A novel method for mapping reefs and subtidal rocky habitats using artificial neural networks

Reefs and subtidal rocky habitats are sites of high biodiversity and productivity which harbour commercially important species of fish and invertebrates. Although the conservation management of reef associated species has been informed using species distribution models (SDM) and community based approaches, to date their use has been constrained to specific regions where the locality and spatial extent of reefs is well known. Much of the world's subtidal habitats remain either undiscovered or unmapped, including coasts of intense human use. Consequently, to facilitate a stronger understanding of species-environmental relationships there is an urgent need for a cost and time effective standard method to map reefs at fine spatial resolutions across broad geographical extents. We used bathymetric data (∼250 m resolution) to calculate the local slope and curvature of the seabed. We then constructed artificial neural networks (ANNs) to forecast the probability of reef occurrence within grid cells as a function of bathymetric and slope variables. Testing over an independent data set not used in training showed that ANNs were able to accurately predict the location of reefs for 86% of all grid cells (Kappa = 0.63) without over fitting. The ANN with greatest support, combining bathymetric values of the target grid cell with the slope of adjacent grid cells, was used to map inshore reef locations around the Southern Australian coastline (∼250 m resolution). Broadly, our results show that reefs are identifiable from coarse-scale bathymetry data of the seabed. We anticipate that our research technique will strengthen systematic conservation planning tools in many regions of the world, by enabling the identification of rocky substratum and mapping in localities that remain poorly surveyed due to logistics or monetary constraints.     

Evolutionary diversity and spatiotemporal dynamics of a spatial game

In this paper, the evolution of cooperation is studied by a spatially structured evolutionary game model in which the players are located on a two-dimensional square lattice. Each player can choose one of the following strategies: “always defect” (ALLD), “tit-for-tat” (TFT), and “always cooperate” (ALLC). Players merely interact with four immediate neighbors at first and adjust strategies according to their rewards. First, the evolutionary dynamics of the three strategies in non-spatial population is investigated, and the results indicate that cooperation is not favored in most settings without spatial structure. Next, an analytical method, which is based on comparing the local payoff structures, is introduced for the spatial game model. Using the conditions derived from the method as criteria, the parameter plane for two major parameters of the spatial game model is divided and nine representative regions are identified. In each parameter region, a distinct spatiotemporal dynamics is characterized. The spatiotemporal dynamics not only verify that the spatial structure promote the evolution of cooperation but also reveal how cooperation is favored. Our results show that spatial structure is the keystone of the evolution of intraspecific diversity.     

The performance of state-of-the-art modelling techniques depends on geographical distribution of species

We explored the effects of prevalence, latitudinal range and clumping (spatial autocorrelation) of species distribution patterns on the predictive accuracy of eight state-of-the-art modelling techniques: Generalized Linear Models (GLMs), Generalized Boosting Method (GBM), Generalized Additive Models (GAMs), Classification Tree Analysis (CTA), Artificial Neural Network (ANN), Multivariate Adaptive Regression Splines (MARS), Mixture Discriminant Analysis (MDA) and Random Forest (RF). One hundred species of Lepidoptera, selected from the Distribution Atlas of European Butterflies, and three climate variables were used to determine the bioclimatic envelope for each butterfly species. The data set consisting of 2620 grid squares 30′ × 60′ in size all over Europe was randomly split into the calibration and the evaluation data sets. The performance of different models was assessed using the area under the curve (AUC) of a receiver operating characteristic (ROC) plot. Observed differences in modelling accuracy among species were then related to the geographical attributes of the species using GAM. The modelling performance was negatively related to the latitudinal range and prevalence, whereas the effect of spatial autocorrelation on prediction accuracy depended on the modelling technique. These three geographical attributes accounted for 19–61% of the variation in the modelling accuracy. Predictive accuracy of GAM, GLM and MDA was highly influenced by the three geographical attributes, whereas RF, ANN and GBM were moderately, and MARS and CTA only slightly affected. The contrasting effects of geographical distribution of species on predictive performance of different modelling techniques represent one source of uncertainty in species spatial distribution models. This should be taken into account in biogeographical modelling studies and assessments of climate change impacts.     

