R package version 1.6.7}
}
@article{lee_stability_2007,
title = {Stability of behavioral syndromes but plasticity in individual behavior: consequences for rockfish stock enhancement},
volume = {82},
issn = {0378-1909},
url = {http://www.springerlink.com/index/10.1007/s10641-007-9288-4},
doi = {10.1007/s10641-007-9288-4},
number = {2},
journal = {Environmental Biology of Fishes},
author = {Lee, Jonathan S. F. and Bereijikian, Barry A.},
month = oct,
year = {2007},
pages = {179--186}
}
@article{gigerenzer_null_2004,
title = {The null ritual: {What} you always wanted to know about significance testing but were afraid to ask},
shorttitle = {The null ritual},
journal = {The SAGE handbook of quantitative methodology for the social sciences},
author = {Gigerenzer, G. and Krauss, S. and Vitouch, O.},
year = {2004},
pages = {391--408}
}
@article{faisal_inferring_2010,
title = {Inferring species interaction networks from species abundance data: {A} comparative evaluation of various statistical and machine learning methods},
volume = {5},
issn = {1574-9541},
url = {http://www.sciencedirect.com/science/article/pii/S1574954110000786},
doi = {http://dx.doi.org/10.1016/j.ecoinf.2010.06.005},
number = {6},
journal = {Ecological Informatics},
author = {Faisal, Ali and Dondelinger, Frank and Husmeier, Dirk and Beale, Colin M.},
year = {2010},
keywords = {Bio-climate envelope},
pages = {451 -- 464}
}
@article{andersson_biological_1999,
title = {The biological cost of antibiotic resistance},
volume = {2},
number = {5},
journal = {Current opinion in Microbiology},
author = {Andersson, D. I and Levin, B. R},
year = {1999},
pages = {489--493}
}
@article{andres_interspecific_2000,
title = {Interspecific comparison of cadmium and zinc contamination in the organs of four fish species along a polymetallic pollution gradient ({Lot} {River}, {France})},
volume = {248},
issn = {0048-9697},
url = {http://www.sciencedirect.com/science/article/B6V78-4007PC9-S/2/8151ca84d9eb9c54f87d6afeb7807377},
doi = {10.1016/S0048-9697(99)00477-5},
abstract = {The impact of cadmium (Cd) and zinc (Zn) discharges related to an old zinc ore treatment facility in the Lot River (France) was investigated in four fish species (the chub: Leusciscus cephalus, the roach: Rutilus rutilus, the perch: Perca fluviatilis and the bream: Abramis brama). The organisms were sampled in four stations along the polymetallic contamination gradient. Cd and Zn analysis were carried out in five organs (gills, posterior intestine, liver, kidneys and skeletal muscle) in order to highlight the potential pathways of uptake, storage and elimination of metals. The results indicate a very strong Cd contamination in fish collected downstream from the metal source. The kidneys have the highest cadmium concentrations, but the gills and the intestine, as exchange organs, present the largest variations between the stations in close relation with the contamination gradient. Cd concentrations measured in the liver vary only slightly among the sampling stations. Unlike the trends observed for Cd, Zn levels in fish populations are strongly regulated and do not follow ambient Zn concentrations. The concentrations measured vary also according to fish species, for both Cd and Zn. This study shows that the trophic habits can explain the interspecific differences in Cd bioaccumulation. Zn levels observed for each species in non-contaminated populations also help to understand metal bioaccumulation patterns in polluted sites, suggesting that the determinism of interspecific differences is constitutive.},
number = {1},
urldate = {2010-05-30},
journal = {The Science of The Total Environment},
author = {Andres, S. and Ribeyre, F. and Tourencq, J. -N. and Boudou, A.},
month = mar,
year = {2000},
keywords = {Bioaccumulation, Cadmium, Fish, Lot River, Organotropism, Species, Zinc},
pages = {11--25}
}
@article{terman_discrimination_1970,
title = {Discrimination of auditory intensities by rats},
volume = {13},
issn = {0022-5002},
doi = {10.1901/jeab.1970.13-145},
