references.bib

@article{elith2009,
  title = {Species {{Distribution Models}}: {{Ecological Explanation}} and {{Prediction Across Space}} and {{Time}}},
  shorttitle = {Species {{Distribution Models}}},
  author = {Elith, Jane and Leathwick, John R.},
  year = {2009},
  journal = {Annual Review of Ecology, Evolution, and Systematics},
  volume = {40},
  number = {1},
  pages = {677--697},
  doi = {10.1146/annurev.ecolsys.110308.120159},
  url = {http://www.annualreviews.org/doi/abs/10.1146/annurev.ecolsys.110308.120159},
  urldate = {2013-01-18},
  abstract = {Species distribution models (SDMs) are numerical tools that combine observations of species occurrence or abundance with environmental estimates. They are used to gain ecological and evolutionary insights and to predict distributions across landscapes, sometimes requiring extrapolation in space and time. SDMs are now widely used across terrestrial, freshwater, and marine realms. Differences in methods between disciplines reflect both differences in species mobility and in ``established use.'' Model realism and robustness is influenced by selection of relevant predictors and modeling method, consideration of scale, how the interplay between environmental and geographic factors is handled, and the extent of extrapolation. Current linkages between SDM practice and ecological theory are often weak, hindering progress. Remaining challenges include: improvement of methods for modeling presence-only data and for model selection and evaluation; accounting for biotic interactions; and assessing model uncertainty.},
  keywords = {Niche,NICHE (Ecology),Niche theory,SDM,species distribution modeling,to read},
  file = {/home/paulo/OneDrive/Literature/ZoteroAttachments/Elith_Leathwick_2009_Annual Review of Ecology, Evolution, and Systematics/elith_leathwick_2009_species_distribution.pdf}
}

@article{phillipsMaximumEntropyModeling2006a,
  title = {Maximum Entropy Modeling of Species Geographic Distributions},
  author = {Phillips, Steven J. and Anderson, Robert P. and Schapire, Robert E.},
  year = {2006},
  journal = {Ecological modeling},
  volume = {190},
  number = {3},
  pages = {231--259},
  issn = {0304-3800},
  doi = {10.1016/j.ecolmodel.2005.03.026},
  url = {https://www.sciencedirect.com/science/article/pii/S030438000500267X},
  urldate = {2024-08-08},
  abstract = {The availability of detailed environmental data, together with inexpensive and powerful computers, has fueled a rapid increase in predictive modeling of species environmental requirements and geographic distributions. For some species, detailed presence/absence occurrence data are available, allowing the use of a variety of standard statistical techniques. However, absence data are not available for most species. In this paper, we introduce the use of the maximum entropy method (Maxent) for modeling species geographic distributions with presence-only data. Maxent is a general-purpose machine learning method with a simple and precise mathematical formulation, and it has a number of aspects that make it well-suited for species distribution modeling. In order to investigate the efficacy of the method, here we perform a continental-scale case study using two Neotropical mammals: a lowland species of sloth, Bradypus variegatus, and a small montane murid rodent, Microryzomys minutus. We compared Maxent predictions with those of a commonly used presence-only modeling method, the Genetic Algorithm for Rule-Set Prediction (GARP). We made predictions on 10 random subsets of the occurrence records for both species, and then used the remaining localities for testing. Both algorithms provided reasonable estimates of the species' range, far superior to the shaded outline maps available in field guides. All models were significantly better than random in both binomial tests of omission and receiver operating characteristic (ROC) analyses. The area under the ROC curve (AUC) was almost always higher for Maxent, indicating better discrimination of suitable versus unsuitable areas for the species. The Maxent modeling approach can be used in its present form for many applications with presence-only datasets, and merits further research and development.},
  keywords = {Distribution,Maximum entropy,Modeling,Niche,Range},
  file = {/home/paulo/Zotero/storage/AF3JSVVZ/S030438000500267X.html}
}

