Wetlands : a perspective for mapping, monitoring, and modeling

Experience gained from numerous projects conducted by the U.S. Environmental Protection Agency's (EPA) Environmental Monitoring Systems Laboratory in Las Vegas, Nevada has provided insight to functional issues of mapping, monitoring, and modeling of wetland habitats. Three case studies in poster form describe these issues pertinent to managing wetland resources as mandated under Federal laws. A multiphase project was initiated by the EPA Alaska operations office to provide detailed wetland mapping of arctic plant communities in an area under petroleum development pressure. Existing classification systems did not meet EPA needs. Therefore a Habitat Classification System (HCS) derived from aerial photography was compiled. In conjunction with this photointerpretive keys were developed. These products enable EPA personnel to map large inaccessible areas of the arctic coastal plain and evaluate the sensitivity of various wetland habitats relative to petroleum development needs.

Development and application of a predictive model of freshwater fish assemblage composition to evaluate river health in eastern Australia

Multivariate predictive models are widely used tools for assessment of aquatic ecosystem health and models have been successfully developed for the prediction and assessment of aquatic macroinvertebrates, diatoms, local stream habitat features and fish. We evaluated the ability of a modelling method based on the River InVertebrate Prediction and Classification System (RIVPACS) to accurately predict freshwater fish assemblage composition and assess aquatic ecosystem health in rivers and streams of south-eastern Queensland, Australia. The predictive model was developed, validated and tested in a region of comparatively high environmental variability due to the unpredictable nature of rainfall and river discharge. The model was concluded to provide sufficiently accurate and precise predictions of species composition and was sensitive enough to distinguish test sites impacted by several common types of human disturbance (particularly impacts associated with catchment land use and associated local riparian, in-stream habitat and water quality degradation). The total number of fish species available for prediction was low in comparison to similar applications of multivariate predictive models based on other indicator groups, yet the accuracy and precision of our model was comparable to outcomes from such studies. In addition, our model developed for sites sampled on one occasion and in one season only (winter), was able to accurately predict fish assemblage composition at sites sampled during other seasons and years, provided that they were not subject to unusually extreme environmental conditions (e.g. extended periods of low flow that restricted fish movement or resulted in habitat desiccation and local fish extinctions).

Delineating environmental envelopes to improve mapping of species distributions, via a hurdle model with CART &/or MaxEnT

Species distribution modelling (SDM) typically analyses species’ presence together with some form of absence information. Ideally absences comprise observations or are inferred from comprehensive sampling. When such information is not available, then pseudo-absences are often generated from the background locations within the study region of interest containing the presences, or else absence is implied through the comparison of presences to the whole study region, e.g. as is the case in Maximum Entropy (MaxEnt) or Poisson point process modelling. However, the choice of which absence information to include can be both challenging and highly influential on SDM predictions (e.g. Oksanen and Minchin, 2002). In practice, the use of pseudo- or implied absences often leads to an imbalance where absences far outnumber presences. This leaves analysis highly susceptible to ‘naughty-noughts’: absences that occur beyond the envelope of the species, which can exert strong influence on the model and its predictions (Austin and Meyers, 1996). Also known as ‘excess zeros’, naughty noughts can be estimated via an overall proportion in simple hurdle or mixture models (Martin et al., 2005). However, absences, especially those that occur beyond the species envelope, can often be more diverse than presences. Here we consider an extension to excess zero models. The two-staged approach first exploits the compartmentalisation provided by classification trees (CTs) (as in O’Leary, 2008) to identify multiple sources of naughty noughts and simultaneously delineate several species envelopes. Then SDMs can be fit separately within each envelope, and for this stage, we examine both CTs (as in Falk et al., 2014) and the popular MaxEnt (Elith et al., 2006). We introduce a wider range of model performance measures to improve treatment of naughty noughts in SDM. We retain an overall measure of model performance, the area under the curve (AUC) of the Receiver-Operating Curve (ROC), but focus on its constituent measures of false negative rate (FNR) and false positive rate (FPR), and how these relate to the threshold in the predicted probability of presence that delimits predicted presence from absence. We also propose error rates more relevant to users of predictions: false omission rate (FOR), the chance that a predicted absence corresponds to (and hence wastes) an observed presence, and the false discovery rate (FDR), reflecting those predicted (or potential) presences that correspond to absence. A high FDR may be desirable since it could help target future search efforts, whereas zero or low FOR is desirable since it indicates none of the (often valuable) presences have been ignored in the SDM. For illustration, we chose Bradypus variegatus, a species that has previously been published as an exemplar species for MaxEnt, proposed by Phillips et al. (2006). We used CTs to increasingly refine the species envelope, starting with the whole study region (E0), eliminating more and more potential naughty noughts (E1–E3). When combined with an SDM fit within the species envelope, the best CT SDM had similar AUC and FPR to the best MaxEnt SDM, but otherwise performed better. The FNR and FOR were greatly reduced, suggesting that CTs handle absences better. Interestingly, MaxEnt predictions showed low discriminatory performance, with the most common predicted probability of presence being in the same range (0.00-0.20) for both true absences and presences. In summary, this example shows that SDMs can be improved by introducing an initial hurdle to identify naughty noughts and partition the envelope before applying SDMs. This improvement was barely detectable via AUC and FPR yet visible in FOR, FNR, and the comparison of predicted probability of presence distribution for pres/absence.

