@ARTICLE{Drake2014-qz,
  title     = "Ensemble algorithms for ecological niche modeling from
               presence‐background and presence‐only data",
  author    = "Drake, John M",
  journal   = "Ecosphere",
  publisher = "Wiley",
  volume    =  5,
  number    =  6,
  pages     = "1--16",
  abstract  = "Ecological niche modeling is central to many problems in
               population dynamics, conservation biology, biogeography, and
               evolutionary ecology. Recently, methodological advances have been
               achieved by adopting modeling principles from statistical and
               machine learning. One such principle that has not been explored
               is that voted ensembles of weakly tuned (underfit) models often
               exhibit similar bias and lower variance than individual
               well‐tuned models. A further contribution of machine learning is
               the one class support vector machine. One class support vector
               machines may be more suited to ecological niche modeling than
               classification algorithms because the “boundary estimation”
               property of this class of models better represents the conception
               of the niche as the set of environments in which populations of a
               species persist. This paper brings together these approaches and
               applies them to a data set on the distribution two‐spined
               blackfish (Gadopsis bispinosus) in southeastern Australia with
               the aim of improving performance and enriching the repertoire of
               methods for ecological niche modeling. First, a study of low‐bias
               bagging (LOBAG) applied to conventional two‐class support vector
               machines, an approach to presence‐background modeling, found this
               method to be comparable to another widely used method, MAXENT.
               Next, I introduce a variant of LOBAG for one class
               classification, LOBAG‐OC, to be used for ecological niche
               modeling with presence‐only data. This method performed better
               than other presence‐only methods (DOMAIN and BIOCLIM) and nearly
               as well as LOBAG and MAXENT, despite the fact that the
               presence‐only problem is considerably more difficult than the
               corresponding presence‐background problem. Results of these
               analyses suggest that LOBAG and LOBAG‐OC may provide robust,
               high‐performing solutions to the presence‐background and
               presence‐only modeling problems and point to ways in which
               ecological niche modeling may be simultaneously both improved and
               automated.",
  month     =  jun,
  year      =  2014,
  url       = "https://esajournals.onlinelibrary.wiley.com/doi/abs/10.1890/ES13-00202.1",
  doi       = "10.1890/es13-00202.1",
  language  = "en"
}

@ARTICLE{Drake2015-sb,
  title    = "Range bagging: a new method for ecological niche modelling from
              presence-only data",
  author   = "Drake, John M",
  journal  = "J. R. Soc. Interface",
  volume   =  12,
  number   =  107,
  abstract = "The ecological niche is the set of environments in which a
              population of a species can persist without introduction of
              individuals from other locations. A good mathematical or
              computational representation of the niche is a prerequisite to
              addressing many questions in ecology, biogeography, evolutionary
              biology and conservation. A particularly challenging question for
              ecological niche modelling is the problem of presence-only
              modelling. That is, can an ecological niche be identified from
              records drawn only from the set of niche environments without
              records from non-niche environments for comparison? Here, I
              introduce a new method for ecological niche modelling from
              presence-only data called range bagging. Range bagging draws on
              the concept of a species' environmental range, but was inspired by
              the empirical performance of ensemble learning algorithms in other
              areas of ecological research. This paper extends the concept of
              environmental range to multiple dimensions and shows that range
              bagging is computationally feasible even when the number of
              environmental dimensions is large. The target of the range bagging
              base learner is an environmental tolerance of the species in a
              projection of its niche and is therefore an ecologically
              interpretable property of a species' biological requirements. The
              computational complexity of range bagging is linear in the number
              of examples, which compares favourably with the main alternative,
              Qhull. In conclusion, range bagging appears to be a reasonable
              choice for niche modelling in applications in which a
              presence-only method is desired and may provide a solution to
              problems in other disciplines where one-class classification is
              required, such as outlier detection and concept learning.",
  month    =  jun,
  year     =  2015,
  doi      = "10.1098/rsif.2015.0086",
  keywords = "Qhull; ecological niche model; niche; range; species distribution
              model; zero net growth isocline",
  language = "en"
}