@article{phillipsOpeningBlackBox2017a,
  title = {Opening the Black Box: An Open-Source Release of {{Maxent}}},
  shorttitle = {Opening the Black Box},
  author = {Phillips, Steven J. and Anderson, Robert P. and Dud{\'i}k, Miroslav and Schapire, Robert E. and Blair, Mary E.},
  year = {2017},
  journal = {Ecography},
  volume = {40},
  number = {7},
  pages = {887--893},
  issn = {1600-0587},
  doi = {10.1111/ecog.03049},
  url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/ecog.03049},
  urldate = {2024-08-08},
  abstract = {This software note announces a new open-source release of the Maxent software for modeling species distributions from occurrence records and environmental data, and describes a new R package for fitting such models. The new release (ver. 3.4.0) will be hosted online by the American Museum of Natural History, along with future versions. It contains small functional changes, most notably use of a complementary log-log (cloglog) transform to produce an estimate of occurrence probability. The cloglog transform derives from the recently-published interpretation of Maxent as an inhomogeneous Poisson process (IPP), giving it a stronger theoretical justification than the logistic transform which it replaces by default. In addition, the new R package, maxnet, fits Maxent models using the glmnet package for regularized generalized linear models. We discuss the implications of the IPP formulation in terms of model inputs and outputs, treating occurrence records as points rather than grid cells and interpreting the exponential Maxent model (raw output) as as an estimate of relative abundance. With these two open-source developments, we invite others to freely use and contribute to the software.},
  copyright = {{\copyright} 2017 The Authors},
  langid = {english},
  file = {/home/paulo/Zotero/storage/BAIPA2JR/Phillips et al. - 2017 - Opening the black box an open-source release of Maxent.pdf}
}

@misc{phillipsMaxentSoftwareSpecies,
  title = {Maxent software for modeling species niches and distributions (Version 3.4.4)},
  author = {Phillips, S.J. and Dud{\'i}k, M. and Schapire, R.},
  url = {http://biodiversityinformatics.amnh.org/open_source/maxent/},
  year = {2024},
  urldate = {2024-08-08},
}

@Manual{maxnetcit,
    title = {maxnet: Fitting 'Maxent' Species Distribution Models with 'glmnet'},
    author = {Steven Phillips},
    year = {2021},
    note = {R package version 0.1.4},
    url = {https://CRAN.R-project.org/package=maxnet},
  }

@article{vanswaay2010,
  title = {Erebia Albergana. {{The IUCN Red List}} of {{Threatened Species}} 2010},
  shorttitle = {{{IUCN Red List}} of {{Threatened Species}}},
  author = {{van Swaay}, C. and Wynhoff, Irma and Verovnik, R and Wiemers, M and Lopez Munguira, M and Maes, D and Sasic, M and Verstrael, T and Warren, M and Settele, J},
  year = {2010},
  journal = {IUCN Red List of Threatened Species},
  doi = {10.2305/IUCN.UK.2010-1.RLTS.T173278A6984115.en},
  url = {https://www.iucnredlist.org/en},
  urldate = {2024-08-28},
  abstract = {Established in 1964, the IUCN Red List of Threatened Species has evolved to become the world's most comprehensive information source on the global conservation status of animal, fungi and plant species.},
  file = {/home/paulo/Zotero/storage/36GZ9PDB/6984115.html}
}

@misc{grassdevelopmentteam2024,
  title = {{{GRASS GIS}}},
  author = {{GRASS Development Team}},
  year = {2024},
  doi = {10.5281/zenodo.5176030},
  url = {https://grass.osgeo.org},
  urldate = {2024-10-20},
  publisher = {Open Source Geospatial Foundation},
}

