Prediction of species' distributions is central to diverse applications in ecology, evolution and conservation science. There is increasing electronic access to vast sets of occurrence records in museums and herbaria, yet little effective guidance on how best to use this information in the context of numerous approaches for modelling distributions. To meet this need, we compared 16 modelling methods over 226 species from 6 regions of the world, creating the most comprehensive set of model comparisons to date. We used presence-only data to fit models, and independent presence-absence data to evaluate the predictions. Along with well-established modelling methods such as generalised additive models and GARP and BIOCLIM, we explored methods that either have been developed recently or have rarely been applied to modelling species' distributions. These include machine-learning methods and community models, both of which have features that may make them particularly well suited to noisy or sparse information, as is typical of species' occurrence data. Presence-only data were effective for modelling species' distributions for many species and regions. The novel methods consistently outperformed more established methods. The results of our analysis are promising for the use of data from museums and herbaria, especially as methods suited to the noise inherent in such data improve.

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Novel methods improve prediction of species' distributions from occurrence data

In the last two decades, interest in species distribution models (SDMs) of plants and animals has grown dramatically. Recent advances in SDMs allow us to potentially forecast anthropogenic effects on patterns of biodiversity at different spatial scales. However, some limitations still preclude the use of SDMs in many theoretical and practical applications. Here, we provide an overview of recent advances in this field, discuss the ecological principles and assumptions underpinning SDMs, and highlight critical limitations and decisions inherent in the construction and evaluation of SDMs. Particular emphasis is given to the use of SDMs for the assessment of climate change impacts and conservation management issues. We suggest new avenues for incorporating species migration, population dynamics, biotic interactions and community ecology into SDMs at multiple spatial scales. Addressing all these issues requires a better integration of SDMs with ecological theory.

Predicting species distribution: offering more than simple habitat models.

Aim: Techniques that predict species potential distributions by combining observed occurrence records with environmental variables show much potential for application across a range of biogeographical analyses. Some of the most promising applications relate to species for which occurrence records are scarce, due to cryptic habits, locally restricted distributions or low sampling effort. However, the minimum sample sizes required to yield useful predictions remain difficult to determine. Here we developed and tested a novel jackknife validation approach to assess the ability to predict species occurrence when fewer than 25 occurrence records are available. Location: Madagascar. Methods: Models were developed and evaluated for 13 species of secretive leaf-tailed geckos (Uroplatus spp.) that are endemic to Madagascar, for which available sample sizes range from 4 to 23 occurrence localities (at 1 km2 grid resolution). Predictions were based on 20 environmental data layers and were generated using two modelling approaches: a method based on the principle of maximum entropy (Maxent) and a genetic algorithm (GARP). Results: We found high success rates and statistical significance in jackknife tests with sample sizes as low as five when the Maxent model was applied. Results for GARP at very low sample sizes (less than c. 10) were less good. When sample sizes were experimentally reduced for those species with the most records, variability among predictions using different combinations of localities demonstrated that models were greatly influenced by exactly which observations were included. Main conclusions: We emphasize that models developed using this approach with small sample sizes should be interpreted as identifying regions that have similar environmental conditions to where the species is known to occur, and not as predicting actual limits to the range of a species. The jackknife validation approach proposed here enables assessment of the predictive ability of models built using very small sample sizes, although use of this test with larger sample sizes may lead to overoptimistic estimates of predictive power. Our analyses demonstrate that geographical predictions developed from small numbers of occurrence records may be of great value, for example in targeting field surveys to accelerate the discovery of unknown populations and species. © 2007 The Authors.

Predicting species distributions from small numbers of occurrence records: A test case using cryptic geckos in Madagascar

Most methods for modeling species distributions from occurrence records require additional data representing the range of environmental conditions in the modeled region. These data, called background or pseudo-absence data, are usually drawn at random from the entire region, whereas occurrence collection is often spatially biased toward easily accessed areas. Since the spatial bias generally results in environmental bias, the difference between occurrence collection and background sampling may lead to inaccurate models. To correct the estimation, we propose choosing background data with the same bias as occurrence data. We investigate theoretical and practical implications of this approach. Accurate information about spatial bias is usually lacking, so explicit biased sampling of background sites may not be possible. However, it is likely that an entire target group of species observed by similar methods will share similar bias. We therefore explore the use of all occurrences within a target group as biased background data. We compare model performance using target-group background and randomly sampled background on a comprehensive collection of data for 226 species from diverse regions of the world. We find that target-group background improves average performance for all the modeling methods we consider, with the choice of background data having as large an effect on predictive performance as the choice of modeling method. The performance improvement due to target-group background is greatest when there is strong bias in the target-group presence records. Our approach applies to regression-based modeling methods that have been adapted for use with occurrence data, such as generalized linear or additive models and boosted regression trees, and to Maxent, a probability density estimation method. We argue that increased awareness of the implications of spatial bias in surveys, and possible modeling remedies, will substantially improve predictions of species distributions.