abstract = {Rats were trained to press one of two keys when a standard intensity value of a 4.0-kHz sine tone (70 or 100 db re 2 × 10−4 microbar) was presented from a centrally located loudspeaker. Pressing the other key was reinforced when comparison intensity values (as much as 30 db less than the standard value) were presented. The animals initiated tone presentations by breaking a light beam at the rear of the chamber. Correct choices produced brain-stimulation reinforcement, and errors produced a timeout. A procedure designed by Jenkins was used to partial out choice data under potential control of sequential cues in the stimulus series. When the standard-comparison intensity difference was varied, the rats showed similar psychometric functions despite wide differences in response bias (relative position preference). A signal detection analysis showed that response biases for individual animals remained fairly consistent during psychophysical testing. The trend of decreasing choice accuracy at small intensity differences was described by the cumulative normal probability function. The similarity of psychometric functions obtained with 70- and 100-db standards supported Weber's law. There was some evidence that response latencies were controlled by intensity differences even when choice behavior was undifferentiated.},
number = {2},
journal = {Journal of the Experimental Analysis of Behavior},
author = {Terman, Michael},
month = mar,
year = {1970},
pmid = {54948975494897},
pmcid = {1333756},
pages = {145--160}
}
@article{funk_conservation_2007,
title = {Conservation genetics of snowy plovers ({Charadrius} alexandrinus) in the {Western} {Hemisphere}: population genetic structure and delineation of subspecies},
volume = {8},
issn = {1566-0621},
url = {http://www.springerlink.com/index/10.1007/s10592-006-9278-7},
doi = {10.1007/s10592-006-9278-7},
number = {6},
journal = {Conservation Genetics},
author = {Funk, W. Chris and Mullins, Thomas D. and Haig, Susan M.},
month = jan,
year = {2007},
pages = {1287--1309}
}
@article{ferrier_extended_2002,
title = {Extended statistical approaches to modelling spatial pattern in biodiversity in northeast {New} {South} {Wales}. {I}. {Species}-level modelling},
volume = {11},
issn = {0960-3115},
url = {http://dx.doi.org/10.1023/A%3A1021302930424},
doi = {10.1023/A:1021302930424},
language = {English},
number = {12},
journal = {Biodiversity \& Conservation},
author = {Ferrier, Simon and Watson, Graham and Pearce, Jennie and Drielsma, Michael},
year = {2002},
keywords = {biodiversity, Northeast New South Wales, regional conservation planning, Statistical modelling, surrogates},
pages = {2275--2307}
}
@article{kinnear_red_2002,
title = {The red fox in {Australia}–an exotic predator turned biocontrol agent},
volume = {108},
number = {3},
journal = {Biological Conservation},
author = {Kinnear, J. E. and Sumner, N. R. and Onus, M. L.},
year = {2002},
pages = {335--359}
}
@misc{lee_learning_2012,
title = {Learning {Mixed} {Graphical} {Models}},
url = {http://cds.cern.ch/record/1451206},
abstract = {We consider the problem of learning the structure of a pairwise graphical model over continuous and discrete variables. We present a new pairwise model for graphical models with both continuous and discrete variables. We estimate the parameters of this model by approximating the likelihood with the pseudolikelihood and regularizing with group-sparsity penalties. We also consider a conditional model that incorporates features. Two algorithms for solving the optimization problem are presented. The proposed models are compared with competing methods on synthetic data and a survey dataset.},
urldate = {2015-03-19},
journal = {CERN Document Server},
author = {Lee, Jason D. and Hastie, Trevor J.},
month = may,
year = {2012},
file = {Full Text PDF:/Users/davidjharris/Library/Application Support/Firefox/Profiles/0iu9o3qs.default/zotero/storage/UP4MZ6UR/Lee and Hastie - 2012 - Learning Mixed Graphical Models.pdf:application/pdf;Snapshot:/Users/davidjharris/Library/Application Support/Firefox/Profiles/0iu9o3qs.default/zotero/storage/XK3Z6ZXA/1451206.html:text/html}
}
@article{zipkin_multi-species_2010,
title = {Multi-species occurrence models to evaluate the effects of conservation and management actions},
volume = {143},
issn = {0006-3207},
url = {http://www.sciencedirect.com/science/article/pii/S0006320709004819},
doi = {10.1016/j.biocon.2009.11.016},