@article{fick2017,
  ids = {fickWorldClimNew1km2017},
  title = {{{WorldClim}} 2: New 1-km Spatial Resolution Climate Surfaces for Global Land Areas},
  shorttitle = {{{WorldClim}} 2},
  author = {Fick, Stephen E. and Hijmans, Robert J.},
  year = {2017},
  month = oct,
  journal = {International Journal of Climatology},
  volume = {37},
  number = {12},
  pages = {4302--4315},
  issn = {0899-8418, 1097-0088},
  doi = {10.1002/joc.5086},
  url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/joc.5086},
  urldate = {2021-03-23},
}

@article{jarnevich2015,
  title = {Caveats for Correlative Species Distribution Modeling},
  author = {Jarnevich, Catherine S. and Stohlgren, Thomas J. and Kumar, Sunil and Morisette, Jeffery T. and Holcombe, Tracy R.},
  year = {2015},
  month = sep,
  journal = {Ecological Informatics},
  volume = {29},
  pages = {6--15},
  issn = {15749541},
  doi = {10.1016/j.ecoinf.2015.06.007},
  url = {http://linkinghub.elsevier.com/retrieve/pii/S1574954115000977},
  urldate = {2016-03-26},
  langid = {english},
  keywords = {presence absence,sampling,SDM,species distribution modeling},
  annotation = {PhD\_034},
  file = {/home/paulo/OneDrive/Literature/ZoteroAttachments/Jarnevich et al_2015_Ecological Informatics/jarnevich_et_al_2015_caveats_for_correlative.pdf}
}

@article{phillips2009a,
  title = {Sample Selection Bias and Presence-Only Distribution Models: Implications for Background and Pseudo-Absence Data},
  shorttitle = {Sample Selection Bias and Presence-Only Distribution Models},
  author = {Phillips, Steven J. and Dud{\'i}k, Miroslav and Elith, Jane and Graham, Catherine H. and Lehmann, Anthony and Leathwick, John and Ferrier, Simon},
  year = {2009},
  journal = {Ecological Applications},
  volume = {19},
  number = {1},
  pages = {181--197},
  issn = {1051-0761},
  doi = {10.1890/07-2153.1},
  url = {http://www.esajournals.org/doi/abs/10.1890/07-2153.1},
  urldate = {2009-11-06},
  keywords = {data analysis,LiteratureOverview,maxent,model bias,model uncertainty,predictive power,SDM,species distribution modeling,statistics,Uncertainty},
  annotation = {PhD\_042},
  file = {/home/paulo/OneDrive/Literature/ZoteroAttachments/Phillips et al_2009_Ecological Applications/phillips_et_al_2009_sample_selection_bias.pdf}
}

@article{kramerSampling2013,
  title = {The Importance of Correcting for Sampling Bias in {{MaxEnt}} Species Distribution Models},
  author = {{Kramer-Schadt}, Stephanie and Niedballa, J{\"u}rgen and Pilgrim, John D. and Schr{\"o}der, Boris and Lindenborn, Jana and Reinfelder, Vanessa and Stillfried, Milena and Heckmann, Ilja and Scharf, Anne K. and Augeri, Dave M. and Cheyne, Susan M. and Hearn, Andrew J. and Ross, Joanna and Macdonald, David W. and Mathai, John and Eaton, James and Marshall, Andrew J. and Semiadi, Gono and Rustam, Rustam and Bernard, Henry and Alfred, Raymond and Samejima, Hiromitsu and Duckworth, J. W. and {Breitenmoser-Wuersten}, Christine and Belant, Jerrold L. and Hofer, Heribert and Wilting, Andreas},
  year = {2013},
  journal = {Diversity and Distributions},
  volume = {19},
  number = {11},
  pages = {1366--1379},
  issn = {1472-4642},
  doi = {10.1111/ddi.12096},
  urldate = {2014-10-04},
  langid = {english},
  annotation = {PhD\_045}
}