Predictive modelling to aid the regional-scale management of a vertebrate pest

Extensive resources are allocated to managing vertebrate pests, yet spatial understanding of pest threats, and how they respond to management, is limited at the regional scale where much decision-making is undertaken. We provide regional-scale spatial models and management guidance for European rabbits (Oryctolagus cuniculus) in a 260,791 km(2) region in Australia by determining habitat suitability, habitat susceptibility and the effects of the primary rabbit management options (barrier fence, shooting and baiting and warren ripping) or changing predation or disease control levels. A participatory modelling approach was used to develop a Bayesian network which captured the main drivers of suitability and spread, which in turn was linked spatially to develop high resolution risk maps. Policy-makers, rabbit managers and technical experts were responsible for defining the questions the model needed to address, and for subsequently developing and parameterising the model. Habitat suitability was determined by conditions required for warren-building and by above-ground requirements, such as food and harbour, and habitat susceptibility by the distance from current distributions, habitat suitability, and the costs of traversing habitats of different quality. At least one-third of the region had a high probability of being highly suitable (support high rabbit densities), with the model supported by validation. Habitat susceptibility was largely restricted by the current known rabbit distribution. Warren ripping was the most effective control option as warrens were considered essential for rabbit persistence. The anticipated increase in disease resistance was predicted to increase the probability of moderately suitable habitat becoming highly suitable, but not increase the at-risk area. We demonstrate that it is possible to build spatial models to guide regional-level management of vertebrate pests which use the best available knowledge and capture fine spatial-scale processes.

Geospatial environmental data modelling applications using remote sensing, GIS and spatial statistics