abstract = {Conservation and management actions often have direct and indirect effects on a wide range of species. As such, it is important to evaluate the impacts that such actions may have on both target and non-target species within a region. Understanding how species richness and composition differ as a result of management treatments can help determine potential ecological consequences. Yet it is difficult to estimate richness because traditional sampling approaches detect species at variable rates and some species are never observed. We present a framework for assessing management actions on biodiversity using a multi-species hierarchical model that estimates individual species occurrences, while accounting for imperfect detection of species. Our model incorporates species-specific responses to management treatments and local vegetation characteristics and a hierarchical component that links species at a community-level. This allows for comprehensive inferences on the whole community or on assemblages of interest. Compared to traditional species models, occurrence estimates are improved for all species, even for those that are rarely observed, resulting in more precise estimates of species richness (including species that were unobserved during sampling). We demonstrate the utility of this approach for conservation through an analysis comparing bird communities in two geographically similar study areas: one in which white-tailed deer (Odocoileus virginianus) densities have been regulated through hunting and one in which deer densities have gone unregulated. Although our results indicate that species and assemblage richness were similar in the two study areas, point-level richness was significantly influenced by local vegetation characteristics, a result that would have been underestimated had we not accounted for variability in species detection.},
number = {2},
urldate = {2013-04-28},
journal = {Biological Conservation},
author = {Zipkin, Elise F. and Andrew Royle, J. and Dawson, Deanna K. and Bates, Scott},
month = feb,
year = {2010},
keywords = {Bayesian analysis, Bird communities, Distribution modeling, Hierarchical modeling, Non-target species, Occurrence modeling, species richness},
pages = {479--484}
}
@article{price_behavioral_1999,
title = {Behavioral development in animals undergoing domestication},
volume = {65},
number = {3},
journal = {Applied Animal Behaviour Science},
author = {Price, E. O},
year = {1999},
pages = {245--271}
}
@article{yu_multi-label_2011,
title = {Multi-label {Classification} for {Multi}-{Species} {Distribution} {Modeling}},
url = {http://andrewsforest.oregonstate.edu/pubs/pdf/pub4735.pdf},
journal = {Proceedings of the 28th International Conference on Machine Learning},
author = {Yu, Jun and Wong, Weng-Keen and Dietterich, Tom and Jones, Julia and Betts, Matthew and Frey, Sarah and Shirley, Susan and Miller, Jeffrey and White, Matt},
year = {2011}
}
@article{aguilera_bayesian_2011,
title = {Bayesian networks in environmental modelling},
volume = {26},
issn = {1364-8152},
url = {http://www.sciencedirect.com/science/article/pii/S1364815211001472},
doi = {http://dx.doi.org/10.1016/j.envsoft.2011.06.004},
number = {12},
journal = {Environmental Modelling \& Software},
author = {Aguilera, P. A. and Fernández, A. and Fernández, R. and Rumí, R. and Salmerón, A.},
year = {2011},
keywords = {Review},
pages = {1376 -- 1388}
}
@article{lessard_inferring_2012,
title = {Inferring local ecological processes amid species pool influences},
volume = {27},
issn = {0169-5347},
url = {http://www.sciencedirect.com/science/article/pii/S016953471200167X},
doi = {http://dx.doi.org/10.1016/j.tree.2012.07.006},
number = {11},
journal = {Trends in Ecology \& Evolution},
author = {Lessard, Jean-Philippe and Belmaker, Jonathan and Myers, Jonathan A. and Chase, Jonathan M. and Rahbek, Carsten},
year = {2012},
pages = {600 -- 607}
}
@article{wei_markov_2007,
title = {A {Markov} random field model for network-based analysis of genomic data},
volume = {23},
issn = {1367-4803, 1460-2059},
url = {http://bioinformatics.oxfordjournals.org/content/23/12/1537},
doi = {10.1093/bioinformatics/btm129},
abstract = {Motivation: A central problem in genomic research is the identification of genes and pathways involved in diseases and other biological processes. The genes identified or the univariate test statistics are often linked to known biological pathways through gene set enrichment analysis in order to identify the pathways involved. However, most of the procedures for identifying differentially expressed (DE) genes do not utilize the known pathway information in the phase of identifying such genes. In this article, we develop a Markov random field (MRF)-based method for identifying genes and subnetworks that are related to diseases. Such a procedure models the dependency of the DE patterns of genes on the networks using a local discrete MRF model.