@article{fourcade2014,
  title = {Mapping {{Species Distributions}} with {{MAXENT Using}} a {{Geographically Biased Sample}} of {{Presence Data}}: {{A Performance Assessment}} of {{Methods}} for {{Correcting Sampling Bias}}},
  shorttitle = {Mapping {{Species Distributions}} with {{MAXENT Using}} a {{Geographically Biased Sample}} of {{Presence Data}}},
  author = {Fourcade, Yoan and Engler, Jan O. and R{\"o}dder, Dennis and Secondi, Jean},
  year = {2014},
  month = may,
  journal = {PLOS ONE},
  volume = {9},
  number = {5},
  pages = {e97122},
  publisher = {{Public Library of Science}},
  issn = {1932-6203},
  doi = {10.1371/journal.pone.0097122},
  urldate = {2023-12-02},
  langid = {english},
  keywords = {Biogeography,Cartography,Conservation biology,Conservation science,Ecological niches,Environmental geography,Geographic distribution,Geography},
  file = {/home/paulo/OneDrive/Literature/ZoteroAttachments/Fourcade et al_2014_Mapping Species Distributions with MAXENT Using a Geographically Biased Sample.pdf}
}

@article{beck2014a,
  title = {Spatial Bias in the {{GBIF}} Database and Its Effect on Modeling Species' Geographic Distributions},
  author = {Beck, Jan and B{\"o}ller, Marianne and Erhardt, Andreas and Schwanghart, Wolfgang},
  year = {2014},
  month = jan,
  journal = {Ecological Informatics},
  volume = {19},
  pages = {10--15},
  issn = {1574-9541},
  doi = {10.1016/j.ecoinf.2013.11.002},
  url = {https://www.sciencedirect.com/science/article/pii/S1574954113001155},
  urldate = {2024-10-20},
  abstract = {Species distribution modeling, in combination with databases of specimen distribution records, is advocated as a solution to the problem of distributional data limitation in biogeography and ecology. The global biodiversity information facility (GBIF), a portal that collates digitized collection and survey data, is the largest online provider of distribution records. However, all distributional databases are spatially biassed due to uneven effort of sampling, data storage and mobilization. Such bias is particularly pronounced in GBIF, where nation-wide differences in funding and data sharing lead to huge differences in contribution to GBIF. We use a common Eurasian butterfly (Aglais urticae) as an exemplar taxon to provide evidence that range model quality is decreasing due to the spatial clustering of distributional records in GBIF. Furthermore, we show that such loss of model quality would go unnoticed with standard methods of model quality evaluation. Using evaluations of model predictions of the Swiss distribution of the species, we compare distribution models of full data with data where a subsampling procedure removes spatial bias at the cost of record numbers, but not of spatial extent of records. We show that data with less spatial bias produce better predictive models even though they are based on less input data. Our subsampling routine may therefore be a suitable method to reduce the impact of spatial bias to species distribution models. Our results warn of automatized applications of species distribution models to distributional databases (as has been advocated and implemented), as internal model evaluation did not show the decline of model quality with increased spatial bias (but rather the opposite) while expert evaluation clearly did.},
  keywords = {AUC,CSCF,Ecological niche modeling,Lepidoptera,Maxent,Small tortoiseshell},
  file = {/home/paulo/OneDrive/Literature/ZoteroAttachments/Beck_2014/Beck et al. - 2014 - Spatial bias in the GBIF database and its effect on modeling species' geographic distributions.pdf;/home/paulo/Zotero/storage/IY7F6RJY/S1574954113001155.html}
}