This thesis presents novel modelling applications for environmental geospatial data using remote sensing, GIS and statistical modelling techniques. The studied themes can be classified into four main themes: (i) to develop advanced geospatial databases. Paper (I) demonstrates the creation of a geospatial database for the Glanville fritillary butterfly (Melitaea cinxia) in the Åland Islands, south-western Finland; (ii) to analyse species diversity and distribution using GIS techniques. Paper (II) presents a diversity and geographical distribution analysis for Scopulini moths at a world-wide scale; (iii) to study spatiotemporal forest cover change. Paper (III) presents a study of exotic and indigenous tree cover change detection in Taita Hills Kenya using airborne imagery and GIS analysis techniques; (iv) to explore predictive modelling techniques using geospatial data. In Paper (IV) human population occurrence and abundance in the Taita Hills highlands was predicted using the generalized additive modelling (GAM) technique. Paper (V) presents techniques to enhance fire prediction and burned area estimation at a regional scale in East Caprivi Namibia. Paper (VI) compares eight state-of-the-art predictive modelling methods to improve fire prediction, burned area estimation and fire risk mapping in East Caprivi Namibia. The results in Paper (I) showed that geospatial data can be managed effectively using advanced relational database management systems. Metapopulation data for Melitaea cinxia butterfly was successfully combined with GPS-delimited habitat patch information and climatic data. Using the geospatial database, spatial analyses were successfully conducted at habitat patch level or at more coarse analysis scales. Moreover, this study showed it appears evident that at a large-scale spatially correlated weather conditions are one of the primary causes of spatially correlated changes in Melitaea cinxia population sizes. In Paper (II) spatiotemporal characteristics of Socupulini moths description, diversity and distribution were analysed at a world-wide scale and for the first time GIS techniques were used for Scopulini moth geographical distribution analysis. This study revealed that Scopulini moths have a cosmopolitan distribution. The majority of the species have been described from the low latitudes, sub-Saharan Africa being the hot spot of species diversity. However, the taxonomical effort has been uneven among biogeographical regions. Paper III showed that forest cover change can be analysed in great detail using modern airborne imagery techniques and historical aerial photographs. However, when spatiotemporal forest cover change is studied care has to be taken in co-registration and image interpretation when historical black and white aerial photography is used. In Paper (IV) human population distribution and abundance could be modelled with fairly good results using geospatial predictors and non-Gaussian predictive modelling techniques. Moreover, land cover layer is not necessary needed as a predictor because first and second-order image texture measurements derived from satellite imagery had more power to explain the variation in dwelling unit occurrence and abundance. Paper V showed that generalized linear model (GLM) is a suitable technique for fire occurrence prediction and for burned area estimation. GLM based burned area estimations were found to be more superior than the existing MODIS burned area product (MCD45A1). However, spatial autocorrelation of fires has to be taken into account when using the GLM technique for fire occurrence prediction. Paper VI showed that novel statistical predictive modelling techniques can be used to improve fire prediction, burned area estimation and fire risk mapping at a regional scale. However, some noticeable variation between different predictive modelling techniques for fire occurrence prediction and burned area estimation existed.

Farmland birds and habitat heterogeneity in intensively cultivated boreal agricultural landscapes

The biodiversity of farmland ecosystems has decreased remarkably during the latter half of the 20th century, and this development is due to intensive farming with its various environmental effects. In the countries of the EU the Common Agricultural Policy (CAP) is the main determinant affecting farmland biodiversity, since the agricultural policy defines guidelines of agricultural practices. In addition to policies promoting intensive farming, CAP also includes national agri-environment schemes (AES), in which a part of subsidies paid to farmers is directed to acts that are presumed to promote environmental protection and biodiversity. In order to shape AES into relevant and powerful tools for biodiversity protection, detailed studies on the effects of agriculture on species and species assemblages are needed. In my thesis I investigated the importance of habitat heterogeneity and effects of different habitat and landscape characteristics on farmland bird abundance and diversity in typical cereal cultivation-dominated southern Finnish agricultural environments. The extensive data used were collected by territory mapping. My two main study species were the drastically declined ortolan bunting (Emberiza hortulana) and the phenomenally increased tree sparrow (Passer montanus); in addition I studied assemblages of 20 species breeding in open arable and edge/bush habitats. In light of my results I discuss whether the Finnish AES take into account the habitat needs of farmland birds, and I provide suggestions for improvement of the future AES. My results show that heterogeneity of both uncultivated and cultivated habitats increases abundance and species richness among farmland birds, but in this respect the amount and diversity of uncultivated habitats are essential. Ditches in particular are a keystone structure for farmland birds in boreal landscapes. Ditches lined by trees or bushes increased ortolan bunting abundance. Loss of that kind of ditches (and clearance of forest and bush patches), reduced breeding ortolan buntings, mainly by decreasing availability of song-posts that are important for the breeding groups of the species. Heterogeneity of uncultivated habitats, most importantly open ditches and the habitat patch richness, increased densities and species richnesses of species assemblages of open arable and edge/bush habitats. Human impact (winter-feeding, nest-boxes) affected favourably the tree sparrow s rapid range expansion in southern Finland, but any habitat types had no significant effects. At the moment the Finnish agri-environmental policy does not conserve farmland ditches as a habitat type. Instead, sub-surface drainage is financially promoted. This is a fatal mistake as far as farmland biodiversity is concerned. In addition to the maintenance of ditches, at least the following aspects should be included more than is done previously in the measures of the future AES: 1) promotion of diverse crop rotation (especially by promoting animal husbandry), 2) maintenance of tree and bush vegetation in islets and along ditches, 3) promotion of organic farming.

