Large-scale Distribution Models Research Articles

Large-scale distribution models for optimal prediction of Eastern black rail habitat within tidal ecosystems

Eastern black rails (Laterallus jamaicensis jamaicensis) are among the rarest and least-studied birds in North America and were recently listed as threatened under the U.S. Endangered Species Act. Spatial models that predict habitat quality across the subspecies range are therefore needed to inform conservation, recovery, and monitoring efforts for this rare bird. We used data from 47,585 call-broadcast surveys collected at 7906 sites over a 3-decade period (1990s, 2000s, 2010s; 23 total years) to build species distribution models for eastern black rails. We used hierarchical Bayesian occupancy models and predictive model selection to develop multi-scale models that optimally predict habitat suitability for eastern black rails within tidal wetlands while also accounting for imperfect detection of these cryptic birds during field surveys. We also used raster regression techniques to translate model predictions into 30-m resolution maps of habitat suitability for eastern black rails within tidal wetlands along the eastern seaboard of the United States. The model predicted suitability of breeding habitat as a function of wetland attributes (e.g., cover of high marsh and terrestrial border), hydrologic modification, and disturbance from human development measured over multiple spatial scales. We also found differences in habitat relationships for eastern black rails when compared to models that included both North American subspecies of black rail. Important results included negative effects of shrub-scrub wetlands, and strong positive effects of high marsh, terrestrial border, and impoundments on breeding season occupancy. Our study provides an example of integrating detection-non-detection data and modern statistical methods to build predictive distribution models for an extremely rare species, while also providing rigorous predictions of breeding habitat quality for the eastern black rail within tidal wetlands. These models will facilitate optimal monitoring, habitat conservation, and recovery planning efforts for eastern black rails and provide a foundation for future research and conservation of this imperiled bird.

Connecting regional-scale tree distribution models with seed dispersal kernels

Regional scale forest distribution models are important tools for biogeography and understanding the structure of forest communities in space. These models take climate and geographic variables as input and are therefore helpful for long-term decision support and climate adaptation planning. Generally, local processes of tree germination and seedling survival are resolved probabilistically with explanatory variables such as elevation, latitude, exposure, soil type, moisture availability, climate and weather inputs and `trained’ using landscape and regional presence-absence data and machine learning techniques. How seeds are distributed in these models, that is, determining the dispersal kernel, is far more problematic. The challenge is that variables conditioning vertebrate seed dispersal (motility and probability of utilization or caching in response to cover type) are not represented in large scale distribution models, and in fact vary on scales (10-100 meters) that are much smaller than the smallest pixel size for the distribution model (1-10 kilometers). We present a homogenized seed digestion kernel (HSDK) which incorporates this scale separation. Homogenization naturally links highly variable small-scale processes (like seed foraging and caching by birds and rodents) with large scale effects (like dispersal of seeds over tens of kilometers). We develop a homogenization strategy to predict seed dispersal on landscape scales, analytically linking small-scale variables (landscape fraction cover by tree type, gut residence times and cover type utilization by frugivorous birds) with large scale behaviors. Closed form approximations are developed in two dimensions for two limiting cases of seed handling behavior, and the approach is illustrated using landscape data and piñon-pine dispersal in a 630,000 square kilometer region in the southwestern US.

Potential distribution of coyotes (Canis latrans), Virginia opossums (Didelphis virginiana), striped skunks (Mephitis mephitis), and raccoons (Procyon lotor) in the Chicago Metropolitan Area

