@article{moreno:2009,
 ISSN = {07490208, 15515036},
 URL = {http://www.jstor.org/stable/25737965},
 abstract = {Coastal and marine tourism is a growing sector, in terms of both participation and economic relevance. For its enjoyment and safety, coastal and marine recreation requires suitable weather conditions, and attractive environments. Climate change will impact both. At the same time, tourism is an important source of the greenhouse gases (GHG) that are causing climate change. To date, and with the exception of beach recreation, most studies on climate change and tourism have concentrated either on impacts on whole countries and world regions or on certain ecosystems like coasts, focusing their attention in only one activity and overlooking the great diversity and very different weather requirements of the rest of activities these environments support. This paper reviews the importance of coastal and marine environments for recreation, presents current knowledge on the impacts of climate change on activities in these environments and identifies the main scientific gaps. This study provides an updated knowledge base for understanding climate change relationship with coastal and marine tourism and proposes a number of future research lines.},
 author = {A. Moreno and B. Amelung},
 journal = {Journal of Coastal Research},
 pages = {1140--1144},
 publisher = {Coastal Education & Research Foundation, Inc.},
 title = "{Climate Change and Coastal & Marine Tourism: Review and Analysis}",
 urldate = {2025-03-03},
 year = 2009
}

@article{breiman:2001,
  title={Random forests},
  author={Breiman, Leo},
  journal={Machine learning},
  volume={45},
  pages={5--32},
  year={2001},
  doi={10.1023/A:1010933404324},
  publisher={Springer}
}
@article{devries:2024,
  title={CASCADE: Dataset of extant coccolithophore size, carbon content and global distribution},
  author={{de Vries}, Joost and Poulton, Alex J and Young, Jeremy R and Monteiro, Fanny M and Sheward, Rosie M and Johnson, Roberta and Hagino, Kyoko and Ziveri, Patrizia and Wolf, Levi J},
  journal={Scientific data},
  volume={11},
  number={1},
  pages={920},
  year={2024},
  doi={10.1038/s41597-024-03724-z},
  publisher={Nature Publishing Group UK London}
}
@article{dyck:2010,
ISSN = {1573-6989},
URL = {https://doi.org/10.1007/s10818-010-9088-3},
abstract = {Our goal in this paper is to estimate the total output in an economy that is currently dependent (at least partially) on current fisheries output. We therefore applied the Leontief technological coefficients at current production and then estimate total output supported throughout the economy at the current level of production. Estimates of gross revenue from capture fisheries suggest that the direct value of output for this sector is US $80–85 billion annually (Sumaila et al., Journal of Bioeconomics 9(1):39–51, 2007; Willmann et al., The Sunken Billions, World Bank, FAO, Washington DC, Rome, 2009). However, as a primary or a potential economic base industry, there are a vast number of secondary economic activities—from boat building to international transport—that are supported by world fisheries, yet these related activities are rarely considered when evaluating the economic impact of fisheries. This study applies an input–output methodology to estimate the total direct, indirect, and induced impact of marine capture fisheries on the world economy. While results suggest that there is a great deal of variation in fishing output multipliers between regions and countries, when we apply the output multipliers to the capture fisheries sector at the global level, we find that significant indirect and induced effects place the impact of this sector to world output nearly three times larger than the value of landings at first sale, at between US $225 and 240 billion per year.},
author = {Andrew J. Dyck and U. Rashid Sumaila},
journal = {Journal of Bioeconomics},
pages = {227--243},
title = "{Economic impact of ocean fish populations in the global fishery}",
year = 2010,
doi = {10.1007/s10818-010-9088-3},
urldate = {2025-03-03}
}

@article{hauck:2023,
URL = {https://doi.org/10.1098/rsta.2022.0063},
abstract = {Estimates of ocean CO2 uptake from global ocean biogeochemistry models and p⁢CO2-based data products differ substantially, especially in high latitudes and in the trend of the CO2 uptake since 2000. Here, we assess the effect of data sparsity on two p⁢CO2-based estimates by subsampling output from a global ocean biogeochemistry model. The estimates of the ocean CO2 uptake are improved from a sampling scheme that mimics present-day sampling to an ideal sampling scheme with 1000 evenly distributed sites. In particular, insufficient sampling has given rise to strong biases in the trend of the ocean carbon sink in the p⁢CO2 products. The overestimation of the CO2 flux trend by 20–35% globally and 50–130% in the Southern Ocean with the present-day sampling is reduced to less than 15% with the ideal sampling scheme. A substantial overestimation of the decadal variability of the Southern Ocean carbon sink occurs in one product and appears related to a skewed data distribution in p⁢CO2 space. With the ideal sampling, the bias in the mean CO2 flux is reduced from 9–12% to 2–9% globally and from 14–26% to 5–17% in the Southern Ocean. On top of that, discrepancies of about 0.4 PgC yr−1 (15%) persist due to uncertainties in the gas-exchange calculation.},
author = {Judith Hauck and Cara Nissen and Peter Landschützer and Christian Rödenbeck and Seth Bushinsky and Are Olsen},
journal = {Philosophical Transactions of the Royal Society A},
volume = {381},
title = "{Sparse observations induce large biases in estimates of the global ocean CO2 sink: an ocean model subsampling experiment}",
year = 2023,
doi = {10.1098/rsta.2022.0063},
urldate = {2025-03-03}
}

