Projecting future expansion of invasive species: comparing and improving methodologies for species distribution modeling.

Modeling the distributions of species, especially of invasive species in non-native ranges, involves multiple challenges. Here, we developed some novel approaches to species distribution modeling aimed at reducing the influences of such challenges and improving the realism of projections. We estimated species-environment relationships for Parthenium hysterophorus L. (Asteraceae) with four modeling methods run with multiple scenarios of (i) sources of occurrences and geographically isolated background ranges for absences, (ii) approaches to drawing background (absence) points, and (iii) alternate sets of predictor variables. We further tested various quantitative metrics of model evaluation against biological insight. Model projections were very sensitive to the choice of training dataset. Model accuracy was much improved using a global dataset for model training, rather than restricting data input to the species' native range. AUC score was a poor metric for model evaluation and, if used alone, was not a useful criterion for assessing model performance. Projections away from the sampled space (i.e., into areas of potential future invasion) were very different depending on the modeling methods used, raising questions about the reliability of ensemble projections. Generalized linear models gave very unrealistic projections far away from the training region. Models that efficiently fit the dominant pattern, but exclude highly local patterns in the dataset and capture interactions as they appear in data (e.g., boosted regression trees), improved generalization of the models. Biological knowledge of the species and its distribution was important in refining choices about the best set of projections. A post hoc test conducted on a new Parthenium dataset from Nepal validated excellent predictive performance of our 'best' model. We showed that vast stretches of currently uninvaded geographic areas on multiple continents harbor highly suitable habitats for parthenium. However, discrepancies between model predictions and parthenium invasion in Australia indicate successful management for this globally significant weed.

A brighter shade of future climate on Himalayan musk deer Moschus leucogaster.

Himalayan musk deer (Moschus leucogaster) is classified as an endangered species by IUCN with a historically misunderstood distribution due to misidentification with other species of musk deer, Moschus spp. Taking advantage of recent genetic analyses confirming the species of various populations in Nepal and China, we produced an accurate estimate of the species' current and future distribution under multiple climate change scenarios. We collected high-quality occurrence data using systematic surveys of various protected areas of Nepal to train species distribution models. The most influential determinants of the distribution of Himalayan musk deer were precipitation of the driest quarter, temperature seasonality, and annual mean temperature. These variables, and precipitation in particular, determine the vegetation type and structure in the Himalaya, which is strongly correlated with the distribution of Himalayan musk deer. We predicted suitable habitats between the Annapurna and Kanchenjunga region of Nepal Himalaya as well as the adjacent Himalaya in China. Under multiple climate change scenarios, the vast majority (85-89%) of current suitable sites are likely to remain suitable and many new areas of suitable habitat may emerge to the west and north of the current species range in Nepal and China. Two-thirds of current and one-third of future suitable habitats are protected by the extensive network of protected areas in Nepal. The projected large gains in suitable sites may lead to population expansion and conservation gains, only when the threat of overexploitation and population decline is under control.

A better index for analysis of co-occurrence and similarity.

Scientists often need to know whether pairs of entities tend to occur together or independently. Standard approaches to this issue use co-occurrence indices such as Jaccard, Sørensen-Dice, and Simpson. We show that these indices are sensitive to the prevalences of the entities they describe and that this invalidates their interpretability. We propose an index, α, that is insensitive to prevalences. Published datasets reanalyzed with both α and Jaccard's index (J) yield profoundly different biological inferences. For example, a published analysis using J contradicted predictions of the island biogeography theory finding that community stability increased with increasing physical isolation. Reanalysis of the same dataset with the estimator [Formula: see text] reversed that result and supported theoretical predictions. We found similarly marked effects in reanalyses of antibiotic cross-resistance and human disease biomarkers. Our index α is not merely an improvement; its use changes data interpretation in fundamental ways.

Greening and browning of the Himalaya: Spatial patterns and the role of climatic change and human drivers.