Selecting parameters for calibration via sensitivity analysis: An individual-based model of mosquitofish population dynamics

A stochastic individual-based model (IBM) of mosquitofish population dynamics in experimental ponds was constructed in order to increase, virtually, the number of replicates of control populations in an ecotoxicology trial, and thus to increase the statistical power of the experiments. In this context, great importance had to be paid to model calibration as this conditions the use of the model as a reference for statistical comparisons. Accordingly, model calibration required that both mean behaviour and variability behaviour of the model were in accordance with real data. Currently, identifying parameter values from observed data is still an open issue for IBMs, especially when the parameter space is large. Our model included 41 parameters: 30 driving the model expectancy and 11 driving the model variability. Under these conditions, the use of “Latin hypercube” sampling would most probably have “missed” some important combinations of parameter values. Therefore, complete factorial design was preferred. Unfortunately, due to the constraints of the computational capacity, cost-acceptable “complete designs” were limited to no more than nine parameters, the calibration question becoming a parameter selection question. In this study, successive “complete designs” were conducted with different sets of parameters and different parameter values, in order to progressively narrow the parameter space. For each “complete design”, the selection of a maximum of nine parameters and their respective n values was carefully guided by sensitivity analysis. Sensitivity analysis was decisive in selecting parameters that were both influential and likely to have strong interactions. According to this strategy, the model of mosquitofish population dynamics was calibrated on real data from two different years of experiments, and validated on real data from another independent year. This model includes two categories of agents; fish and their living environment. Fish agents have four main processes: growth, survival, puberty and reproduction. The outputs of the model are the length frequency distribution of the population and the 16 scalar variables describing the fish populations. In this study, the length frequency distribution was parameterized by 10 scalars in order to be able to perform calibration. The recently suggested notion of “probabilistic distribution of the distributions” was also applied to our case study, and was shown to be very promising for comparing length frequency distributions (as such).     

A criterion validity test of the contingent valuation method: comparing hypothetical and actual voting behavior for a public referendum

This study pursues external validation of contingent valuation by comparing survey results with the voting outcome of a Corvallis, Oregon, referendum to fund a riverfront improvement project through increased property taxes. Survey respondents hypothetically make a voting decision—with no financial consequences—on the upcoming referendum. The survey sample consists of respondents verified to have voted in the election. We use available precinct-level election data to compare the proportion of “yes” survey and referendum votes as well as estimate voting models and mean willingness to pay (WTP) based on the two sets of data. We find that survey responses match the actual voting outcome and WTP estimates based on the two are not statistically different. Contrary to similar studies, our statistical results do not depend on re-coding the majority of “undecided” survey responses to “no.” Furthermore, such a re-coding of responses may be inappropriate for our data set.     

The resource curse revisited and revised: A tale of paradoxes and red herrings

We critically evaluate the empirical basis for the so-called resource curse and find that, despite the topic's popularity in economics and political science research, this apparent paradox may be a red herring. The most commonly used measure of “resource abundance” can be more usefully interpreted as a proxy for “resource dependence”—endogenous to underlying structural factors. In multiple estimations that combine resource abundance and dependence, institutional, and constitutional variables, we find that (i) resource abundance, constitutions, and institutions determine resource dependence, (ii) resource dependence does not affect growth, and (iii) resource abundance positively affects growth and institutional quality.     