@article{meyer:2021,
author = {Meyer, Hanna and Pebesma, Edzer},
title = {Predicting into unknown space? Estimating the area of applicability of spatial prediction models},
journal = {Methods in Ecology and Evolution},
volume = {12},
number = {9},
pages = {1620-1633},
keywords = {machine learning, model transferability, predictive modelling, Random Forest, remote sensing, spatial mapping, uncertainty},
doi = {10.1111/2041-210X.13650},
url = {https://besjournals.onlinelibrary.wiley.com/doi/abs/10.1111/2041-210X.13650},
eprint = {https://besjournals.onlinelibrary.wiley.com/doi/pdf/10.1111/2041-210X.13650},
abstract = {Abstract Machine learning algorithms have become very popular for spatial mapping of the environment due to their ability to fit nonlinear and complex relationships. However, this ability comes with the disadvantage that they can only be applied to new data if these are similar to the training data. Since spatial mapping requires predictions to new geographic space which in many cases goes along with new predictor properties, a method to assess the area to which a prediction model can be reliably applied is required. Here, we suggest a methodology that delineates the ‘area of applicability’ (AOA) that we define as the area where we enabled the model to learn about relationships based on the training data, and where the estimated cross-validation performance holds. We first propose a ‘dissimilarity index’ (DI) that is based on the minimum distance to the training data in the multidimensional predictor space, with predictors being weighted by their respective importance in the model. The AOA is then derived by applying a threshold which is the (outlier-removed) maximum DI of the training data derived via cross-validation. We further use the relationship between the DI and the cross-validation performance to map the estimated performance of predictions. We illustrate the approach in a simulated case study chosen to mimic ecological realities and test the credibility by using a large set of simulated data. The simulation studies showed that the prediction error within the AOA is comparable to the cross-validation error of the trained model, while the cross-validation error does not apply outside the AOA. This applies to models being trained with randomly distributed training data, as well as when training data are clustered in space and where spatial cross-validation is applied. Using the relationship between DI and cross-validation performance showed potential to limit predictions to the area where a user-defined performance applies. We suggest to add the AOA computation to the modeller's standard toolkit and to present predictions for the AOA only. We further suggest to report a map of DI-dependent performance estimates alongside prediction maps and complementary to (cross-)validation performance measures and the common uncertainty estimates.},
year = {2021}
}

@article{hoyer:2017,
  title     = {xarray: {N-D} labeled arrays and datasets in {Python}},
  author    = {Hoyer, S. and J. Hamman},
  journal   = {Journal of Open Research Software},
  volume    = {5},
  number    = {1},
  year      = {2017},
  publisher = {Ubiquity Press},
  doi       = {10.5334/jors.148},
  url       = {https://doi.org/10.5334/jors.148}
}

@software{pandas:2025,
    author       = {{The pandas development team}},
    title        = {pandas-dev/pandas: Pandas},
    year         = 2025,
    publisher    = {Zenodo},
    version      = {latest},
    doi          = {10.5281/zenodo.3509134},
    url          = {https://doi.org/10.5281/zenodo.3509134}
}

@InProceedings{mckinney:2010,
  author    = {{W}es {M}c{K}inney },
  title     = {{D}ata {S}tructures for {S}tatistical {C}omputing in {P}ython },
  booktitle = {{P}roceedings of the 9th {P}ython in {S}cience {C}onference },
  pages     = {56 - 61 },
  year      = {2010 },
  editor    = {{S}t\'efan van der {W}alt and {J}arrod {M}illman },
  doi       = {10.25080/Majora-92bf1922-00a }
}

@Article{         harris:2020,
 title         = {Array programming with {NumPy}},
 author        = {Charles R. Harris and K. Jarrod Millman and St{\'{e}}fan J.
                 van der Walt and Ralf Gommers and Pauli Virtanen and David
                 Cournapeau and Eric Wieser and Julian Taylor and Sebastian
                 Berg and Nathaniel J. Smith and Robert Kern and Matti Picus
                 and Stephan Hoyer and Marten H. van Kerkwijk and Matthew
                 Brett and Allan Haldane and Jaime Fern{\'{a}}ndez del
                 R{\'{i}}o and Mark Wiebe and Pearu Peterson and Pierre
                 G{\'{e}}rard-Marchant and Kevin Sheppard and Tyler Reddy and
                 Warren Weckesser and Hameer Abbasi and Christoph Gohlke and
                 Travis E. Oliphant},
 year          = {2020},
 month         = sep,
 journal       = {Nature},
 volume        = {585},
 number        = {7825},
 pages         = {357--362},
 doi           = {10.1038/s41586-020-2649-2},
 publisher     = {Springer Science and Business Media {LLC}},
 url           = {https://doi.org/10.1038/s41586-020-2649-2}
}

