Regional data refine local predictions: modeling the distribution of plant species abundance on a portion of the central plains

Species distribution models are frequently used to predict species occurrences in novel conditions, yet few studies have examined the consequences of extrapolating locally collected data to regional landscapes. Similarly, the process of using regional data to inform local prediction for species distribution models has not been adequately evaluated. Using boosted regression trees, we examined errors associated with extrapolating models developed with locally collected abundance data to regional-scale spatial extents and associated with using regional data for predictions at a local extent for a native and non-native plant species across the northeastern central plains of Colorado. Our objectives were to compare model results and accuracy between those developed locally and extrapolated regionally, those developed regionally and extrapolated locally, and to evaluate extending species distribution modeling from predicting the probability of presence to predicting abundance. We developed models to predict the spatial distribution of plant species abundance using topographic, remotely sensed, land cover and soil taxonomic predictor variables. We compared model predicted mean and range abundance values to observed values between local and regional. We also evaluated model prediction performance based on Pearson's correlation coefficient. We show that: (1) extrapolating local models to regional extents may restrict predictions, (2) regional data can help refine and improve local predictions, and (3) boosted regression trees can be useful to model and predict plant species abundance. Regional sampling designed in concert with large sampling frameworks such as the National Ecological Observatory Network may improve our ability to monitor changes in local species abundance.     

Assessing the impact of alternative land-use zoning policies on future ecosystem services

Land use conversions rank among the most significant drivers of change in ecosystem services worldwide, affecting human wellbeing and threatening the survival of other species. Hence, predicting the effects of land use decisions on ecosystem services has emerged as a crucial need in spatial planning, and in the associated Strategic Environmental Assessment (SEA) practice. The paper presents a case-study research aimed at empirically exploring how the implementation of different land-use zoning policies affect the future provision of a set of ecosystem services (water purification, soil conservation, habitat for species, carbon sequestration and timber production). The study area is located in The Araucanía, one of Chile's Administrative Regions. The first part of the methods consisted in the construction of land-use scenarios associated to different policies. Subsequently, the effects of the land-use scenarios on the provision of the selected ecosystem services were assessed in a spatially explicit way, by using modeling tools. Finally, a set of metrics was developed to compare scenarios, and trade-offs in the provision of different ecosystem services were made explicit through trade-off curves. The results indicate that, for this case study, spatial configuration of land uses is as an important factor as their size. This suggests that the analysis of land-use patterns deserves attention, and that this information should be included in scenario exercises aimed to support spatial planning. The paper concludes by discussing the potential contribution of the approach to support SEA of spatial plans.     

Patterns of Spatial Autocorrelation in Stream Water Chemistry

Geostatistical models are typically based on symmetric straight-line distance, which fails to represent the spatial configuration, connectivity, directionality, and relative position of sites in a stream network. Freshwater ecologists have explored spatial patterns in stream networks using hydrologic distance measures and new geostatistical methodologies have recently been developed that enable directional hydrologic distance measures to be considered. The purpose of this study was to quantify patterns of spatial correlation in stream water chemistry using three distance measures: straight-line distance, symmetric hydrologic distance, and weighted asymmetric hydrologic distance. We used a dataset collected in Maryland, USA to develop both general linear models and geostatistical models (based on the three distance measures) for acid neutralizing capacity, conductivity, pH, nitrate, sulfate, temperature, dissolved oxygen, and dissolved organic carbon. The spatial AICC methodology allowed us to fit the autocorrelation and covariate parameters simultaneously and to select the model with the most support in the data. We used the universal kriging algorithm to generate geostatistical model predictions. We found that spatial correlation exists in stream chemistry data at a relatively coarse scale and that geostatistical models consistently improved the accuracy of model predictions. More than one distance measure performed well for most chemical response variables, but straight-line distance appears to be the most suitable distance measure for regional geostatistical modeling. It may be necessary to develop new survey designs that more fully capture spatial correlation at a variety of scales to improve the use of weighted asymmetric hydrologic distance measures in regional geostatistical models.     