Mapping the risk of soil-transmitted helminthic infections in the Philippines

Background In order to increase the efficient allocation of soil-transmitted helminth (STH) disease control resources in the Philippines, we aimed to describe for the first time the spatial variation in the prevalence of A. lumbricoides, T. trichiura and hookworm across the country, quantify the association between the physical environment and spatial variation of STH infection and develop predictive risk maps for each infection. Methodology/Principal Findings Data on STH infection from 35,573 individuals across the country were geolocated at the barangay level and included in the analysis. The analysis was stratified geographically in two major regions: 1) Luzon and the Visayas and 2) Mindanao. Bayesian geostatistical models of STH prevalence were developed, including age and sex of individuals and environmental variables (rainfall, land surface temperature and distance to inland water bodies) as predictors, and diagnostic uncertainty was incorporated. The role of environmental variables was different between regions of the Philippines. This analysis revealed that while A. lumbricoides and T. trichiura infections were widespread and highly endemic, hookworm infections were more circumscribed to smaller foci in the Visayas and Mindanao. Conclusions/Significance This analysis revealed significant spatial variation in STH infection prevalence within provinces of the Philippines. This suggests that a spatially targeted approach to STH interventions, including mass drug administration, is warranted. When financially possible, additional STH surveys should be prioritized to high-risk areas identified by our study in Luzon.

Optimal Guidance for Accurate Lunar Soft Landing with Minimum Fuel Consumption using Model Predictive Static Programming

In this paper the soft lunar landing with minimum fuel expenditure is formulated as a nonlinear optimal guidance problem. The realization of pinpoint soft landing with terminal velocity and position constraints is achieved using Model Predictive Static Programming (MPSP). The high accuracy of the terminal conditions is ensured as the formulation of the MPSP inherently poses final conditions as a set of hard constraints. The computational efficiency and fast convergence make the MPSP preferable for fixed final time onboard optimal guidance algorithm. It has also been observed that the minimum fuel requirement strongly depends on the choice of the final time (a critical point that is not given due importance in many literature). Hence, to optimally select the final time, a neural network is used to learn the mapping between various initial conditions in the domain of interest and the corresponding optimal flight time. To generate the training data set, the optimal final time is computed offline using a gradient based optimization technique. The effectiveness of the proposed method is demonstrated with rigorous simulation results.

Mapping and Distribution of Torpedograss and Evaluating the Effectiveness of Torpedograss Management Activities in Lake Okeechobee, Florida

Thousands of hectares of native plants and shallow open water habitat have been displaced in Lake Okeechobee’s marsh by the invasive exotic species torpedograss ( Panicum repens L.). The rate of torpedograss expansion, it’s areal distribution and the efficacy of herbicide treatments used to control torpedograss in the lake’s marsh were quantified using aerial color infra red (IR) photography.(PDF has 6 pages.)

Pilot investigation of remote sensing for intertidal oyster mapping in coastal South Carolina: a methods comparison

South Carolina’s oyster reefs are a major component of the coastal landscape. Eastern oysters Crassostrea virginica are an important economic resource to the state and serve many essential functions in the environment, including water filtration, creek bank stabilization and habitat for other plants and animals. Effective conservation and management of oyster reefs is dependent on an understanding of their abundance, distribution, condition, and change over time. In South Carolina, over 95% of the state’s oyster habitat is intertidal. The current intertidal oyster reef database for South Carolina was developed by field assessment over several years. This database was completed in the early 1980s and is in need of an update to assess resource/habitat status and trends across the state. Anthropogenic factors such as coastal development and associated waterway usage (e.g., boat wakes) are suspected of significantly altering the extent and health of the state’s oyster resources. In 2002 the NOAA Coastal Services Center’s (Center) Coastal Remote Sensing Program (CRS) worked with the Marine Resources Division of the South Carolina Department of Natural Resources (SCDNR) to develop methods for mapping intertidal oyster reefs along the South Carolina coast using remote sensing technology. The objective of this project was to provide SCDNR with potential methodologies and approaches for assessing oyster resources in a more efficiently than could be accomplished through field digitizing. The project focused on the utility of high-resolution aerial imagery and on documenting the effectiveness of various analysis techniques for accomplishing the update. (PDF contains 32 pages)

