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.     

Incorporating low-resolution historic species location data decreases performance of distribution models

Developing robust species distribution models is important as model outputs are increasingly being incorporated into conservation policy and management decisions. A largely overlooked component of model assessment and refinement is whether to include historic species occurrence data in distribution models to increase the data sample size. Data of different temporal provenance often differ in spatial accuracy and precision. We test the effect of inclusion of historic coarse-resolution occurrence data on distribution model outputs for 187 species of birds in Australian tropical savannas. Models using only recent (after 1990), fine-resolution data had significantly higher model performance scores measured with area under the receiver operating characteristic curve (AUC) than models incorporating both fine- and coarse-resolution data. The drop in AUC score is positively correlated with the total area predicted to be suitable for the species (R² = 0.163-0.187, depending on the environmental predictors in the model), as coarser data generally leads to greater predicted areas. The remaining unexplained variation is likely to be due to the covariate errors resulting from resolution mismatch between species records and environmental predictors. We conclude that decisions regarding data use in species distribution models must be conscious of the variation in predictions that mixed-scale datasets might cause.     

Habitat classification modeling with incomplete data: pushing the habitat envelope.

Habitat classification models (HCMs) are invaluable tools for species conservation, land-use planning, reserve design, and metapopulation assessments, particularly at broad spatial scales. However, species occurrence data are often lacking and typically limited to presence points at broad scales. This lack of absence data precludes the use of many statistical techniques for HCMs. One option is to generate pseudo-absence points so that the many available statistical modeling tools can bb used. Traditional techniques generate pseudo-absence points at random across broadly defined species ranges, often failing to include biological knowledge concerning the species-habitat relationship. We incorporated biological knowledge of the species-habitat relationship into pseudo-absence points by creating habitat envelopes that constrain the region from which points were randomly selected. We define a habitat envelope as an ecological representation of a species, or species feature's (e.g., nest) observed distribution (i.e., realized niche) based on a single attribute, or the spatial intersection of multiple attributes. We created HCMs for Northern Goshawk (Accipiter gentilis atricapillus) nest habitat during the breeding season across Utah forests with extant nest presence points and ecologically based pseudo-absence points using logistic regression. Predictor variables were derived from 30-m USDA Landfire and 250-m Forest Inventory and Analysis (FIA) map products. These habitat-envelope-based models were then compared to null envelope models which use traditional practices for generating pseudo-absences. Models were assessed for fit and predictive capability using metrics such as kappa, threshold-independent receiver operating characteristic (ROC) plots, adjusted deviance (D(adj)2), and cross-validation, and were also assessed for ecological relevance. For all cases, habitat envelope-based models outperformed null envelope models and were more ecologically relevant, suggesting that incorporating biological knowledge into pseudo-absence point generation is a powerful tool for species habitat assessments. Furthermore, given some a priori knowledge of the species-habitat relationship, ecologically based pseudo-absence points can be applied to any species, ecosystem, data resolution, and spatial extent.     

Landscape-level impact of tropical forest loss and fragmentation on bird occurrence in eastern Guatemala

Tropical forest destruction and fragmentation of habitat patches may reduce population persistence at the landscape level. Given the complex nature of simultaneously evaluating the effects of these factors on biotic populations, statistical presence/absence modelling has become an important tool in conservation biology. This study uses logistic regression to evaluate the independent effects of tropical forest cover and fragmentation on bird occurrence in eastern Guatemala. Logistic regression models were constructed for 10 species with varying response to habitat alteration. Predictive variables quantified forest cover, fragmentation and their interaction at three different radii (200, 500 and 1000 m scales) of 112 points where presence of target species was determined. Most species elicited a response to the 1000 m scale, which was greater than most species’ reported territory size. Thus, their presence at the landscape scale is probably regulated by extra-territorial phenomena, such as dispersal. Although proportion of forest cover was the most important predictor of species’ presence, there was strong evidence of area-independent and -dependent fragmentation effects on species presence, results that contrast with other studies from northernmost latitudes. Species’ habitat breadth was positively correlated with AIC model values, indicating a better fit for species more restricted to tropical forest. Species with a narrower habitat breadth also elicited stronger negative responses to forest loss. Habitat breadth is thus a simple measure that can be directly related to species’ vulnerability to landscape modification. Model predictive accuracy was acceptable for 4 of 10 species, which were in turn those with narrower habitat breadths.     

Are existing vegetation maps adequate to predict bird distributions?