Spatial Scale of Autocorrelation of Assemblages of Benthic Invertebrates in Two Upland Rivers in South-Eastern Australia and Its Implications for Biomonitoring and Impact Assessment in Streams

Spatial autocorrelation in ecological systems is a critical issue for monitoring (and a general understanding of ecological dynamics) yet there are very few data available, especially for riverine systems. Here, we report here on assemblage-level autocorrelation in the benthic-invertebrate assemblages of riffles in two adjacent, relatively pristine rivers in south-eastern Victoria, Australia (40-km reaches of the Wellington [surveys in summers of 1996 and 1997] and Wonnangatta Rivers [survey in summer of 1996 only], with 16 sites in each river). We found that analyses were similar if the data were resolved to family or to species level. Spatial autocorrelation was assessed by using Mantel-tests for the data partitioned into different sets of spatial separations of survey sites (e.g. 0–6 km, 6–12 km, etc.). We found strong small-scale (≤6 km) autocorrelation in the Wellington River, which is consistent with known dispersal abilities of many aquatic invertebrates. Surprisingly, there were strong negative correlations at longer distance classes for the Wellington River in one of the two summers (20–40 km) and the Wonnangatta River (12–20 km). That two largely unimpacted, adjacent rivers should have such different autocorrelation patterns suggests that impact assessment cannot assume dependence or independence of sites a priori. We discuss the implications of these results for use of “reference” sites to assess impacts at nominally affected sites.     

The use of spatial empirical models to estimate soil erosion in arid ecosystems

The central objective of this project was to utilize geographical information systems and remote sensing to compare soil erosion models, including Modified Pacific South-west Inter Agency Committee (MPSIAC), Erosion Potential Method (EPM), and Revised Universal Soil Loss Equation (RUSLE), and to determine their applicability for arid regions such as Kuwait. The northern portion of Umm Nigga, containing both coastal and desert ecosystems, falls within the boundaries of the de-militarized zone (DMZ) adjacent to Iraq and has been fenced off to restrict public access since 1994. Results showed that the MPSIAC and EPM models were similar in spatial distribution of erosion, though the MPSIAC had a more realistic spatial distribution of erosion and presented finer level details. The RUSLE presented unrealistic results. We then predicted the amount of soil loss between coastal and desert areas and fenced and unfenced sites for each model. In the MPSIAC and EPM models, soil loss was different between fenced and unfenced sites at the desert areas, which was higher at the unfenced due to the low vegetation cover. The overall results implied that vegetation cover played an important role in reducing soil erosion and that fencing is much more important in the desert ecosystems to protect against human activities such as overgrazing. We conclude that the MPSIAC model is best for predicting soil erosion for arid regions such as Kuwait. We also recommend the integration of field-based experiments with lab-based spatial analysis and modeling in future research.     

Evaluation of modeling as a tool to determine the potential impacts related to drilling wastes in the Brazilian offshore

Numerical models that predict the fate of drilling discharges at sea constitute a valuable tool for both the oil industry and regulatory agencies. In order to provide reliable estimates, models must be validated through the comparison of predictions with field or laboratory observations. In this paper, we used the Offshore Operators Committee Model to simulate the discharges from two wells drilled at Campos Basin, offshore SE Brazil, and compared the results with field observations obtained 3 months after drilling. The comparison showed that the model provided reasonable predictions, considering that data about currents were reconstructed and theoretical data were used to characterize the classes of solids. The model proved to be a valuable tool to determine the degree of potential impact associated to drilling activities. However, since the accuracy of the model is directly dependent on the quality of input data, different possible scenarios should be considered when used for forecast modeling.     

Integrating classification and regression tree (CART) with GIS for assessment of heavy metals pollution

The classification and regression tree (CART) model integrated with geographical information systems and the assessment of heavy-metals pollution system was developed to assess the heavy metals pollution in Fuyang, Zhejiang, China. The integration of the decision tree model with ArcGIS Engine 9 using a COM implementation in Microsoft® Visual Basic 6.0 provided an approach for assessing the spatial distribution of soil Zn content with high predictive accuracy. The Zn concentration classes estimated by CART assigned the right classes with an accuracy of near 90%. This is a great improvement compared to the ordinary Kriging method for the spatial autocorrelation of the study area severely destroyed by human activities. Also, it can be used to investigate the inter-relationships between the heavy metals pollution and environmental and anthropogenic variables. Moreover, the research presents model predictions over space for further applications and investigations.     