Seagrass and Aquatic Habitat Assessment Workshop Summary

Seagrass communities are among the richest and most productive, photoautotrophic coastal systems in the world. They protect and improve water quality, provide shoreline stabilization, and are important habitats for an array of fish, birds, and other wildlife. Hence, much can be gained by protecting and restoring these important living resources. Human’s impact on these vital resources from population growth, pollution, and physical damage from boating and other activities can disrupt the growth of these seagrasses communities and have devastating effects on their health and vitality. Inventory and monitoring are required to determine the dynamics of seagrasses and devise better protection and restoration for these rich resources. The purpose of this seagrass workshop, sponsored by NOAA’s CSC , USGS, and FMRI, was to move toward greater objectivity and accuracy in seagrass mapping and monitoring. This workshop helped foster interaction and communication among seagrass professionals. In order to begin the process of determining the best uniform mapping process for the biological research community. Increasing such awareness among the seagrass and management communities, it is hoped that an improved understanding of the monitoring and mapping process will lead to more effective and efficient preservation os submerged aquatic vegetation. (PDF contains 20 pages)

Acoustic signatures of the seafloor: Tools for predicting grouper habitat

Groupers are important components of commercial and recreational fisheries. Current methods of diver-based grouper census surveys could potentially benefit from development of remotely sensed methods of seabed classification. The goal of the present study was to determine if areas of high grouper abundance have characteristic acoustic signatures. A commercial acoustic seabed mapping system, QTC View Series V, was used to survey an area near Carysfort Reef, Florida Keys. Acoustic data were clustered using QTC IMPACT software, resulting in three main acoustic classes covering 94% of the area surveyed. Diver-based data indicate that one of the acoustic classes corresponded to hard substrate and the other two represented sediment. A new measurement of seabed heterogeneity, designated acoustic variability, was also computed from the acoustic survey data in order to more fully characterize the acoustic response (i.e., the signature) of the seafloor. When compared with diver-based grouper census data, both acoustic classification and acoustic variability were significantly different at sites with and without groupers. Sites with groupers were characterized by hard bottom substrate and high acoustic variability. Thus, the acoustic signature of a site, as measured by acoustic classification or acoustic variability, is a potentially useful tool for stratifying diver sampling effort for grouper census.

Predicting the effects of climate change on sea turtle nesting habitat in Florida