@software{jai:2025,
  author       = {Jai Ram Rideout and
                  Greg Caporaso and
                  Evan Bolyen and
                  Daniel McDonald and
                  Yoshiki Vázquez Baeza and
                  Jorge Cañardo Alastuey and
                  Anders Pitman and
                  Jamie Morton and
                  Qiyun Zhu and
                  Jose Navas and
                  Kestrel Gorlick and
                  Justine Debelius and
                  Zech Xu and
                  Matt Aton and
                  llcooljohn and
                  Joshua Shorenstein and
                  Laurent Luce and
                  Will Van Treuren and
                  John Chase and
                  charudatta-navare and
                  Antonio Gonzalez and
                  Colin J. Brislawn and
                  Weronika Patena and
                  Karen Schwarzberg and
                  teravest and
                  Jens Reeder and
                  Igor Sfiligoi and
                  shiffer1 and
                  nbresnick and
                  Dr. K. D. Murray},
  title        = {scikit-bio/scikit-bio: scikit-bio 0.6.3},
  month        = jan,
  year         = 2025,
  publisher    = {Zenodo},
  version      = {0.6.3},
  doi          = {10.5281/zenodo.14640761},
  url          = {https://doi.org/10.5281/zenodo.14640761},
  swhid        = {swh:1:dir:3d7e418ed20f8d5c588d18a83c466ee2fb0da6eb
                   ;origin=https://doi.org/10.5281/zenodo.593387;visi
                   t=swh:1:snp:91a2b0adb7ab0ff47a8367fcd810c0c0487302
                   6d;anchor=swh:1:rel:519bdf1fef07db38679a1f541b0311
                   bf37a24d84;path=scikit-bio-scikit-bio-9fbc82f
                  },
}

@article{Pedregosa:2011,
  title={Scikit-learn: Machine Learning in {P}ython},
  author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
          and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
          and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
          Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
  journal={Journal of Machine Learning Research},
  volume={12},
  pages={2825--2830},
  year={2011},
  doi = {10.48550/arXiv.1201.0490}
}

@inproceedings{Chen:2016,
 author = {Chen, Tianqi and Guestrin, Carlos},
 title = {{XGBoost}: A Scalable Tree Boosting System},
 booktitle = {Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
 series = {KDD '16},
 year = {2016},
 isbn = {978-1-4503-4232-2},
 location = {San Francisco, California, USA},
 pages = {785--794},
 numpages = {10},
 url = {http://doi.acm.org/10.1145/2939672.2939785},
 doi = {10.1145/2939672.2939785},
 acmid = {2939785},
 publisher = {ACM},
 address = {New York, NY, USA},
 keywords = {large-scale machine learning},
}

@misc{evans:2017,
  title={YAML Ain’t Markup Language (YAML™) Version 1.2},
  author={Evans, Clark and Ben-Kiki, Oren and d{\"o}t Net, I},
  year={2021},
  url={https://yaml.org/spec/1.2.2/}
}

@software{joblib:2024,
author = {{The joblib developers}},
doi = {10.5281/zenodo.14915601},
year = {2025},
title = {{joblib}},
url = {https://github.com/joblib/joblib},
version = {latest}
}
@article{anderson:2023,
  title={elapid: Species distribution modeling tools for Python},
  author={Anderson, Christopher B},
  journal={Journal of Open Source Software},
  volume={8},
  number={84},
  pages={4930},
  year={2023},
  doi={10.21105/joss.04930}
}
@article{thuiller:2009,
  title={BIOMOD--a platform for ensemble forecasting of species distributions},
  author={Thuiller, Wilfried and Lafourcade, Bruno and Engler, Robin and Ara{\'u}jo, Miguel B},
  journal={Ecography},
  volume={32},
  number={3},
  pages={369--373},
  year={2009},
  doi={10.1111/j.1600-0587.2008.05742.x},
  publisher={Wiley Online Library}
}
@article {devries:2025,
	author = {{de Vries}, Joost and Monteiro, Fanny M. and Poulton, Alex J. and Wiseman, Nicola A. and Wolf, Levi J.},
	title = {A diverse community constitutes global coccolithophore calcium carbonate stocks},
	elocation-id = {2025.09.11.675535},
	year = {2025},
	doi = {10.1101/2025.09.11.675535},
	publisher = {Cold Spring Harbor Laboratory},
	URL = {https://www.biorxiv.org/content/early/2025/09/16/2025.09.11.675535},
	eprint = {https://www.biorxiv.org/content/early/2025/09/16/2025.09.11.675535.full.pdf},
	journal = {bioRxiv}
}