Creation and preservation of vegetation patterns by grazing

Structural patterns of tall stands (“tussock”) and short stands (“lawn”) are observed in grazed vegetation throughout the world. Such structural vegetation diversity influences plant and animal diversity. A possible mechanism for the creation and preservation of such patterns is a positive feedback between grazing and plant palatability. Although some theoretical studies have addressed this point in a non-spatial setting, the spatial consequences of this feedback mechanism on the stability and spatial characteristics of vegetation structure patterns have not been studied.We addressed this issue by analyzing a spatially explicit individual-based plant-grazer simulation model, based on published empirical relations and the assumption of optimal foraging.In the model, the selection by the grazer of short stands (that have a higher energy content and are more palatable) is affected by traveling costs and the spatial organization of swards. Nevertheless, the most selected biomass in this type of short stands was the optimal biomass predicted by cropping and digestion constraints. As a result of the optimal foraging strategy, the grazers displayed Lévy-flight traveling behavior during the simulations with characteristic exponent μ ≈ 2.Patterns of short and tall stands created by grazing were preserved for at least a decade. Even in seasonal habitat, the spatial organization of the patterns remained relatively constant, despite fluctuations in the area of short stands. Heterogeneity of initial vegetation increased heterogeneity of the grazing-induced pattern, but did not affect its stability.The area of short stands that was preserved by grazing scaled with the herbivore mass to the power 0.4 and with the carrying capacity of the vegetation to the power −0.75.Patterns of tall and short stands can be created and perpetuated by optimally grazing ruminants, irrespective of possible underlying soil patterns. The simulations generate predictions for the stability and spatial characteristics of such structural vegetation patterns.     

Transferability of Species Distribution Models: a Functional Habitat Approach for Two Regionally Threatened Butterflies

Abstract:  Numerous models for predicting species distribution have been developed for conservation purposes. Most of them make use of environmental data (e.g., climate, topography, land use) at a coarse grid resolution (often kilometres). Such approaches are useful for conservation policy issues including reserve-network selection. The efficiency of predictive models for species distribution is usually tested on the area for which they were developed. Although highly interesting from the point of view of conservation efficiency, transferability of such models to independent areas is still under debate. We tested the transferability of habitat-based predictive distribution models for two regionally threatened butterflies, the green hairstreak ( Callophrys rubi ) and the grayling ( Hipparchia semele ), within and among three nature reserves in northeastern Belgium. We built predictive models based on spatially detailed maps of area-wide distribution and density of ecological resources. We used resources directly related to ecological functions (host plants, nectar sources, shelter, microclimate) rather than environmental surrogate variables. We obtained models that performed well with few resource variables. All models were transferable—although to different degrees—among the independent areas within the same broad geographical region. We argue that habitat models based on essential functional resources could transfer better in space than models that use indirect environmental variables. Because functional variables can easily be interpreted and even be directly affected by terrain managers, these models can be useful tools to guide species-adapted reserve management.     

Optimal harvesting of ecologically interdependent fish species

The optimal exploitation of a two-species predator-prey system is considered, using Lotka-Volterra-type equations. Due to the density-dependence of ecological efficiency, both species should be harvested simultaneously over a range of relative prices. Beyond the limits of this price range, either the prey species should be utilized indirectly by harvesting the predator, or the predator should be eliminated to maximize the prey yield. Neglecting harvesting costs, the simultaneous harvest of prey and predators requires that a unit of prey biomass increase in value by being “processed” by predators. Certain results from single-species fishery models are shown not to apply to multispecies models. These are as follows: (i) Optimal regulation of a free access fishery may call for subsidizing instead of taxing the harvest of predator species. (ii) Increasing the discount rate may, at “moderate” levels, imply that the optimal standing stock of biomass increases instead of decreasing. (iii) A rising price or a falling cost per unit fishing effort of a species may raise and not lower the optimal standing stock of that species.     