Rising global temperatures threaten the survival of many plant and animal species. Having already risen at an unprecedented rate in the past century, temperatures are predicted to rise between 0.3 and 7.5C in North America over the next 100 years (Hawkes et al. 2007). Studies have documented the effects of climate warming on phenology (timing of seasonal activities), with observations of early arrival at breeding grounds, earlier ends to the reproductive season, and delayed autumnal migrations (Pike et al. 2006). In addition, for species not suited to the physiological demands of cold winter temperatures, increasing temperatures could shift tolerable habitats to higher latitudes (Hawkes et al. 2007). More directly, climate warming will impact thermally sensitive species like sea turtles, who exhibit temperature-dependent sexual determination. Temperatures in the middle third of the incubation period determine the sex of sea turtle offspring, with higher temperatures resulting in a greater abundance of female offspring. Consequently, increasing temperatures from climate warming would drastically change the offspring sex ratio (Hawkes et al. 2007). Of the seven extant species of sea turtles, three (leatherback, Kemp’s ridley, and hawksbill) are critically endangered, two (olive ridley and green) are endangered, and one (loggerhead) is threatened. Considering the predicted scenarios of climate warming and the already tenuous status of sea turtle populations, it is essential that efforts are made to understand how increasing temperatures may affect sea turtle populations and how these species might adapt in the face of such changes. In this analysis, I seek to identify the impact of changing climate conditions over the next 50 years on the availability of sea turtle nesting habitat in Florida given predicted changes in temperature and precipitation. I predict that future conditions in Florida will be less suitable for sea turtle nesting during the historic nesting season. This may imply that sea turtles will nest at a different time of year, in more northern latitudes, to a lesser extent, or possibly not at all. It seems likely that changes in temperature and precipitation patterns will alter the distribution of sea turtle nesting locations worldwide, provided that beaches where the conditions are suitable for nesting still exist. Hijmans and Graham (2006) evaluate a range of climate envelope models in terms of their ability to predict species distributions under climate change scenarios. Their results suggested that the choice of species distribution model is dependent on the specifics of each individual study. Fuller et al. (2008) used a maximum entropy approach to model the potential distribution of 11 species in the Arctic Coastal Plain of Alaska under a series of projected climate scenarios. Recently, Pike (in press) developed Maxent models to investigate the impacts of climate change on green sea turtle nest distribution and timing. In each of these studies, a set of environmental predictor variables (including climate variables), for which ‘current’ conditions are available and ‘future’ conditions have been projected, is used in conjunction with species occurrence data to map potential species distribution under the projected conditions. In this study, I will take a similar approach in mapping the potential sea turtle nesting habitat in Florida by developing a Maxent model based on environmental and climate data and projecting the model for future climate data. (PDF contains 5 pages)

Littoral vegetation of Lake Tohopekaliga: community descriptions prior to a large-scale fisheries habitat-enhancement project

An extreme dry-down and muck-removal project was conducted at Lake Tohopekaliga, Florida, in 2003-2004, to remove dense vegetation from inshore areas and improve habitat degraded by stabilized water levels. Vegetation was monitored from June 2002 to December 2003, to describe the pre-existing communities in terms of composition and distribution along the environmental gradients. Three study areas (Treatment-Selection Sites) were designed to test the efficacy of different treatments in enhancing inshore habitat, and five other study areas (Whole-Lake Monitoring Sites) were designed to monitor the responses of the emergent littoral vegetation as a whole. Five general community types were identified within the study areas by recording aboveground biomasses and stem densities of each species. These communities were distributed along water and soils gradients, with water depth and bulk density explaining most of the variation. The shallowest depths were dominated by a combination of Eleocharis spp., Luziola fluitans, and Panicum repens; while the deeper areas had communities of Nymphaea odorata and Nuphar luteum; Typha spp.; or Paspalidium geminatum and Hydrilla verticillata. Mineralized soils were common in both the shallow and deep-water communities, while the intermediate depths had high percentages of organic material in the soil. These intermediate depths (occurring just above and just below low pool stage) were dominated by Pontederia cordata, the main species targeted by the habitat enhancement project. This emergent community occurred in nearly monocultural bands around the lake (from roughly 60–120 cm in depth at high pool stage) often having more diverse floating mats along the deep-water edge. The organic barrier these mats create is believed to impede access of sport fish to shallow-water spawning areas, while the overall low diversity of the community is evidence of its competitive nature in stabilized waters. With continued monitoring of these study areas long-term effects of the restoration project can be assessed and predictive models may be created to determine the efficacy and legitimacy of such projects in the future.

The use of multibeam sonar mapping techniques to refine population estimates of the endangered white abalone (Haliotis sorenseni)

Multibeam sonar mapping techniques provide detailed benthic habitat information that can be combined with the data on species-specific habitat preferences to provide highly accurate calculations of populations in a particular area. The amount of suitable habitat available for the endangered white abalone (Haliotis sorenseni) was quantified to aid in obtaining an accurate estimate of the number of remaining individuals at two offshore banks and one island site off the coast of southern California. Habitat was mapped by using multibeam sonar survey techniques and categorized by using rugosity and topographic position analysis. Abalone densities were evaluated by using a remotely operated vehicle and video transect methods. The total amount of suitable habitat at these three sites was far greater than that previously estimated. Therefore, although present estimates of white abalone densities are several orders of magnitude lower than historic estimates, the total population is likely larger than previously reported because of the additional amount of habitat surveyed in this study.