@article{acevedo2012,
  title = {Delimiting the Geographical Background in Species Distribution modeling},
  author = {Acevedo, Pelayo and {Jim{\'e}nez-Valverde}, Alberto and Lobo, Jorge M. and Real, Raimundo},
  year = {2012},
  journal = {Journal of Biogeography},
  volume = {39},
  number = {8},
  pages = {1383--1390},
  issn = {1365-2699},
  doi = {10.1111/j.1365-2699.2012.02713.x},
  url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1365-2699.2012.02713.x},
  urldate = {2023-10-18},
  abstract = {Aim The extent of the study area (geographical background, GB) can strongly affect the results of species distribution models (SDMs), but as yet we lack objective and practicable criteria for delimiting the appropriate GB. We propose an approach to this problem using trend surface analysis (TSA) and provide an assessment of the effects of varying GB extent on the performance of SDMs for four species. Location Mainland Spain. Methods Using data for four well known wild ungulate species and different GBs delimited with a TSA, we assessed the effects of GB extent on the predictive performance of SDMs: specifically on model calibration (Miller's statistic) and discrimination (area under the curve of the receiver operating characteristic plot, AUC; sensitivity and specificity), and on the tendency of the models to predict environmental potential when they are projected beyond their training area. Results In the training area, discrimination significantly increased and calibration decreased as the GB was enlarged. In contrast, as GB was enlarged, both discriminatory power and calibration decreased when assessed in the core area of the species distributions. When models trained using small GBs were projected beyond their training area, they showed a tendency to predict higher environmental potential for the species than those models trained using large GBs. Main conclusions By restricting GB extent using a geographical criterion, model performance in the core area of the species distribution can be significantly improved. Large GBs make models demonstrate high discriminatory power but are barely informative. By delimiting GB using a geographical criterion, the effect of historical events on model parameterization may be reduced. Thus purely environmental models are obtained that, when projected onto a new scenario, depict the potential distribution of the species. We therefore recommend the use of TSA in geographically delimiting the GB for use in SDMs.},
  copyright = {{\copyright} 2012 Blackwell Publishing Ltd},
  langid = {english},
  keywords = {Calibration,discrimination,environmental potential,extent,geographical background,historical factors,Spain,species distribution models,trend surface analysis,ungulate distributions},
  file = {/home/paulo/OneDrive/Literature/ZoteroAttachments/Acevedo et al_2012_Journal of Biogeography/Acevedo et al_2012_Delimiting the geographical background in species distribution modeling.pdf}
}

@article{barveCrucialRoleAccessible2011,
  title = {The Crucial Role of the Accessible Area in Ecological Niche Modeling and Species Distribution Modeling},
  author = {Barve, Narayani and Barve, Vijay and {Jim{\'e}nez-Valverde}, Alberto and {Lira-Noriega}, Andr{\'e}s and Maher, Sean P. and Peterson, A. Townsend and Sober{\'o}n, Jorge and Villalobos, Fabricio},
  year = {2011},
  month = jun,
  journal = {Ecological modeling},
  volume = {222},
  number = {11},
  pages = {1810--1819},
  issn = {0304-3800},
  doi = {10.1016/j.ecolmodel.2011.02.011},
  url = {https://www.sciencedirect.com/science/article/pii/S0304380011000780},
  urldate = {2024-09-21},
  abstract = {Using known occurrences of species and correlational modeling approaches has become a common paradigm in broad-scale ecology and biogeography, yet important aspects of the methodology remain little-explored in terms of conceptual basis. Here, we explore the conceptual and empirical reasons behind choice of extent of study area in such analyses, and offer practical, but conceptually justified, reasoning for such decisions. We assert that the area that has been accessible to the species of interest over relevant time periods represents the ideal area for model development, testing, and comparison.},
  keywords = {Accessible area,Area of distribution,Ecological niche,Geographic extent,Pleistocene}
}

@article{phillipsSampleSelectionBias2009,
  title = {Sample Selection Bias and Presence-Only Distribution Models: Implications for Background and Pseudo-Absence Data},
  shorttitle = {Sample Selection Bias and Presence-Only Distribution Models},
  author = {Phillips, Steven J. and Dud{\'i}k, Miroslav and Elith, Jane and Graham, Catherine H. and Lehmann, Anthony and Leathwick, John and Ferrier, Simon},
  year = {2009},
  journal = {Ecological Applications},
  volume = {19},
  number = {1},
  pages = {181--197},
  issn = {1051-0761},
  doi = {10.1890/07-2153.1},
  url = {http://www.esajournals.org/doi/abs/10.1890/07-2153.1},
  urldate = {2009-11-06},
  keywords = {data analysis,LiteratureOverview,maxent,model bias,model uncertainty,predictive power,SDM,species distribution modeling,statistics,Uncertainty},
  annotation = {PhD\_042},
  file = {/home/paulo/OneDrive/Literature/ZoteroAttachments/Phillips et al_2009_Ecological Applications/phillips_et_al_2009_sample_selection_bias.pdf}
}