The importance of spatial autocorrelation,extent and resolution in predicting forest bird occurrence

Concerns about declines in forest biodiversity underscore the need for accurate estimates of the distribution and abundance of organisms at large scales and at resolutions that are fine enough to be appropriate for management. This paper addresses three major objectives: (i) to determine whether the resolution of typical air photo-derived forest inventory is sufficient for the accurate prediction of site occupancy by forest birds. We compared prediction success of habitat models using air photo variables to models with variables derived from finer resolution, ground-sampled vegetation plots. (ii) To test whether incorporating spatial autocorrelation into habitat models via autologistic regression increases prediction success. (iii) To determine whether landscape structure is an important factor in predicting bird distribution in forest-dominated landscapes. Models were tested locally (Greater Fundy Ecosystem [GFE]) using cross-validation, and regionally using an independent data set from an area located ca. 250 km to the northwest (Riley Brook [RB]). We found significant positive spatial autocorrelation in the residuals of at least one habitat model for 76% (16/21) of species examined. In these cases, the logistic regression assumption of spatially independent errors was violated. Logistic models that ignored spatial autocorrelation tended to overestimate habitat effects. Though overall prediction success was higher for autologistic models than logistic models in the GFE, the difference was only significantly improved for one species. Further, the inclusion of spatial covariates did little to improve model performance in the geographically discrete study area. For 62% (13/21) of species examined, landscape variables were significant predictors of forest bird occurrence even after statistically controlling for stand-level variability. However, broad spatial extents explained less variation than local factors. In the GFE, 76% (16/21) of air photo and 81% (17/21) of ground plot models were accurate enough to be of practical utility (AUC > 0.7). When applied to RB, both model types performed effectively for 55% (11/20) of the species examined. We did not detect an overall difference in prediction success between air photo and ground plot models in either study area. We conclude that air photo data are as effective as fine resolution vegetation data for predicting site occupancy for the majority of species in this study. These models will be of use to forest managers who are interested in mapping species distributions under various timber harvest scenarios, and to protected areas planners attempting to optimize reserve function.     

A comparative study of prognostic MM5 meteorological modeling with aircraft, wind profiler, lidar, tethered balloon and RASS data over Philadelphia during a 1999 summer episode

This study presents a comparative evaluation of the prognostic meteorological Fifth Generation NCAR Pennsylvania State University Mesoscale Model (MM5) using data from the Northeast Oxidant and Particle Study (NE-OPS) research program collected over Philadelphia, PA during a summer episode in 1999. A set of model simulations utilizing a nested grid of 36 km, 12 km and 4 km horizontal resolutions with 21 layers in the vertical direction was performed for a period of 101 h from July 15, 1999; 12 UTC to July 19, 1999; 17 UTC. The model predictions obtained with 4 km horizontal grid resolution were compared with the NE-OPS observations. Comparisons of model temperature with aircraft data revealed that the model exhibited slight underestimation as noted by previous investigators. Comparisons of model temperature with aircraft and tethered balloon data indicate that the mean absolute error varied up to 1.5 °C. The comparisons of model relative humidity with aircraft and tethered balloon indicate that the mean relative error varied from –11% to –22% for the tethered balloon and from –5% to –30% for the aircraft data. The mean relative error for water vapor mixing ratio with respect to the lidar data exhibited a negative bias consistent with the humidity bias corresponding to aircraft and tethered balloon data. The tendency of MM5 to produce estimates of very low wind speeds, especially in the early-mid afternoon hours, as noted by earlier investigators, is seen in this study also. It is indeed true that the initial fields as well as the fields utilized in the data assimilation also contribute to some of the differences between the model and observations. Studies such as these which compare the grid averaged mean state variables with observations have inherent difficulties. Despite the above limitations, the results of the present study broadly conform to the general traits of MM5 as noted by earlier investigators.     