Predicting coral bleaching in response to environmental stressors using 8 years of global-scale data

Coral reefs have experienced extensive mortality over the past few decades as a result of temperature-induced mass bleaching events. There is an increasing realization that other environmental factors, including water mixing, solar radiation, water depth, and water clarity, interact with temperature to either exacerbate bleaching or protect coral from mass bleaching. The relative contribution of these factors to variability in mass bleaching at a global scale has not been quantified, but can provide insights when making large-scale predictions of mass bleaching events. Using data from 708 bleaching surveys across the globe, a framework was developed to predict the probability of moderate or severe bleaching as a function of key environmental variables derived from global-scale remote-sensing data. The ability of models to explain spatial and temporal variability in mass bleaching events was quantified. Results indicated approximately 20% improved accuracy of predictions of bleaching when solar radiation and water mixing, in addition to elevated temperature, were incorporated into models, but predictive accuracy was variable among regions. Results provide insights into the effects of environmental parameters on bleaching at a global scale.     

Opportunities for Improved Risk Assessments of Exotic Species in Canada Using Bioclimatic Modeling

This paper briefly reviews the process of exotic pest risk assessments and presents some examples of emerging opportunities for spatial bioclimatic modeling of exotic species in Canada. This type of analysis can support risk assessments but does not replace the need for on-going high quality field-based observations to validate and update models. Bioclimatic analysis of several exotic pests is provided to illustrate both opportunities and limits. A link is demonstrated to the National Forest Inventory to characterize timber volumes at risk for one exotic species. Challenges' are both scientific and administrative. More accessible and current field survey data are required to improve models. Our experience is that for many exotic species, historical, and even current, data are not always digital or quality controlled for taxonomic identity and accurate geo-referencing. This inhibits their use for integrated spatial modeling applications.     

Spatial Economic–Hydroecological Modelling and Evaluation of Land Use Impacts in the Vecht Wetlands Area

Wetlands provide many important goods and services to human societies, and generate nonuse values as well. Wetlands are also very sensitive ecosystems that are subject to much stress from human activities. Reducing the stress on wetlands requires a spatial matching between physical planning, hydrological and ecological processes, and economic activities. Spatially integrated modelling and evaluation can support this. The present study has developed a triple layer model that integrates information and concepts from social and natural sciences to address the analysis and evaluation of land-use scenarios for a wetlands area in the Netherlands, the Vecht area. This is the floodplain of river Vecht, located in the centre of the Netherlands. The study has resulted in a set of linked spatial hydrological, ecological and economic models, formulated at the level of grids and polders. The main activities incorporated in the system of models are housing, infrastructure, agriculture, recreation and nature conservation. The formulation of alternative development scenarios is dominated by land use and land cover options that are consistent with the stimulation of agriculture, nature or recreation. Two aggregate performance indicators have been constructed from model output, namely net present value of changes and environmental quality. The spatial characteristics of these indicators are retained in a spatial evaluation that ranks scenarios.     

Use of a non-planning driving background change methodology to assess the land-use planning impact on the environment

The implementation of land-use planning (LUP) impacts on the environment and may degrade regional environmental quality due to the changes in land-use patterns. To minimize the environmental impact of non-LUP driven (NPD) land-use background changes (LUBC), we propose an NPD land-use background change (NPD-LUBC) methodology to improve the LUP environmental impact assessment (LUPEA) process. With the application of the state-impact-state (SIS) framework and spatial information technologies including remote sensing and geographic information system (GIS), the methodology was tested on the eastern Chinese city of Lianyungang. The results indicate that (1) our proposed methodology is capable of distinguishing the environmental impact in implementing a target LUP, i.e. a LUP scheme to be assessed, from those driven by NPD-LUBC; (2) the prediction of NPD Land-use pattern simulated through the discrimination of NPD forces provides the basic data to support the LUPEA using our proposed methodology; (3) SO₂, PM₁₀, and COD emitted into the atmosphere and water are the major pollutants considered in the LUPEA of the city; and (4) despite the positive impacts of the comprehensive land-use change under the direction of target LUP, the implementation of LUP would have negative impacts on the regional environment in the entire study area and in individual counties/districts respectively in 2020. Our proposed methodology provides a new and operable way for the areas with data to simulate NPD land-use pattern and predict the individual indicator values for the assessment.     