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Sample selection bias and presence-only distribution models: implications for background and pseudo-absence data

http://web.clas.ufl.edu/users/mbinford/GEOXXXX_Biogeography/LiteratureForLinks/Austin_2002_spatial_prediction_species_distribution_ecology-statistics_EcolModel.pdf

Spatial prediction of species distribution: an interface between ecological theory and statistical modelling

https://www.whoi.edu/cms/files/Ecological_Modelling_2002_Boyce_53558.pdf

Evaluating resource selection functions

We investigated grizzly bear (Ursus arctos) selection of three road types in the northern United States and southern British Columbia from 1986 to 1991. We hypothesized that grizzly bears select against open (public use allowed), restricted (forestry use only), and closed roads (no public use allowed) in that order. We analyzed use of roads for 11 bears (five females and six males) in an area containing open and closed roads and 11 bears (seven females and four males) in an adjacent area containing restricted roads. We used χ 2 and log-linear models to test for selection of habitat type and distance to road categories. Ten of 12 females and 5 of 10 males (15 of 22 bears) selected against (P < 0.05) low-elevation interior cedar-hemlock and for (P < 0.05) high-elevation Englemann spruce (Picea engelmannii Parry ex Engelm.) - subalpine fir (Abies lasiocarpa (Hook.) Nutt.). After accounting for habitat, 4 of 5 females and 3 of 6 males (7 of 11 bears) selected against open roads and 3 of 5 females and 0 of 6 males (3 of 11 bears) selected against closed roads. No females (n =7 ) or males (n = 4) (0 of 11 bears) selected against restricted roads. Our results are inconsistent with the hypothesis that bears select against open, restricted, and closed roads in that order. Most females and males selected against open roads, most females selected against closed roads, and no bears selected against restricted roads. The type of human activity along roads plays a role in bear responses to roads, and this aspect should be incorporated into future bear-road studies.

Grizzly bear use of open, closed, and restricted forestry roads

Generalizability of songbird habitat models in boreal mixedwood forests of Alberta

We tested the commonly held hypotheses that grizzly bears (Ursus arctos) select against clearcuts and young forests and select for natural openings and old forests in the Selkirk Mountains from 198...

Grizzly bear selection of managed and unmanaged forests in the Selkirk Mountains

Objectives:

The objective of this study is to test for an association between the sex of conceptuses of the mother's preceding pregnancies and fetal development and early neonatal survival in normal pregnancy.



Methods:

A population of 27,243 neonates, including a subsample of 7,773 “newborn/mother/placenta units” were divided into cohorts according to the sex of the neonate and the sex and number of conceptuses of the mother's preceding pregnancies. The average birth weight, placenta weight and early neonatal mortality rate were measured for each cohort and compared. The “dose effect” of preceding pregnancy was tested by linear and quadratic regression analysis, and by chi-square trend test for linearity of proportions.



Results:

The results have shown an association between these three variables and the preceding pregnancies of the mother. Fetal development and early survival of the neonate are positively associated with the mother's preceding pregnancies of same sex as the neonate, and negatively associated with the preceding pregnancies of opposite sex to the neonate. The strength of the phenomenon increases with parity, at least for the first three parities. The association is statistically significant.



Conclusions:

The association between fetal development and neonatal survival and preceding pregnancies of the mother would be compatible with the action of male and female specific antigens capable of affecting selective implantation of blastocysts, which commands subsequent fetal development as well as early neonatal survival. Am. J. Hum. Biol. 22:708-715, 2010. © 2010Wiley-Liss, Inc.

Influence of the mother's preceding pregnancies on fetal development and postnatal survival of the neonate, in normal pregnancy. An immunological phenomenon?

Pierre R. Vernier

Papers

Evaluating resource selection functions

Grizzly bear use of open, closed, and restricted forestry roads

Generalizability of songbird habitat models in boreal mixedwood forests of Alberta

Grizzly bear selection of managed and unmanaged forests in the Selkirk Mountains

Influence of the mother's preceding pregnancies on fetal development and postnatal survival of the neonate, in normal pregnancy. An immunological phenomenon?