@article{wardPresenceOnlyDataEM2009,
  title = {Presence-{{Only Data}} and the {{EM Algorithm}}},
  author = {Ward, Gill and Hastie, Trevor and Barry, Simon and Elith, Jane and Leathwick, John R.},
  year = {2009},
  month = jun,
  journal = {Biometrics},
  volume = {65},
  number = {2},
  pages = {554--563},
  issn = {0006-341X},
  doi = {10.1111/j.1541-0420.2008.01116.x},
  url = {https://doi.org/10.1111/j.1541-0420.2008.01116.x},
  urldate = {2024-09-22},
  abstract = {In ecological modeling of the habitat of a species, it can be prohibitively expensive to determine species absence. Presence-only data consist of a sample of locations with observed presences and a separate group of locations sampled from the full landscape, with unknown presences. We propose an expectation--maximization algorithm to estimate the underlying presence--absence logistic model for presence-only data. This algorithm can be used with any off-the-shelf logistic model. For models with stepwise fitting procedures, such as boosted trees, the fitting process can be accelerated by interleaving expectation steps within the procedure. Preliminary analyses based on sampling from presence--absence records of fish in New Zealand rivers illustrate that this new procedure can reduce both deviance and the shrinkage of marginal effect estimates that occur in the naive model often used in practice. Finally, it is shown that the population prevalence of a species is only identifiable when there is some unrealistic constraint on the structure of the logistic model. In practice, it is strongly recommended that an estimate of population prevalence be provided.},
  file = {/home/paulo/OneDrive/Literature/ZoteroAttachments/Ward_2009/Ward et al. - 2009 - Presence-Only Data and the EM Algorithm.pdf}
}

@article{phillipsModelingSpeciesDistributions2008,
  title = {Modeling of Species Distributions with {{Maxent}}: New Extensions and a Comprehensive Evaluation},
  shorttitle = {Modeling of Species Distributions with {{Maxent}}},
  author = {Phillips, S. J and Dudik, M.},
  year = {2008},
  journal = {Ecography},
  volume = {31},
  number = {2},
  pages = {161--175},
  keywords = {Cited in: TSEB,Paper: Conservation east Africa,Paper: TSEB Ethiopia},
  annotation = {\#13},
  file = {/home/paulo/OneDrive/Literature/ZoteroAttachments/Phillips_Dudik_2008_Ecography/phillips_dudik_2008_modeling_of_species.pdf}
}

@article{barbet-massin2012,
  title = {Selecting Pseudo-Absences for Species Distribution Models: How, Where and How Many?: {{How}} to Use Pseudo-Absences in Niche modeling?},
  shorttitle = {Selecting Pseudo-Absences for Species Distribution Models},
  author = {{Barbet-Massin}, Morgane and Jiguet, Fr{\'e}d{\'e}ric and Albert, C{\'e}cile H{\'e}l{\`e}ne and Thuiller, Wilfried},
  year = {2012},
  month = apr,
  journal = {Methods in Ecology and Evolution},
  volume = {3},
  number = {2},
  pages = {327--338},
  issn = {2041210X},
  doi = {10.1111/j.2041-210X.2011.00172.x},
  url = {http://doi.wiley.com/10.1111/j.2041-210X.2011.00172.x},
  urldate = {2018-03-24},
  abstract = {1. Species distribution models are increasingly used to address questions in conservation biology, ecology and evolution. The most effective species distribution models require data on both species presence and the available environmental conditions (known as background or pseudo-absence data) in the area. However, there is still no consensus on how and where to sample these pseudoabsences and how many.},
  langid = {english},
  keywords = {presence absence,sampling,SDM,species distribution modeling},
  file = {/home/paulo/OneDrive/Literature/ZoteroAttachments/Barbet-Massin et al_2012_Methods in Ecology and Evolution/barbet-massin_et_al_2012_selecting.pdf}
}