Mapping forest composition from the Canadian National Forest Inventory and land cover classification maps

Canada's National Forest Inventory (CanFI) provides coarse-grained, aggregated information on a large number of forest attributes. Though reasonably well suited for summary reporting on national forest resources, the coarse spatial nature of this data limits its usefulness in modeling applications that require information on forest composition at finer spatial resolutions. An alternative source of information is the land cover classification produced by the Canadian Forest Service as part of its Earth Observation for Sustainable Development of Forests (EOSD) initiative. This product, which is derived from Landsat satellite imagery, provides relatively high resolution coverage, but only very general information on forest composition (such as conifer, mixedwood, and deciduous). Here we link the CanFI and EOSD products using a spatial randomization technique to distribute the forest composition information in CanFI to the forest cover classes in EOSD. The resultant geospatial coverages provide randomized predictions of forest composition, which incorporate the fine-scale spatial detail of the EOSD product and agree in general terms with the species composition summaries from the original CanFI estimates. We describe the approach and provide illustrative results for selected major commercial tree species in Canada.     

Using a hybrid approach to optimize experimental network design for aquifer parameter identification

The main objective of this study is to evaluate the land-use change and its relationship with its driving factors in the loess hilly region. In this study, a case study was carried out in Pengyang County. We set two land-use demand scenarios (a baseline scenario (scenario 1) and a real land-use requirement scenario (scenario 2)) during year 2001–2005 via assuming the effect of driving factors on land-use change keeps stable from 1993 to 2005. Two simulated land-use patterns of 2005 are therefore achieved accordingly by use of the conversion of land use and its effects model at small regional extent. Kappa analyses are conducted to compare each simulated land-use pattern with the reality. Results show that (1) the associated kappa values were decreased from 0.83 in 1993–2000 to 0.27 (in scenario 1) and 0.23 (in scenario 2) in 2001–2005 and (2) forest and grassland were the land-use types with highest commission errors, which implies that conversion of both the land-use types mentioned above is the main determinant of change of kappa values. Our study indicates the land-use change was driven by the synthetic multiply factors including natural and social–economic factors (e.g., slope, aspect, elevation, distance to road, soil types, and population dense) in 1993–2000 until “Grain for Green Project” was implemented and has become the dominant factor in 2001–2005.     

Wildfire seasonality and land use: when do wildfires prefer to burn?

Because of the increasing anthropogenic fire activity, understanding the role of land-use in shaping wildfire regimes has become a major concern. In the last decade, an increasing number of studies have been carried out on the relationship between land-use and wildfire patterns, in order to identify land-use types where fire behaves selectively, showing a marked preference (or avoidance) in terms of fire incidence. By contrast, the temporal aspects of the relationship between landuse types and wildfire occurrence have received far less attention. The aim of this paper is, thus, to analyze the temporal patterns of fire occurrence in Sardinia (Italy) during the period 2000–2006 to identify land-use types where wildfires occur earlier or later than expected from a random null model. The study highlighted a close relationship between the timing of fire occurrence and land-cover that is primarily governed by two complementary processes: climatic factors that act indirectly on the timing of wildfires determining the spatial distribution of land-use types, and human population and human pressure that directly influence fire ignition. From a practical viewpoint, understanding the temporal trends of wildfires within the different land-use classes can be an effective decision-support tool for fire agencies in managing fire risk and for producing provisional models of fire behavior under changing climatic scenarios and evolving landscapes.     

Spatial variation in soil carbon in the organic layer of managed boreal forest soil—implications for sampling design

We studied within-site spatial variation of the carbon stock in the organic layer of boreal forest soil. A total of 1,006 soil samples were taken in ten forest stands (five Scots pine stands and five Norway spruce stands). Our results indicate that the spatial autocorrelation disappears at a distance of 75-225 cm. This spatial autocorrelation should be taken into account in the sampling design by locating the sampling points at adequate intervals. With a sample size of over 20-30 samples per site, additional soil samples do not notably improve the precision of the site mean estimate. An adequate sample size is dependent on the purpose of sampling and on the site-specific soil variation. Our results on the dependence between sample size and precision of the mean estimates can be applied in designing efficient soil monitoring in boreal coniferous forests.     