Results: Simulation studies indicated that the method is quite effective in identifying genes and subnetworks that are related to disease and has higher sensitivity and lower false discovery rates than the commonly used procedures that do not use the pathway structure information. Applications to two breast cancer microarray gene expression datasets identified several subnetworks on several of the KEGG transcriptional pathways that are related to breast cancer recurrence or survival due to breast cancer.
Conclusions: The proposed MRF-based model efficiently utilizes the known pathway structures in identifying the DE genes and the subnetworks that might be related to phenotype. As more biological networks are identified and documented in databases, the proposed method should find more applications in identifying the subnetworks that are related to diseases and other biological processes.
Contact: [email protected] or [email protected]},
language = {en},
number = {12},
urldate = {2015-03-21},
journal = {Bioinformatics},
author = {Wei, Zhi and Li, Hongzhe},
month = jun,
year = {2007},
pmid = {17483504},
pages = {1537--1544},
file = {Full Text PDF:/Users/davidjharris/Library/Application Support/Firefox/Profiles/0iu9o3qs.default/zotero/storage/C2INZ82N/Wei and Li - 2007 - A Markov random field model for network-based anal.pdf:application/pdf;Snapshot:/Users/davidjharris/Library/Application Support/Firefox/Profiles/0iu9o3qs.default/zotero/storage/6RVHZMFB/1537.html:text/html}
}
@article{vincent_stacked_2010,
title = {Stacked denoising autoencoders: {Learning} useful representations in a deep network with a local denoising criterion},
volume = {9999},
journal = {The Journal of Machine Learning Research},
author = {Vincent, Pascal and Larochelle, Hugo and Lajoie, Isabelle and Bengio, Yoshua and Manzagol, Pierre-Antoine},
year = {2010},
pages = {3371--3408}
}
@article{neal_connectionist_1992,
title = {Connectionist learning of belief networks},
volume = {56},
issn = {0004-3702},
url = {http://www.sciencedirect.com/science/article/pii/0004370292900656},
doi = {http://dx.doi.org/10.1016/0004-3702(92)90065-6},
abstract = {Connectionist learning procedures are presented for “sigmoid” and “noisy-OR” varieties of probabilistic belief networks. These networks have previously been seen primarily as a means of representing knowledge derived from experts. Here it is shown that the “Gibbs sampling” simulation procedure for such networks can support maximum-likelihood learning from empirical data through local gradient ascent. This learning procedure resembles that used for “Boltzmann machines”, and like it, allows the use of “hidden” variables to model correlations between visible variables. Due to the directed nature of the connections in a belief network, however, the “negative phase” of Boltzmann machine learning is unnecessary. Experimental results show that, as a result, learning in a sigmoid belief network can be faster than in a Boltzmann machine. These networks have other advantages over Boltzmann machines in pattern classification and decision making applications, are naturally applicable to unsupervised learning problems, and provide a link between work on connectionist learning and work on the representation of expert knowledge.},
number = {1},
journal = {Artificial Intelligence},
author = {Neal, Radford M.},
year = {1992},
pages = {71 -- 113}
}
@article{connor_assembly_1979,
title = {The assembly of species communities: chance or competition?},
journal = {Ecology},
author = {Connor, Edward F and Simberloff, Daniel},
year = {1979},
pages = {1132--1140}
}
@inproceedings{mnih_conditional_2011,
title = {Conditional {Restricted} {Boltzmann} {Machines} for {Structured} {Output} {Prediction}},
booktitle = {{UAI}},
author = {Mnih, Volodymyr and Larochelle, Hugo and Hinton, Geoffrey E.},
year = {2011},
pages = {514--522}
}
@article{turelli_fallacy_1982,
title = {The {Fallacy} of the {Fallacy} of the {Averages} in {Ecological} {Optimization} {Theory}},
volume = {119},
issn = {00030147},
url = {http://www.jstor.org/stable/2460971},
number = {6},
urldate = {2010-06-09},
journal = {The American Naturalist},
author = {Turelli, Michael and Gillespie, John H. and Schoener, Thomas W.},