@article{moua2020,
  title = {Correcting the Effect of Sampling Bias in Species Distribution Modeling -- {{A}} New Method in the Case of a Low Number of Presence Data},
  author = {Moua, Yi and Roux, Emmanuel and Seyler, Fr{\'e}d{\'e}rique and Briolant, S{\'e}bastien},
  year = {2020},
  month = may,
  journal = {Ecological Informatics},
  volume = {57},
  pages = {101086},
  issn = {15749541},
  doi = {10.1016/j.ecoinf.2020.101086},
  url = {https://linkinghub.elsevier.com/retrieve/pii/S1574954120300364},
  urldate = {2023-12-02},
  abstract = {Species distribution models that only require presence data provide potentially inaccurate results due to sampling bias and presence data scarcity. Methods have been proposed in the literature to minimize the effects of sampling bias, but without explicitly considering the issue of sample size.},
  langid = {english},
  file = {/home/paulo/OneDrive/Literature/ZoteroAttachments/Moua et al_2020_Correcting the effect of sampling bias in species distribution modeling – A new.pdf}
}

@article{whitford2024,
  title = {The Influence of the Number and Distribution of Background Points in Presence-Background Species Distribution Models},
  author = {Whitford, Anna M. and Shipley, Benjamin R. and McGuire, Jenny L.},
  year = {2024},
  month = feb,
  journal = {Ecological modeling},
  volume = {488},
  pages = {110604},
  issn = {0304-3800},
  doi = {10.1016/j.ecolmodel.2023.110604},
  url = {https://www.sciencedirect.com/science/article/pii/S0304380023003344},
  urldate = {2024-03-13},
  abstract = {Species distribution models (SDMs), which relate recorded observations (presences) and absences or background points to environmental characteristics, are powerful tools used to generate hypotheses about the biogeography, ecology, and conservation of species. Although many researchers have examined the effects of presence and background point distributions on model outputs, they have not systematically evaluated the effects of various methods of background point sampling on the performance of a single model algorithm across many species. Therefore, a consensus on the preferred methods of background point sampling is lacking. Here, we conducted presence-background SDMs for 20 vertebrate species in North America under a variety of background point conditions, varying the number of background points used, the size of the buffer used to constrain the background points around the occurrences, and the percentage of background points sampled within the buffer (``spatial weighting''). We evaluated the accuracy and transferability of the models using Boyce index, overlap with expert-generated range maps, and area overpredicted and underpredicted by the SDM (and AUC for comparability with other studies). SDM performance is highly dependent on the species modelled but is affected by the number and spread of background points. Models with little spatial weighting had high accuracy (overlap values), but extreme extrapolation errors and overprediction. In contrast, SDMs with high transferability (high Boyce index values and low overprediction) had moderate-to-high spatial weighting. These results emphasize the importance of both background points and evaluation metric selection in SDMs. For other, more successful metrics, using many background points with spatial weighting may be preferred for models with large extents. These results can assist researchers in selecting the background point parameters most relevant for their research question, allowing them to fine-tune their hypotheses on the distribution of species through space and time.},
  keywords = {Boyce index,MaxEnt,Model evaluation,North America,Simulation,Species ranges,Transferability},
  file = {/home/paulo/OneDrive/Literature/ZoteroAttachments/Whitford et al_2024_Ecological modeling/Whitford et al_2024_The influence of the number and distribution of background points in.pdf}
}