The reliable detection and attribution of changes in vegetation greenness is a prerequisite for the development of strategies for the sustainable management of ecosystems. We conducted a robust trend analysis on remote sensing derived vegetation index time-series matrices to detect significant changes in inter-annual vegetation productivity (greening versus browning) for the entire Himalaya, a biodiverse and ecologically sensitive yet understudied region. The spatial variability in trend was assessed considering elevation, 12 dominant land cover/use types and 10 ecoregions. To assess trend causation, at local scale, we compared multi-temporal imagery, and at regional scale, referenced ecological theories of mountain vegetation dynamics and ancillary literature. Overall, 17.56% of Himalayan vegetation (71,162km2) exhibited significant trend (p<0.01) and majority (94%) showed positive trend (greening). Trend distribution showed strong elevational and ecoregion dependence as greening was most dominant at lower and middle elevations whereas majority of the browning occurred at higher elevation (>3800m), with eastern high Himalaya browning more dominantly than western high Himalaya. Land cover/use based categorization confirmed dominant greening of rainfed and irrigated agricultural areas, though cropped areas in western Himalaya contained higher proportion of greening areas. While rising atmospheric CO2 concentration and nitrogen deposition are the most likely climatic causes of detected greening, success of sustainable forestry practices (community forestry in Nepal) along with increasing agricultural fertilization and irrigation facilities could be possible human drivers. Comparison of multi-temporal imagery enabled direct attribution of some browning areas to anthropogenic land change (dam, airport and tunnel construction). Our satellite detected browning of high altitude vegetation in eastern Himalaya confirm the findings of recent dendrochronology based studies which possibly resulted from reduced pre-monsoon moisture availability in recent decades. These results have significant implications for environmental management in the context of climate change and ecosystem dynamics in the Himalaya.

Statistical analysis of co-occurrence patterns in microbial presence-absence datasets.

Drawing on a long history in macroecology, correlation analysis of microbiome datasets is becoming a common practice for identifying relationships or shared ecological niches among bacterial taxa. However, many of the statistical issues that plague such analyses in macroscale communities remain unresolved for microbial communities. Here, we discuss problems in the analysis of microbial species correlations based on presence-absence data. We focus on presence-absence data because this information is more readily obtainable from sequencing studies, especially for whole-genome sequencing, where abundance estimation is still in its infancy. First, we show how Pearson's correlation coefficient (r) and Jaccard's index (J)-two of the most common metrics for correlation analysis of presence-absence data-can contradict each other when applied to a typical microbiome dataset. In our dataset, for example, 14% of species-pairs predicted to be significantly correlated by r were not predicted to be significantly correlated using J, while 37.4% of species-pairs predicted to be significantly correlated by J were not predicted to be significantly correlated using r. Mismatch was particularly common among species-pairs with at least one rare species (<10% prevalence), explaining why r and J might differ more strongly in microbiome datasets, where there are large numbers of rare taxa. Indeed 74% of all species-pairs in our study had at least one rare species. Next, we show how Pearson's correlation coefficient can result in artificial inflation of positive taxon relationships and how this is a particular problem for microbiome studies. We then illustrate how Jaccard's index of similarity (J) can yield improvements over Pearson's correlation coefficient. However, the standard null model for Jaccard's index is flawed, and thus introduces its own set of spurious conclusions. We thus identify a better null model based on a hypergeometric distribution, which appropriately corrects for species prevalence. This model is available from recent statistics literature, and can be used for evaluating the significance of any value of an empirically observed Jaccard's index. The resulting simple, yet effective method for handling correlation analysis of microbial presence-absence datasets provides a robust means of testing and finding relationships and/or shared environmental responses among microbial taxa.

Impact of a short-term heat event on C and N relations in shoots vs. roots of the stress-tolerant C4 grass, Andropogon gerardii.

Global warming will increase heat waves, but effects of abrupt heat stress on shoot-root interactions have rarely been studied in heat-tolerant species, and abrupt heat-stress effects on root N uptake and shoot C flux to roots and soil remains uncertain. We investigated effects of a high-temperature event on shoot vs. root growth and function, including transfer of shoot C to roots and soil and uptake and translocation of soil N by roots in the warm-season drought-tolerant C4 prairie grass, Andropogon gerardii. We heated plants in the lab and field (lab=5.5days at daytime of 30+5 or 10°C; field=5days at ambient (up to 32°C daytime) vs. ambient +10°C). Heating had small or no effects on photosynthesis, stomatal conductance, leaf water potential, and shoot mass, but increased root mass and decreased root respiration and exudation per g. (13)C-labeling indicated that heating increased transfer of recently-fixed C from shoot to roots and soil (the latter likely via increased fine-root turnover). Heating decreased efficiency of N uptake by roots (uptake/g root), but did not affect total N uptake or the transfer of labeled soil (15)N to shoots. Though heating increased soil temperature in the lab, it did not do so in the field (10cm depth); yet results were similar for lab and field. Hence, acute heating affected roots more than shoots in this stress-tolerant species, increasing root mass and C loss to soil, but decreasing function per g root, and some of these effects were likely independent of direct effects from soil heating.