Large-scale distribution models are effective predictors of habitat suitability and connectivity across broad landscapes and are useful management tools, though few large-scale species distribution models exist for medium-sized predators in urban landscapes. We modeled the potential distribution of 4 medium-sized predators in a 17,361-km2 portion of the Chicago Metropolitan Area. We applied a maximum entropy algorithm model (MaxEnt) using presence-only data collected via remote cameras from 54 Lake County, Illinois, forest preserves during August–October 2008–2012. Environmental data layers used to model distributions were distances to forest, grassland, barren land, crops, wetlands, developed open space, developed low intensity, developed high intensity, water, primary roads, secondary roads, and tertiary roads. Coyotes (Canis latrans) had the greatest area of potential distribution followed by opossums (Didelphis virginiana), striped skunks (Mephitis mephitis), and raccoons (Procyon lotor). Models for all species had high AUC values (0.90–0.94) indicating strong predictive performance. More than 50% of the study area was predicted to be within the distributional limit for each focal species. Distance to forest was the most important contributory predictor for all species modeled (82% - 96%) and higher probability of presence for all 4 species was indicated closer to forest and further from tertiary roads. However, coyotes and raccoons were predicted to prefer habitat closer to highly-developed areas. Our research indicates medium-sized predators are highly synanthropic and able to persist within the Chicago Metropolitan Area given adequate availability of non-urban land cover, particularly forest, and ample green space linking forest patches within highly-developed areas.

Investigation of temperature conditions in Swiss urban and suburban microhabitats for the overwintering suitability of diapausing Aedes albopictus eggs

BackgroundIn Switzerland, the invasive Asian tiger mosquito, Aedes albopictus, is firmly established in the Canton of Ticino, south of the Alps. According to a large-scale distribution model developed in 2013, suitable climatic conditions for the establishment of Ae. albopictus north of the Alps are found in Basel and Geneva while Zurich appears to be characterized by winters currently being too cold for survival of diapausing eggs. However, the spatial resolution of large-scale distribution models might not be sufficient to detect particular climatic conditions existing in urban settings, such as the presence of microclimatic temperatures, which may positively influence the probability of diapausing eggs to overwinter. In order to investigate this, microclimatic monitoring of potential diapausing sites (i.e. catch basins) and external controls was performed in January 2017 in Ticino and within the cities of Basel, Geneva and Zurich.ResultsMean January temperatures in catch basins of Basel, Geneva and Zurich were always higher than the -1 °C temperature threshold previously set for survival probability of diapausing eggs, while mean January temperatures were below -1 °C in several catch basins south of the Alps, where Ae. albopictus eggs currently overwinter. The catch basin absolute January daily minimum temperatures both south and north of the Alps were in general higher than the external control temperatures. Absolute January daily minimum temperatures in catch basins in Basel, Geneva and Zurich were always above -10 °C, indicating that diapausing Ae. albopictus eggs could potentially survive winter nights in urban areas north of the Alps.ConclusionsThe findings confirmed previous conclusions that urban catch basins can provide favourable conditions for overwintering of diapausing eggs compared to more cold-exposed sites. The results confirmed the presence of suitable winter conditions for the establishment of Ae. albopictus in the cities of Basel and Geneva. In addition, the microclimate-scale analysis added new information compared to the previous large-scale prevision model by showing that also the city of Zurich could provide winter conditions suitable for the establishment of Ae. albopictus. This illustrates the importance of the resolution of climate data in using models to predict Ae. albopictus distribution.

A continental-scale hydrology and water quality model for Europe: Calibration and uncertainty of a high-resolution large-scale SWAT model

Summary A combination of driving forces are increasing pressure on local, national, and regional water supplies needed for irrigation, energy production, industrial uses, domestic purposes, and the environment. In many parts of Europe groundwater quantity, and in particular quality, have come under sever degradation and water levels have decreased resulting in negative environmental impacts. Rapid improvements in the economy of the eastern European block of countries and uncertainties with regard to freshwater availability create challenges for water managers. At the same time, climate change adds a new level of uncertainty with regard to freshwater supplies. In this research we build and calibrate an integrated hydrological model of Europe using the Soil and Water Assessment Tool (SWAT) program. Different components of water resources are simulated and crop yield and water quality are considered at the Hydrological Response Unit (HRU) level. The water resources are quantified at subbasin level with monthly time intervals. Leaching of nitrate into groundwater is also simulated at a finer spatial level (HRU). The use of large-scale, high-resolution water resources models enables consistent and comprehensive examination of integrated system behavior through physically-based, data-driven simulation. In this article we discuss issues with data availability, calibration of large-scale distributed models, and outline procedures for model calibration and uncertainty analysis. The calibrated model and results provide information support to the European Water Framework Directive and lay the basis for further assessment of the impact of climate change on water availability and quality. The approach and methods developed are general and can be applied to any large region around the world.