Bird species are selective on the vegetation types in which they are found but predictive models of bird distribution based on variables derived from land-use/land-cover maps tend to have limited success. It has been suggested that accuracy of existing maps used to derive predictors is in part responsible for the limited success of bird distribution models. In two areas of 4900 km² of Western Andalusia, Spain, we compared the predictive ability of bird distribution models derived from two existing general-purpose land-use/land-cover maps, which differ in their resolution and accuracy: a coarse scale vegetation map of Europe, the CORINE land-cover map, and a detailed regional map, the 1995 land-use/land-cover map of Andalusia from the SINAMBA (Consejerı́a de Medio Ambiente, Junta de Andalucı́a). We compared the bird distribution models derived from these general-purpose vegetation maps with models derived from two more accurate structural vegetation maps built considering directly variables that influence bird habitat selection, one built from satellite images for this study and another obtained by improving the resolution and accuracy of the SINAMBA map with satellite data. We sampled the presence/absence of bird species at 857 points using 15-min point surveys. Predictive models for 54 bird species were built with generalised additive models (GAMs), using as potential predictors the same set of landscape and vegetation structure variables measured on each map. We compared for each bird species the predictive accuracy of the best model derived from each map. Vegetation structure measured at bird sample points was used as ground-truth for comparing the accuracy of vegetation maps. Although maps differed in their resolution and accuracy, the results show that all of them produced similarly accurate bird distribution models, with a mixed map produced with both thematic and satellite information being the best. The models derived from the more accurate vegetation structure maps obtained from satellite data were not more accurate than those derived directly from the SINAMBA or CORINE maps. Our results suggest that some general-purpose land-use/land-cover maps are accurate enough to derive bird distribution models. There is a certain limit to improve vegetation maps above which there is no effect in their power to predict bird distribution.     

Hydraulic modeling of mussel habitat at a bridge-replacement site, Allegheny River, Pennsylvania, USA

The Allegheny River in Pennsylvania supports a large and diverse freshwater-mussel community, including two federally listed endangered species, Pleurobema clava (Clubshell) and Epioblasma torulosa rangiana (Northern Riffleshell). It is recognized that river hydraulics and morphology play important roles in mussel distribution. To assess the hydraulic influences of bridge replacement on mussel habitat, metrics such as depth, velocity, and their derivatives (shear stress, Froude number) were collected or computed.The objectives of the project were to evaluate mussel and hydraulic data at a reference site and to compare those findings to a bridge-replacement site. The findings were used to support a statistical analysis, which establishes correlations between mussel count and hydraulics, and a numerical model to forecast habitat based on the statistics.ArcGIS was selected to manage the data and generate a grid to compute area statistics for 3319, 4.9-m × 4.9-m cells (cell) for total mussel count, depth, velocity, shear stress, and Froude number. The Wilcoxon Rank Sum test indicated no statistical significance between the total mussel count and the hydraulic variables; however, trellis graphs were used to account for the spatial variability in the data set. For the flow conditions measured, the total mussel count per cell is greatest at sections where (1) velocities range from 0.061 to 0.21 m/s, (2) shear stresses range from 0.48 to 3.8 dyne/cm², and (3) Froude numbers range from 0.006 to 0.04.Based on the statistical targets established, the hydraulic model results suggest that an additional 2428 m² or a 30-percent increase in suitable mussel habitat could be generated at the replacement-bridge site when compared to the baseline condition associated with the existing bridge at that same location. The study did not address the influences of substrate, acid mine drainage, sediment loads from tributaries, and surface-water/ground-water exchange on mussel habitat. Future studies could include methods for quantifying (1) channel-substrate composition and distribution using tools such as hydroacoustic echosounders specifically designed and calibrated to identify bed composition and mussel populations, (2) surface-water and ground-water interactions, and (3) a high-streamflow event.     