@article{graham2003,
  title = {Confronting Multicollinearity in Ecological Multiple Regression},
  author = {Graham, Michael H.},
  year = {2003},
  journal = {Ecology},
  volume = {84},
  number = {11},
  pages = {2809--2815},
  issn = {0012-9658},
  doi = {10.1890/02-3114},
  url = {http://www.esajournals.org/doi/abs/10.1890/02-3114},
  urldate = {2014-06-27},
  abstract = {The natural complexity of ecological communities regularly lures ecologists to collect elaborate data sets in which confounding factors are often present. Although multiple regression is commonly used in such cases to test the individual effects of many explanatory variables on a continuous response, the inherent collinearity (multicollinearity) of confounded explanatory variables encumbers analyses and threatens their statistical and inferential interpretation. Using numerical simulations, I quantified the impact of multicollinearity on ecological multiple regression and found that even low levels of collinearity bias analyses (r {$\geq$} 0.28 or r2 {$\geq$} 0.08), causing (1) inaccurate model parameterization, (2) decreased statistical power, and (3) exclusion of significant predictor variables during model creation. Then, using real ecological data, I demonstrated the utility of various statistical techniques for enhancing the reliability and interpretation of ecological multiple regression in the presence of multicollinearity.},
  keywords = {Cited in: species_climate,Paper: Fire Ethiopia,Paper: TSEB Ethiopia},
  annotation = {PhD\_267}
}

@article{craney2002,
  title = {Model-{{Dependent Variance Inflation Factor Cutoff Values}}},
  author = {Craney, Trevor A. and Surles, James G.},
  year = {2002},
  month = mar,
  journal = {Quality Engineering},
  volume = {14},
  number = {3},
  pages = {391--403},
  issn = {0898-2112},
  doi = {10.1081/QEN-120001878},
  url = {http://dx.doi.org/10.1081/QEN-120001878},
  urldate = {2016-03-23},
  abstract = {When creating designed experiments, it is not always possible to run the experiment at the exact settings required to maintain orthogonal effects. However, this is not measurement error when precise measurements of the settings can be made once the experiment begins. A comparison is made for a 15-run Box--Behnken design using both the intended design settings and the actual design settings. Variance inflation factors are used to measure the induced collinearity in the effects. Two cutoff values are suggested for use to determine when an effect's variance inflation factor is too large to keep that effect in the model.},
  file = {/home/paulo/OneDrive/Literature/ZoteroAttachments/Craney_Surles_2002_Quality Engineering/craney_surles_2002_model-dependent_variance.pdf}
}

@article{loboAUCMisleadingMeasure2008,
  title = {{{AUC}}: A Misleading Measure of the Performance of Predictive Distribution Models},
  author = {Lobo, Jorge M. and {Jim{\'e}nez-Valverde}, Alberto and Real, Raimundo},
  year = {2008},
  journal = {Global Ecology and Biogeography},
  volume = {17},
  number = {2},
  pages = {145--151},
  issn = {1466-8238},
  url = {http://dx.doi.org/10.1111/j.1466-8238.2007.00358.x},
  keywords = {AUC,Cited in: species_climate,Cited in: TSEB,distribution modeling,ecological statistics,goodness-of-fit,model accuracy,Paper: TSEB Ethiopia,ROC curve,species distribution model},
  file = {/home/paulo/OneDrive/Literature/ZoteroAttachments/Lobo et al_2008_Global Ecology and Biogeography/lobo_et_al_2008_auc.pdf}
}





@misc{shepherdContinentPolygons2020,
  title = {Continent {{Polygons}}},
  author = {Shepherd, Stephanie},
  year = {2020},
  pages = {8429301 Bytes},
  publisher = {figshare},
  doi = {10.6084/M9.FIGSHARE.12555170.V3},
  url = {https://figshare.com/articles/Continent_Polygons/12555170/3},
  urldate = {2024-08-20},
  abstract = {Shapefiles for each conttinent, subset of publicly available shapefile from ESRI.},
  copyright = {Creative Commons Attribution 4.0 International},
  keywords = {Uncategorized}
}