Comparative interpretation of count, presence–absence and point methods for species distribution models

By G. Aarts, J. Fieberg, Jason Matthiopoulos. 2012

271 citations recorded by the source corpus.

Subfields: Ecological Modeling, Ecology, Economics and Econometrics

View Paper: https://www.semanticscholar.org/paper/dbcb576b4a4395c702a78befcaa89e5c41582147

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Ecological Modeling

1. R. P. Anderson
2. Matt D. White
3. G. Newell
4. A. Townsend Peterson
5. Canran Liu
6. Rune Halvorsen
7. Jorge M. Lobo
8. Steven J. Phillips

In Ecology

1. Matt D. White
2. G. Newell
3. R. P. Anderson
4. Canran Liu
5. A. Townsend Peterson
6. Steven J. Phillips
7. Miroslav Dud'ik
8. Jorge M. Lobo

In Economics and Econometrics

1. Mevin B. Hooten
2. T. Hefley
3. Matt D. White
4. G. Newell
5. Canran Liu
6. Jorge M. Lobo
7. Miroslav Dud'ik
8. G. Aarts

Citations in the corpus

• On the selection of thresholds for predicting species occurrence with presence‐only data (2015, methodology): “Point process modeling has also been introduced to build models with presence-only data (Warton and Shepherd 2010; Chakraborty et al. 2011; Aarts et al. 2012).”
• Opening the black box: an open-source release of Maxent (2017, background): “More recently, it was noted that the exact same maximum likelihood exponential model can be obtained from an inhomogeneous Poisson process (IPP) (Aarts et al. 2012, Fithian and Hastie 2013, Renner and Warton 2013). This development is important for Maxent users, as it yields new interpretations of m”
• Hierarchical Species Distribution Models (2016, methodology): “1), but interpreted as a discrete approximation to the space-time Poisson point process [1].”
• Protected areas network is not adequate to protect a critically endangered East Africa Chelonian: Modelling distribution of pancake tortoise, Malacochersus tornieri under current and future climates (2020, methodology): “where yi is the number of observations in cell i (Cressie 1993, Aarts et al. 2012, Fithian and Hastie 2012). Equation (2) is the occurrence rate in the numerator of Eq. 1, but lacks the normalization term in the denominator. Fithian and Hastie (2012) argue that predicting ROR is the best one can oft”
• Practical guidance on characterizing availability in resource selection functions under a use-availability design. (2013, methodology): “Such assessments could follow the methods present- ed here, and those suggested elsewhere (e.g., Manly et al. 2002, Warton and Shepherd 2010, Aarts et al. 2012) where multiple samples of varying sizes are tested until coefficient estimates converge.”
• Can we derive macroecological patterns from primary Global Biodiversity Information Facility data (2015, background): “Recent studies demonstrate that use-availability designs in which presence observations are modelled against a set of points reflecting the general environmental conditions of the territory under consideration (background absences) reflect only the density of the incorporated observations (Aarts et ”
• Establishing the link between habitat selection and animal population dynamics (2015, methodology): “Furthermore, they are more general than (although conceptually related to) more recent, popular methods such as maximum entropy (Phillips et al. 2006, Elith et al. 2011, Aarts et al. 2012, Renner and Warton 2013).”