Modelling the horizontal spatial structure of planktonic community in Lake Trasimeno (Umbria,Italy) using multivariate geostatistical methods

Spatial heterogeneity of ecological systems has been recognised in recent years as an important ecological feature of an ecosystem, rather than a mere statistical nuisance. However, although considerable interest has been paid to the development of statistical methods for the analysis of spatial environmental data, when in presence of more species or environmental variables common analyses still fail to recognise the necessity of a joint modelling of the whole correlation structure. In this paper, we propose to study the multivariate spatial autocorrelation of a plankton community by making explicit reference to a spatial linear factor model entailing a set of constraints for the spatial structure of the planktonic species. The data set examined come from an intensive 2-day sampling survey performed in July 1991 on Lake Trasimeno (Italy) to investigate the horizontal spatial heterogeneity and distribution of the planktonic community, from small (50 m) to large (1000–10,000 m) scale. The analysis revealed that zooplankton and phytoplankton essentially have different degrees of heterogeneity and different spatial structures which required separate modelling. On the other hand, the similarity of the spatial autocorrelation found within zooplankton and phytoplankton communities, indicates that at the investigated scales of observation the horizontal organisation of both components is not appreciably affected by species-specific behaviours. The analysis of the multivariate spatial patterns emerging from the mapping of the extracted factors suggested an interpretation of the distribution of macrozooplankton and phytoplankton assemblages in terms of planktonic responses to environmental factors of a lake-size scale.     

The role of local and landscape level measures of greenness in modelling boreal plant species richness

Measurements of primary productivity and its heterogeneity based on satellite images can provide useful estimates of species richness and distribution patterns. However, species richness at a given site may depend not only on local habitat quality and productivity but also on the characteristics of the surrounding landscape. In this study we investigated whether the predictions of species richness of plant families in northern boreal landscape in Finland can be improved by incorporating greenness information from the surrounding landscape, as derived from remotely sensed data (mean, maximum, standard deviation and range values of NDVI derived from Landsat ETM), into local greenness models. Using plant species richness data of 28 plant families from 440 grid cells of 25 ha in size, generalized additive models (GAMs) were fitted into three different sets of explanatory variables: (1) local greenness only, (2) landscape greenness only, and (3) combined local and landscape greenness. The derived richness–greenness relationships were mainly unimodal or positively increasing but varied between different plant families, and depended also on whether greenness was measured as mean or maximum greenness. Incorporation of landscape level greenness variables improved significantly both the explanatory power and cross-validation statistics of the models including only local greenness variables. Landscape greenness information derived from remote sensing data integrated with local information has thus the potentiality to improve predictive assessments of species richness over extensive and inaccessible areas, especially in high-latitude landscapes. Overall, the significant relationship between plants and surrounding landscape quality detected here suggests that landscape factors should be considered in preserving species richness of boreal environments, as well as in conservation planning for biodiversity in other environments.     

Influence of grain size on species-habitat models

High resolution remote sensing data facilitate the use of small-scale habitat features such as trees or hedges in the analysis of species-habitat relationships. Such data potentially enable more accurate species-habitat mapping than lower resolution data. Here, for the first time, we systematically investigated this hypothesis by altering the spatial resolution from 1 m up to 1000 m grain size in species-habitat models of 13 bird species. The study area covered the Nidda river catchment in central Germany, a large heterogeneous landscape of 1620 km². A high resolution habitat map of the area was converted to coarser spatial and thematic resolutions in seven steps. We investigated how model performance responded to grain size, and we compared the differential effects of spatial resolution and thematic resolution on model performance. Explained deviance (D²) of the bird models generally decreased with coarser spatial resolution of the data, although it did not decrease monotonically in all species. On average across all species, model D² decreased from 41.5 at 1 m grain size to 15.9 at 1000 m grain size. Ten species were best modelled at 1 m, two species at 3 m and one species at 32 m grain size. Model performance degraded continuously with increasing grain size, both in habitat generalist and habitat specialist bird species, and was systematically lower in habitat generalists. The higher model performance observed at finer grain sizes was most likely caused by the combination of three factors: (1) high spatial accuracy of bird records and (2) a more precise location and delineation of habitat features and, (3) to a lesser degree, by more habitat types differentiated in maps of finer resolution. We conclude that higher spatial and thematic resolution data can be essential for deriving accurate predictions on bird distribution patterns from species-habitat models. Especially for bird species that are sensitive to specific land-use types or to small-scaled habitat features, a grain size of 1-3 m seems most promising.     

