The increasing frequency of zoonotic disease events underscores a need to develop forecasting tools toward a more preemptive approach to outbreak investigation. We apply machine learning to data describing the traits and zoonotic pathogen diversity of the most speciose group of mammals, the rodents, which also comprise a disproportionate number of zoonotic disease reservoirs. Our models predict reservoir status in this group with over 90% accuracy, identifying species with high probabilities of harboring undiscovered zoonotic pathogens based on trait profiles that may serve as rules of thumb to distinguish reservoirs from nonreservoir species. Key predictors of zoonotic reservoirs include biogeographical properties, such as range size, as well as intrinsic host traits associated with lifetime reproductive output. Predicted hotspots of novel rodent reservoir diversity occur in the Middle East and Central Asia and the Midwestern United States.machine learning | disease forecasting | prediction | pace-of-life hypothesis | generalized boosted regression trees I nfectious agents transmitted from animals to humans account for most outbreaks of novel pathogens worldwide (1-3). With over 1 billion cases of human illness attributable to zoonotic disease each year, identifying wild reservoirs of zoonotic pathogens is a perennial public health priority (4). Until now, investigations of disease outbreaks have mostly been reactive, with surveillance efforts targeting a broad host range (5), but because human activities precipitating these events continue to accelerate (4, 6), a more proactive approach is necessary (7,8). Identifying which wildlife species are most likely to serve as reservoirs of future zoonotic diseases and in which regions new outbreaks are most likely to occur are necessary steps toward a preemptive approach to minimizing zoonotic disease risk in humans. To this end, trait profiles inferred from large datasets that distinguish reservoirs from nonreservoir species can play a major role in guiding the search for novel disease reservoirs in the wild. Identifying these distinguishing, intrinsic features of zoonotic reservoirs also has the potential to generate testable hypotheses that can explain why some host species are more permissive to zoonotic infections.To accomplish these goals, we applied generalized boosted regressions (9, 10), a type of machine learning that builds ensembles of classification/regression trees to identify variables that are most important for prediction-in our case, predicting zoonotic reservoir status and hyperreservoir status (species known to carry two or more zoonotic infections). These methods and similar methods have particular use for comparative ecological studies because they accommodate multiple data types as covariates, nonrandom patterns of data missingness, and hidden, nonlinear interactions. The explanatory power of decision tree methods is unaffected by variations in data coverage that may arise because of sampling bias or when species share a particular trait because...

Abstract. Illuminating the ecological and evolutionary dynamics of parasites is one of the most pressing issues facing modern science, and is critical for basic science, the global economy, and human health. Extremely important to this effort are data on the diseasecausing organisms of wild animal hosts (including viruses, bacteria, protozoa, helminths, arthropods, and fungi). Here we present an updated version of the Global Mammal Parasite Database, a database of the parasites of wild ungulates (artiodactyls and perissodactyls), carnivores, and primates, and make it available for download as complete flat files. The updated database has more than 24,000 entries in the main data file alone, representing data from over 2700 literature sources. We include data on sampling method and sample sizes when reported, as well as both "reported" and "corrected" (i.e., standardized) binomials for each host and parasite species. Also included are current higher taxonomies and data on transmission modes used by the majority of species of parasites in the database. In the associated metadata we describe the methods used to identify sources and extract data from the primary literature, how entries were checked for errors, methods used to georeference entries, and how host and parasite taxonomies were standardized across the database. We also provide definitions of the data fields in each of the four files that users can download.

Identifying drivers of infectious disease patterns and impacts at the broadest scales of organisation is one of the most crucial challenges for modern science, yet answers to many fundamental questions remain elusive. These include what factors commonly facilitate transmission of pathogens to novel host species, what drives variation in immune investment among host species, and more generally what drives global patterns of parasite diversity and distribution? Here we consider how the perspectives and tools of macroecology, a field that investigates patterns and processes at broad spatial, temporal and taxonomic scales, are expanding scientific understanding of global infectious disease ecology. In particular, emerging approaches are providing new insights about scaling properties across all living taxa, and new strategies for mapping pathogen biodiversity and infection risk. Ultimately, macroecology is establishing a framework to more accurately predict global patterns of infectious disease distribution and emergence.

Phosphorus (P) loss from agricultural watersheds is generally greater in storm rather than base flow. Although fundamental to P-based risk assessment tools, few studies have quantified the effect of storm size on P loss. Thus, the loss of P as a function of flow type (base and storm flow) and size was quantified for a mixed-land use watershed (FD-36; 39.5 ha) from 1997 to 2006. Storm size was ranked by return period (<1, 1-3, 3-5, 5-10, and >10 yr), where increasing return period represents storms with greater peak and total flow. From 1997 to 2006, storm flow accounted for 32% of watershed discharge yet contributed 65% of dissolved reactive P (DP) (107 g ha(-1) yr(-1)) and 80% of total P (TP) exported (515 g ha(-1) yr(-1)). Of 248 storm flows during this period, 93% had a return period of <1 yr, contributing most of the 10-yr flow (6507 m(3) ha(-1); 63%) and export of DP (574 g ha(-1); 54%) and TP (2423 g ha(-1); 47%). Two 10-yr storms contributed 23% of P exported between 1997 and 2006. A significant increase in storm flow DP concentration with storm size (0.09-0.16 mg L(-1)) suggests that P release from soil and/or area of the watershed producing runoff increase with storm size. Thus, implementation of P-based Best Management Practice needs to consider what level of risk management is acceptable.

Loss of soil nutrients in runoff accelerates eutrophication of surface waters. This study evaluated P and N in surface runoff in relation to rainfall intensity and hydrology for two soils along a single hillslope. Experiments were initiated on 1- by 2-m plots at foot-slope (6%) and mid-slope (30%) positions within an alfalfa (Medicago sativa L.)-orchardgrass (Dactylis glomerata L.) field. Rain simulations (2.9 and 7.0 cm h(-1)) were conducted under wet (spring) and dry (late-summer) conditions. Elevated, antecedent soil moisture at the foot-slope during the spring resulted in less rain required to generate runoff and greater runoff volumes, compared with runoff from the well-drained mid-slope in spring and at both landscape positions in late summer. Phosphorus in runoff was primarily in dissolved reactive form (DRP averaged 71% of total P), with DRP concentrations from the two soils corresponding with soil test P levels. Nitrogen in runoff was mainly nitrate (NO3-N averaged 77% of total N). Site hydrology, not chemistry, was primarily responsible for variations in mass N and P losses with landscape position. Larger runoff volumes from the foot-slope produced higher losses of total P (0.08 kg ha(-1)) and N (1.35 kg ha(-1)) than did runoff from the mid-slope (0.05 total P kg ha(-1); 0.48 kg N ha(-1)), particularly under wet, spring-time conditions. Nutrient losses were significantly greater under the high intensity rainfall due to larger runoff volumes. Results affirm the critical source area concept for both N and P: both nutrient availability and hydrology in combination control nutrient loss.