month = jun,
year = {1982},
note = {ArticleType: primary\_article / Full publication date: Jun., 1982 / Copyright © 1982 The University of Chicago Press},
pages = {879--884}
}
@article{friedman_comparison_1968,
title = {Comparison of some learning models for response bias in signal detection},
volume = {3},
number = {1-A},
journal = {Perception \& Psychophysics},
author = {Friedman, M. P and Carterette, E. C and Nakatani, L. and Ahumada, A.},
year = {1968},
pages = {5--11}
}
@article{tilman_human-caused_2001,
title = {Human-caused environmental change: impacts on plant diversity and evolution},
volume = {98},
shorttitle = {Human-caused environmental change},
number = {10},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
author = {Tilman, D. and Lehman, C.},
year = {2001},
pages = {5433}
}
@article{gotelli_null_1996,
title = {Null models in ecology.},
url = {http://agris.fao.org/agris-search/search.do?recordID=US19960125664},
language = {English},
urldate = {2015-03-19},
author = {Gotelli, N.J. and Graves, G.R.},
year = {1996},
file = {Snapshot:/Users/davidjharris/Library/Application Support/Firefox/Profiles/0iu9o3qs.default/zotero/storage/FD4UJVC4/search.html:text/html}
}
@article{sih_predator-prey_2009,
title = {Predator-prey naïveté, antipredator behavior, and the ecology of predator invasions},
issn = {00301299},
url = {http://blackwell-synergy.com/doi/abs/10.1111/j.1600-0706.2009.18039.x},
doi = {10.1111/j.1600-0706.2009.18039.x},
journal = {Oikos},
author = {Sih, Andrew and Bolnick, Daniel I. and Luttbeg, Barney and Orrock, John L. and Peacor, Scott D. and Pintor, Lauren M. and Preisser, Evan and Rehage, Jennifer S. and Vonesh, James R.},
month = dec,
year = {2009}
}
@book{golding_bayescomm_2015,
title = {{BayesComm}: {Bayesian} {Community} {Ecology} {Analysis}},
url = {http://CRAN.R-project.org/package=BayesComm},
author = {Golding, Nick and Harris, David J.},
year = {2015},
note = {R package version 0.1-2}
}
@article{tyack_implications_2008,
title = {Implications for marine mammals of large-scale changes in the marine acoustic environment},
volume = {89},
number = {3},
journal = {Journal Information},
author = {Tyack, P. L},
year = {2008}
}
@article{worthen_predator-mediated_1989,
title = {Predator-mediated coexistence in laboratory communities of mycophagous {Drosophila} ({Diptera}: {Drosophilidae})},
volume = {14},
shorttitle = {Predator-mediated coexistence in laboratory communities of mycophagous {Drosophila} ({Diptera}},
url = {http://dx.doi.org/10.1111/j.1365-2311.1989.tb00761.x},
doi = {10.1111/j.1365-2311.1989.tb00761.x},
abstract = {1. This laboratory experiment examined the effects of interspecific competition and predation by Ontholestes cingulatus Gravenhorst (Coleoptera: Staphylinidae) on three species of mycophagous Drosophila (Diptera: Drosophilidae): D.tripunctata Loew, D.falleni Wheeler and D.putrida Sturtevant. 2. Single-species and three-species assemblages were exposed to single commercial mushrooms on wet pine shavings in 200 ml culture bottles. A predacious rove beetle (Ontholestes cingulatus) was present in half of the three-species replicates. The stocked adult flies and beetles were removed after 4 days, and the number, biomass and mean mass of emerging progeny was recorded. 3. For all three species the abundance and biomass of the progeny emerging in the 'no predator' communities' was significantly less than for the progeny emerging in single-species replicates, suggesting an interspecific competitive effect. D.tripunctata was the competitive dominant; it emerged in abundance from all seven three-species 'no predator' communities while D.putrida and D.falleni were often excluded. 4. The decrease in production was attributed to strong interspecific competition among larva and not interference among ovipositing adults. 5. Predation on ovipositing adults significantly reduced the number and biomass of D.tripunctata progeny emerging, and indirectly facilitated the number and biomass of emerging D.falleni and D.putrida.Predation on adults reduced larval recruitment, relaxed larval competition, and released the inferior competitors.},
number = {1},
urldate = {2010-06-21},
journal = {Ecological Entomology},