Decentralized regulation of a common property renewable resource industry with irreversible investment

The effectiveness of output controls for rationalizing a common property renewable resource has been called into question by the theoretical work of J. R. Gould [Economica, Nov., 383–402 (1972)]. A proper examination of this question requires an intertemporal analysis, one that takes into account that asymmetries between persistent factors of production (“immaleable capital”) and factors that are instantaneously consumed (“labor” or ldharvest effort”). We present here a nonlinear intertemporal model of a renewable resource industry, under conditions of irreversible capital investment, and undertake to analyze its dynamics, both at open access and under centralized optimal management. We then examine the theoretical possibility of decentralized regulation by Pigouvian taxes, and reconsider the proposition of Gould.     

Application of geostatistical methods to arsenic data from soil samples of the Cova dos Mouros mine (Vila Verde-Portugal)

A total of 286 soil samples were collected in the Cova dos Mouros area. All samples were dry sieved into the <200 mesh size fraction and analysed for Fe, Cu, Zn, Pb, Co, Ni, Bi and Mn by atomic absorption spectrometry (AAS) and for As, Se, Sb and Te by atomic absorption spectrometry-hydrid generation (AAS-HG). Only the results of arsenic are discussed in this paper although the survey was extended to all analysed chemical elements. The purpose of this study was to make a risk probability mapping for arsenic that would allow better knowledge about the vulnerability of the soil to arsenic contamination. To achieve this purpose, the initial variable was transformed into an indicator variable using as thresholds the risk-based standards (intervention values) for soils, as proposed by [Swartjes 1999. Risk based assessment of soil and groundwater quality in the Netherlands: Standards and remediation. J. Geochem. Explor.73 1–10]. To account for spatial structure, sample variograms were computed for the main directions of the sampling grid and a spherical model was fitted to each sample variogram (arsenic variable and indicator variables). The parameters of the spherical model fitted to the arsenic variable were used to predict arsenic concentrations at unsampled locations. A risk probability mapping was also done to assess the vulnerability of the soil towards the mining works. The parameters of the spherical model fitted to each indicator variable were used to estimate probabilities of exceeding the corresponding threshold. The use of indicator kriging as an alternative to ordinary kriging for the soil data of Cova dos Mouros produced unbiased probability maps that allowed assessment of the quality of the soil.     

Harvesting in an eight-species ecosystem

The theory for a general equilibrium ecosystem model that can include large number of interacting species is presented. Features include: (1) individual plants and animals are assumed to behave as if they are maximizing their net energy intake, (2) short- and long-run equilibriums are obtained, (3) species’ population adjustments depend on individual net energies. The theory is applied using simulations of an eight-species Alaskan marine ecosystem for which a “natural” equilibrium is calculated. Humans are introduced by adding a regulated open access fishery that harvests one of the species. Fishing impacts the fish population as well as the populations of other species, including Stellar sea lions, an endangered species. The sensitivity of fish and nonfish species populations to harvesting are calculated.     

GIS-based niche modeling for mapping species' habitat

Ecological "niche modeling" using presence-only locality data and large-scale environmental variables provides a powerful tool for identifying and mapping suitable habitat for species over large spatial extents. We describe a niche modeling approach that identifies a minimum (rather than an optimum) set of basic habitat requirements for a species, based on the assumption that constant environmental relationships in a species' distribution (i.e., variables that maintain a consistent value where the species occurs) are most likely to be associated with limiting factors. Environmental variables that take on a wide range of values where a species occurs are less informative because they do not limit a species' distribution, at least over the range of variation sampled. This approach is operationalized by partitioning Mahalanobis D2 (standardized difference between values of a set of environmental variables for any point and mean values for those same variables calculated from all points at which a species was detected) into independent components. The smallest of these components represents the linear combination of variables with minimum variance; increasingly larger components represent larger variances and are increasingly less limiting. We illustrate this approach using the California Gnatcatcher (Polioptila californica Brewster) and provide SAS code to implement it.