Impact of model parameterization on predictive uncertainty of regional groundwater models in the context of environmental impact assessment

Parameterization strategy impacts models' outputs and the associated uncertainty. This is particularly true for transient regional groundwater models where parameters can only be weakly constrained by insufficient observations. However, this is rarely investigated under any particular model structure. This study bridges this gap using a regional groundwater model developed to understand the impact of coal seam gas extraction on groundwater systems in a probabilistic framework. Two different parameterization schemes were implemented for hydraulic conductivity and specific storage. The first method solely relies on the relationship between hydraulic properties and burial depths. The second more complex strategy allows more spatial variations of hydraulic parameters using pilot points. The study provides new insights and practical guidance on the application of groundwater modelling for environmental impact assessment. The results suggest that the choice of model parameterization has a significant influence on predictive uncertainty. The model using the simple parameterization provides predictions with a much wider range than the model with a more sophisticated parameterization. This is because that the lowly parameterized model tends to generate more extreme effective hydraulic parameter fields unless the parameterization simplification converts the inverse problem to a (close to) well-posed problem that rarely exists for applied regional groundwater modelling. The potential impact of model parameterization should be discussed explicitly in groundwater modelling applications to support decision making to avoid misinterpretation of the modelling results.     

Uncertainty in watershed response predictions induced by spatial variability of precipitation

Negligence to consider the spatial variability of rainfall could result in serious errors in model outputs. The objective of this study was to examine the uncertainty of both runoff and pollutant transport predictions due to the input errors of rainfall. This study used synthetic data to represent the “true” rainfall pattern, instead of interpolated precipitation. It was conducted on a synthetic case area having a total area of 20 km² with ten subbasins. Each subbasin has one rainfall gauge with synthetic precipitation records. Six rainfall storms with varied spatial distribution were generated. The average rainfall was obtained from all of the ten gauges by the arithmetic average method. The input errors of rainfall were induced by the difference between the actual rainfall pattern and estimated average rainfall. The results show that spatial variability of rainfall can cause uncertainty in modeling outputs of hydrologic, which would be transport to pollutant export predictions, when uniformity of rainfall is assumed. Since rainfall is essential information for predicting watershed responses, it is important to consider the properties of rainfall, particularly spatial rainfall variability, in the application of hydrologic and water quality models.     

Prediction of lichen diversity in an UNESCO biosphere reserve – correlation of high resolution remote sensing data with field samples

The present study focuses on developing models to predict lichen species richness in a UNESCO Biosphere Reserve of the Swiss Pre-Alps following a gradient of land-use intensity combining remote sensing data and regression models. The predictive power of the models and the obtained r ranging from 0.5 for lichens on soil to 0.8 for lichens on trees can be regarded as satisfactory to good, respectively. The study revealed that a combination of airborne and spaceborne remote sensing data produced a variety of ecological meaningful variables.     

A Spatial Model to Estimate Habitat Fragmentation and Its Consequences on Long-Term Persistence of Animal Populations

The increasing use of the landscape by humans has led to important diminutions of natural surfaces. The remaining patches of wild habitat are small and isolated from each other among a matrix of inhospitable land-uses. This habitat fragmentation, by disabling population movements and stopping their spread to new habitats, is a major threat to the survival of numerous plant and animal species. We developed a general model, adaptable for specific species, capable of identifying suitable habitat patches within fragmented landscapes and investigating the capacity of populations to move between these patches. This approach combines GIS analysis of a landscape, with spatial dynamic modeling. Suitable habitat is identified using a threshold area to perimeter ratio. Potential movement pathways of species between habitat patches are modeled using a cellular automaton. Habitat connectivity is estimated by overlaying habitat patches with movement pathways. The maximum potential population is calculated within and between connected habitat patches and potential risk of inbreeding within meta-populations is considered. The model was tested on a sample map and applied to scenario maps of predicted land-use change in the Peoria Tri-county region (IL). It (1) showed area of natural area alone was insufficient to estimate the consequences on animal populations; (2) underscored the necessity to use approaches investigating the effect of land-use change spatially through the landscape and the importance of considering species-specific life history characteristics; and (3) highlighted the model's potential utility as an indicator of species likelihood to be affected negatively by land-use scenarios and therefore requiring detailed investigation.