author = {WORTHEN, WADE},
year = {1989},
pages = {117--126}
}
@article{sih_hide_1997,
title = {To hide or not to hide? {Refuge} use in a fluctuating environment},
volume = {12},
number = {10},
journal = {Trends in Ecology \& Evolution},
author = {{Sih}},
year = {1997},
pages = {375--376}
}
@article{eddelbuettel_rcpparmadillo_2014,
title = {{RcppArmadillo}: {Accelerating} {R} with high-performance {C}++ linear algebra},
volume = {71},
url = {http://dx.doi.org/10.1016/j.csda.2013.02.005},
journal = {Computational Statistics and Data Analysis},
author = {Eddelbuettel, Dirk and Sanderson, Conrad},
month = mar,
year = {2014},
pages = {1054--1063}
}
@article{connor_checkered_2013,
title = {The checkered history of checkerboard distributions},
volume = {94},
issn = {0012-9658},
url = {http://www.esajournals.org/doi/abs/10.1890/12-1471.1},
doi = {10.1890/12-1471.1},
abstract = {To address the idea that the process of interspecific competition can be inferred from data on geographical distribution alone and that evidence from geographical distribution implies an important role for interspecific competition in shaping ecological communities, we reexamine the occurrence of “true checkerboard” distributions among the land and freshwater birds in three Melanesian archipelagoes: Vanuatu, the Bismarck Archipelago, and the Solomon Islands. We use the most recently published distributional records and explicitly include the geography of the distributions of species within each archipelago. We use the overlap of convex hulls to estimate the overlap in the geographic range for each pair of species in each of these archipelagoes. We define a “true checkerboard” to consist of a pair of species with exclusive island-by-island distributions, but that have overlapping geographical ranges. To avoid the “dilution effect,” we follow Diamond and Gilpin in focusing only on congeneric and within-guild species pairs as potential competitors. Few, if any, “true checkerboards” exist in these archipelagoes that could possibly have been influenced by competitive interactions, and even “true checkerboards” can arise for reasons other than interspecific competition. The similarity between related species pairs (congeneric and within-guild pairs) and unrelated species pairs in their deviation from expectation of the number of islands shared and the overlap of their geographic ranges indicates that these are not distinct statistical populations, but rather a single population of species pairs. Our result, which is based on an examination of the distributional data alone, is consistent with the interpretation that, in these avifaunas, the distributions of congeneric, within-guild, and unrelated species pairs are shaped by a common set of biological and physical environmental processes.},
number = {11},
urldate = {2015-03-20},
journal = {Ecology},
author = {Connor, Edward F. and Collins, Michael D. and Simberloff, Daniel},
month = nov,
year = {2013},
keywords = {avifauna, bird guilds, Bismarck Archipelago, checkerboard distribution, convex hull overlap, geographic range overlap, interspecific competition, Solomon Islands, species pairs, Vanuatu},
pages = {2403--2414},
file = {Snapshot:/Users/davidjharris/Library/Application Support/Firefox/Profiles/0iu9o3qs.default/zotero/storage/NQZHZDPD/12-1471.html:text/html}
}
@book{wing_caret_2013,
title = {caret: {Classification} and {Regression} {Training}},
url = {http://CRAN.R-project.org/package=caret},
author = {Wing, Max Kuhn Contributions from Jed and Weston, Steve and Williams, Andre and Keefer, Chris and Engelhardt, Allan and Cooper, Tony},
year = {2013},
note = {R package version 5.16-04}
}
@article{gotelli_swap_2003,
title = {Swap algorithms in null model analysis},
journal = {Ecology},
author = {Gotelli, Nicholas J and Entsminger, Gary L},
year = {2003},
pages = {532--535}
}
@article{bell_behavioural_2005,
title = {Behavioural differences between individuals and two populations of stickleback ({Gasterosteus} aculeatus)},
volume = {18},
issn = {1010-061X},
url = {http://www.blackwell-synergy.com/links/doi/10.1111%2Fj.1420-9101.2004.00817.x},
doi = {10.1111/j.1420-9101.2004.00817.x},
number = {2},
journal = {Journal of Evolutionary Biology},