@ARTICLE{Drake2018-ha,
  title     = "Estimating environmental suitability",
  author    = "Drake, John M and Richards, Robert L",
  journal   = "Ecosphere",
  publisher = "Wiley",
  volume    =  9,
  number    =  9,
  pages     = "e02373",
  abstract  = "Abstract Methods for modeling species? distributions in nature
               are typically evaluated empirically with respect to data from
               observations of species occurrence and, occasionally, absence at
               surveyed locations. Such models are relatively ?theory-free.? In
               contrast, theories for explaining species? distributions draw on
               concepts like fitness, niche, and environmental suitability. This
               paper proposes that environmental suitability be defined as the
               conditional probability of occurrence of a species given the
               state of the environment at a location. Any quantity that is
               proportional to this probability is a measure of relative
               suitability and the support of this probability is the niche.
               This formulation suggests new methods for presence-background
               modeling of species distributions that unify statistical
               methodology with the conceptual framework of niche theory. One
               method, the plug-and-play approach, is introduced for the first
               time. Variations on the plug-and-play approach were studied with
               respect to their numerical performance on 106 species from an
               exhaustively sampled presence?absence survey of vegetation in the
               Canton of Vaud, Switzerland. Additionally, we looked at the
               robustness of these methods to the presence of irrelevant
               information and sample size. Although irrelevant variables eroded
               the predictive performance of all methods, these methods were
               found to be both numerically and statistically robust.",
  month     =  sep,
  year      =  2018,
  doi       = "10.1002/ecs2.2373",
  language  = "en"
}

@ARTICLE{Kanamori2009-pz,
  title     = "A least-squares approach to direct importance estimation",
  author    = "Kanamori, Takafumi and Hido, Shohei and Sugiyama, Masashi",
  journal   = "J. Mach. Learn. Res.",
  publisher = "JMLR. org",
  volume    =  10,
  pages     = "1391--1445",
  year      =  2009,
  url       = "https://www.jmlr.org/papers/v10/kanamori09a.html"
}


@ARTICLE{Yamada2013-ra,
  title   = "Relative Density-Ratio Estimation for Robust Distribution Comparison",
  author  = "Yamada, Makoto and Suzuki, Taiji and Kanamori, Takafumi and Hachiya, Hirotaka and Sugiyama, Masashi",
  journal = "Neural Computation",
  volume  =  25,
  number  =  5,
  pages   = "1324--1370",
  year    =  2013,
  doi     = "10.1162/neco_a_00442"
}

@ARTICLE{Maitner2026-bc,
  title     = "Flexible methods for species distribution modeling with small samples",
  author    = "Maitner, Brian S. and Richards, Robert L. and Carlson, Ben S. and Drake, John M. and Merow, Cory",
  journal   = "Ecography",
  volume    =  2026,
  number    =  2,
  pages     = "e08112",
  keywords  = "species distribution models, niche models, MaxEnt, range-bagging, kernel density estimation, presence-only data",
  abstract  = "Species distribution models (SDMs) predict where species live or could potentially live and are a key resource for ecological research and conservation decision-making. However, current SDM methods often perform poorly for rare or inadequately sampled species, which include most species on earth, as well as most of those of the greatest conservation concern. Here, we evaluated the performance of three modeling approaches designed for data-deficient situations: plug-and-play modeling, density-ratio modeling, and environmental-range modeling. We compared the performance of algorithms within these approaches with the maximum entropy (MaxEnt) model, a widely used density-ratio algorithm, both for data-poor species and more generally. We also tested to what extent model cross-validation performance on training data predicts model performance on independent, presence–absence data. We found that no algorithm performed best in all situations. Across all species, MaxEnt performed best on average but was outperformed by one or more of the plug-and-play, density-ratio, or environmental-range algorithms in 72\% of cases. Six of the other algorithms had the area under the receiver operating characteristic curve (AUC) distributions not significantly different from MaxEnt's, and for data-poor species (those with 20 or fewer occurrences), 24 of the algorithms considered had AUC distributions not significantly different from MaxEnt's. However, we found that the algorithm outputs (when thresholded to predict presence vs absence) spanned a wide sensitivity–specificity gradient. Specificity and prediction accuracy assessed on training data were strongly correlated with specificity and prediction accuracy assessed on independent presence–absence data. However, AUC and sensitivity were weakly correlated in training versus testing sets, with only 22\% of species having the same model perform best when evaluated on training and independent, presence absence data. Finally, we show how ensembles of algorithms that span the sensitivity–specificity gradient can represent model disagreement in poorly sampled species and improve model predictions.",
  year      =  2026,
  doi       = "10.1002/ecog.08112"
}