Resolving the Hemodynamic Inverse Problem

The "hemodynamic inverse problem" is the determination of arterial system properties from pressures and flows measured at the entrance of an arterial system. Conventionally, investigators fit reduced arterial system models to data, and the resulting model parameters represent putative arterial properties. However, no unique solution to the inverse problem exists-an infinite number of arterial system topologies result in the same input impedance (Zin) and, therefore, the same pressure and flow. Nevertheless, there are exceptions to this theoretical limitation; total peripheral resistance (Rtot), total arterial compliance (Ctot), and characteristic impedance (ZO) can be uniquely determined from input pressure and flow. Zin is determined completely by Ctot and Rtot at low frequencies, Zo at high frequencies, and arterial topology and reflection effects at intermediate frequencies. We present a novel method to determine the relative contribution of Zo, Ctot, Rtot and arterial topology/reflection to Zin without assuming a particular reduced model. This method is tested with a large-scale distributed model of the arterial system, and is applied to illustrative cases of measured pressure and flow. This work, thus, lays the theoretical foundation for determining the arterial properties responsible for increased pulse pressure with age and various arterial system pathologies.

Should data be partitioned spatially before building large-scale distribution models?

There is growing interest in building predictive models of species distributions over large geographic areas. As larger areas are modelled, however, it is highly likely that heterogeneity in the predictors variable increases and that areas are included where animals respond to habitats in different ways, for example, due to social status. These effects (spatial non-stationary) may weaken model performance. This paper explores whether data partitioning prior to analysis can improve the fit of models and provide ecological insight into distribution patterns. Data on three bird species were modelled for the whole of Spain at 1 km 2 resolution using logistic regression analysis. Data were partitioned into geographic quarters, concentric rings around the centroid of the distribution, and into random samples for comparison. In all cases, data partitioning produced better models as assessed by Receiver Operating Characteristic curve (AUC) statistics than analysis of the global data set. Inclusion of latitude and longitude improved the global models only when added as smoothed splines but produced different probabilities to the partitioned data. Geographic partitioning is a very crude local modelling approach and we suggest that some form of geographically-weighted regression could offer the best solution to large-scale modelling but is computationally intensive on Geographical Information Systems (GIS) data. It is concluded that simple partitioning by geographic quarters may detect spatial non-stationary and alert the modeller to possible problems; that partitioning into more novel arrangements may be used to test ecological hypotheses; and that data should not be partitioned spatially to build and test models if non-stationary is suspected.

Sensitivity of computed values of water balance and nitrate leaching to within soil class variability of transport parameters

Variability with respect to model input data is recognised as a potential source of uncertainty in model predictions. The aim of this study is to estimate the sensitivity of computed values of water balance terms, in particular drainage below the root zone of crops, and that of nitrate leaching, to variability of soil transport parameters within a soil class, and to quantify the domains of sensitivity as a function of soil type. The methodological framework is based on the concept of Areal Non-Point Source Watershed Environmental Response Simulation, coupled with a Latin Hypercube Sampler, to obtain a stochastic model. Two applications are considered. First, a case study is made of the experimental catchment of LaCote St André, predominantly a loam soil, where intensive experimentation has been carried out from 1991 to 1995. Second, a generalisation to different types of soil is carried out. It is shown that for this model within-class variability has no effect in long-term simulations for soils with saturated hydraulic conductivity K s higher than 100 mm/day. For these soils, the concept of representative elementary area is fully acceptable and convenient. Corresponding soil classes can each be described by a single set of parameters (the barycentre (centroı̈d) of the class) with a very small loss of information compared to a very important gain in terms of input data requirements and simulation time. This has important consequences for large-scale distributed models, since it reduces considerably the number of measurements necessary to describe the soil; in particular there may be no need, in this range, to account for spatial variability of textural parameters within a class. In contrast, within-class variability of transport parameters becomes an important source of uncertainty for soil classes below this threshold value of saturated hydraulic conductivity. An estimation of errors resulting from aggregation of transport parameters values to those corresponding to the centroı̈d of the soil class is given. These errors are obviously dependent on K s values and rainfall intensity.