A table summarizing the Tukey's test [64] after the analysis of variance that evaluated the sources of effects on the performance of species distribution models.

<p>The three model types compared are logistic model (LM), boosted regression trees (BRT), and MaxEnt models. The descriptions of the rarity types A-H are provided in Table A in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0129995#pone.0129995.s003" target="_blank">S3 File</a>.</p><p>A table summarizing the Tukey's test [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0129995#pone.0129995.ref006" target="_blank">6</a><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0129995#pone.0129995.ref004" target="_blank">4</a>] after the analysis of variance that evaluated the sources of effects on the performance of species distribution models.</p

The sources and descriptions of environmental variables used to develop species distribution models for the 76 native stream fish species in the United States.

<p>Data are from NHDplusV1 [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0129995#pone.0129995.ref050" target="_blank">50</a>] and NHDplusV2 [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0129995#pone.0129995.ref051" target="_blank">51</a>], NFHAP [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0129995#pone.0129995.ref053" target="_blank">53</a>], USGS-LCI [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0129995#pone.0129995.ref049" target="_blank">49</a>], and PRISM [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0129995#pone.0129995.ref033" target="_blank">33</a>]. The environmental variables, if not specified, were measured per inter-confluence river segment.</p><p>^a This index is calculated based on 15 disturbance variables [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0129995#pone.0129995.ref006" target="_blank">6</a>]. The influence of each distribution variable was weighted by the results of multiple linear regression of all variables against a commonly used biological indicator of habitat condition (i.e., percent intolerant fishes at a site).</p><p>The sources and descriptions of environmental variables used to develop species distribution models for the 76 native stream fish species in the United States.</p

Using Historical Atlas Data to Develop High-Resolution Distribution Models of Freshwater Fishes

<div><p>Understanding the spatial pattern of species distributions is fundamental in biogeography, and conservation and resource management applications. Most species distribution models (SDMs) require or prefer species presence and absence data for adequate estimation of model parameters. However, observations with unreliable or unreported species absences dominate and limit the implementation of SDMs. Presence-only models generally yield less accurate predictions of species distribution, and make it difficult to incorporate spatial autocorrelation. The availability of large amounts of historical presence records for freshwater fishes of the United States provides an opportunity for deriving reliable absences from data reported as presence-only, when sampling was predominantly community-based. In this study, we used boosted regression trees (BRT), logistic regression, and MaxEnt models to assess the performance of a historical metacommunity database with inferred absences, for modeling fish distributions, investigating the effect of model choice and data properties thereby. With models of the distribution of 76 native, non-game fish species of varied traits and rarity attributes in four river basins across the United States, we show that model accuracy depends on data quality (e.g., sample size, location precision), species’ rarity, statistical modeling technique, and consideration of spatial autocorrelation. The cross-validation area under the receiver-operating-characteristic curve (AUC) tended to be high in the spatial presence-absence models at the highest level of resolution for species with large geographic ranges and small local populations. Prevalence affected training but not validation AUC. The key habitat predictors identified and the fish-habitat relationships evaluated through partial dependence plots corroborated most previous studies. The community-based SDM framework broadens our capability to model species distributions by innovatively removing the constraint of lack of species absence data, thus providing a robust prediction of distribution for stream fishes in other regions where historical data exist, and for other taxa (e.g., benthic macroinvertebrates, birds) usually observed by community-based sampling designs.</p></div

The effect of prevalence (i.e., the proportion of presences among all the observations) on the performance of species distribution models.

<p>The total sample size (N) for the two rare species (R), Candy darter (<i>Etheostoma osburni</i>) and Spotfin shiner (<i>Cyprinella spiloptera</i>), was set at 100; while N was decreased from 300 to 100 for the two common species (C), Bigmouth chub (<i>Nocomis platyrhynchus</i>) and Northern hog sucker (<i>Hypentelium nigricans</i>), to evaluate the effect of sample size.</p

Comparing the performance of Lasso logistic regression model and boosted regression tree (BRT) models in terms of the area under the receiver-operating-characteristic (ROC) curve in the 5-fold cross validation for 76 species in the four selected river basins (i.e., New River, Illinois River, Brazos River and Snake River).

<p>The results from the two set of models were generally in agreement, with Pearson’s <i>r</i> over 0.9. For fish species Mountain whitefish, <i>Prosopium williamsoni</i> and Torrent sculpin, <i>Cottus rhotheus</i> (marked as circles) in the Snake River where occurrence data was relatively sparse, the Lasso logistic models outperformed the BRT models.</p

Appendix B. Justification for inclusion and methods for summarizing landscape variables for use in predicting fish traits, summary of correlations among predictors and trait-frequencies, and an assessment of redundancy in predictors.

Justification for inclusion and methods for summarizing landscape variables for use in predicting fish traits, summary of correlations among predictors and trait-frequencies, and an assessment of redundancy in predictors

A map showing the distribution of four river basins (i.e., New River, Illinois River, Brazos River, and Snake River) selected for this study in the contiguous United States.

<p>We can see that all these four rivers pass through multiple states. Fish presence data are sufficient in these four basins in the <i>IchthyMap</i> database for developing and validating species distribution models (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0129995#pone.0129995.s002" target="_blank">S2 File</a>). Specifically, the number of presence records of non-game species used to develop species distribution models was 2,716 for Brazos River Basin, 5,635 for Illinois River Basin, 5,192 for New River Basin and, 412 for the Snake river Basin.</p

Examples of using partial dependence curves to capture ecological thresholds of spatial distribution of species.

<p>For example, the thresholds of mean slope (degree) in the watershed and number of stream-road crossings were identified for Rainbow darter (<i>Etheostoma caeruleum</i>) in the panel A and B. The thresholds of 20-year (1961–1980) average annual minimum temperature and mean annual flow velocity were identified for Mountain redbelly dace (<i>Chrosomus oreas</i>) in the panel C and D.</p

A summary on the Analysis of covariance, ANCOVA [58].

<p>ANCOVA was used to evaluate the effect of model types, incorporation of spatial autocorrelation, species’ rarity type, and data resolution on the performance of species distribution models in terms of the area under the receiver operating characteristic (ROC) curve (AUC). Degree of freedom (D.F.), mean square (M.S.), F statistic and <i>p</i>-value are listed in this table.</p><p>A summary on the Analysis of covariance, ANCOVA [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0129995#pone.0129995.ref058" target="_blank">58</a>].</p

Appendix C. A comparison of variable importance in random forest tree-models.

A comparison of variable importance in random forest tree-models