Modeling net primary production of a fast-growing forest using a light use efficiency model

As interest grows in the quantification of global carbon cycles, Light Use Efficiency (LUE) model predictions of the forest net primary production (NPP) are being developed at an accelerating rate. Such models can provide useful predictions at large scales, but evaluating their performance has been difficult. In this study, a remote sensing-based LUE model was established to estimate forest NPP. Using the forest inventory data (FID) from the regional forest inventory survey in China and established allometric biomass equations, we calculated the biomass, the biomass increment, and the NPP of Eucalyptus urophylla (E. urophylla) plantation plots in the forestry jurisdiction of the Leizhou Forestry Bureau, Southern China. The FID-based NPP and the NPP from LUE model predictions were then compared to each other. Results show that the NPP from model predictions at a spatial resolution of 30 m × 30 m varied from 0 to 265 gC/(m² month) and showed regional differences. In addition, the stand age had variable effects on the average individual biomass of the E. urophylla plantation plots. The average individual biomass of the young and mid-age forests increased exponentially and logarithmically with the stand age (R² = 0.9178 and R² = 0.8683), respectively. For young and mid-age E. urophylla plantation plots, the LUE model-predicted NPP was fairly consistent with the FID-based NPP, but the model predictions of the NPP were higher than the estimates from FID. Through the analysis of the causes of uncertainty and the possible reasons for the discrepancy between the model-based NPP and FID-based NPP, the FID-derived estimates provided a foundation for model evaluation.     

Responses of Seed‐Dispersing Birds to Amount of Rainforest in the Landscape around Fragments

Habitat loss and fragmentation alter the composition of bird assemblages in rainforest. Because birds are major seed dispersers in rainforests, fragmentation‐induced changes to frugivorous bird assemblages are also likely to alter the ecological processes of seed dispersal and forest regeneration, but the specific nature of these changes is poorly understood. We assessed the influence of fragment size and landscape forest cover on the abundance, species composition, and functional properties of the avian seed disperser community in an extensively cleared, former rainforest landscape of subtropical Australia. Bird surveys of fixed time and area in 25 rainforest fragments (1–139 ha in size across a 1800 km² region) provided bird assemblage data which were coupled with prior knowledge of bird species’ particular roles in seed dispersal to give measurements of seven different attributes of the seed disperser assemblage. We used multimodel regression to assess how patch size and surrounding forest cover (within 200 m, 1000 m, and 5000 m radii) influenced variation in the abundance of individual bird species and of functional groups based on bird species’ responses to fragmentation and their roles in seed dispersal. Surrounding forest cover, specifically rainforest cover, generally had a greater effect on frugivorous bird assemblages than fragment size. Amount of rainforest cover within 200 m of fragments was the main factor positively associated with abundances of frugivorous birds that are both fragmentation sensitive and important seed dispersers. Our results suggest a high proportion of local rainforest cover is required for the persistence of seed‐dispersing birds and the maintenance of seed dispersal processes. Thus, even small rainforest fragments can function as important parts of habitat networks for seed‐dispersing birds, whether or not they are physically connected by vegetation. Respuestas de Aves Dispersoras de Semillas al Incremento de Selvas en el Paisaje Alrededor de Fragmentos     

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.     

Movement distances enhance validity of predictive models

Including the distance species are able to move in predictive models improves conservation practice. Bird inventory projects carried out from 1993 to 2004 in Taiwan provide an opportunity to investigate the relationships among species distribution, movement distance, and the environment. We compared projected distributions of 17 Taiwanese endemic bird species using what we called the Standard Method (i.e. movement distance is zero) and what we called the Buffer Method (i.e. movement distance is longer than zero) in three presence-only models (GARP, MAXENT and LIVES). The Standard Method used species original occurrence records directly while the Buffer Method expanded the occurrence of species to areas 1 km² around each recorded location. We first tested the efficacy of the Buffer Method using ten common species of the 17, and then applied the method to two rare species of the 17. For both the common and rare species, the distributions predicted by the two methods showed slight but important differences. The Buffer Method for all species had a higher average predictive probability, while the Standard Method had a higher maximum predictive probability. Most of the values for the area under the curve (AUC) were over 0.8 with the exceptions of Taiwan Barbet (Megalaima nuchalis) and Taiwan Hwamei (Garrulax taewanus), which have recently separated from Indochinese Barbet (Megalaima annamensis) and Chinese Hwamei (Garrulax canorus), and since 2008 and 2006 have been regarded as species endemic to the study area. Kappa values showed good performance for all species using both methods. The Buffer Method, however, resulted in significantly higher sensitivity and accuracy values for all models of species (p < 0.05). We conclude that when modeling species distribution including the area where the species was censused along with areas within the minimum movement areas better defines the surrounding areas that might supplement core habitat requirements. Therefore, using the Buffer Method, species surrounding distribution can be obtained which provides a better understanding of the species distributions. Given that distribution size is a key to the conservation of species, we suggest the Buffer Method can be used in conservation planning.     

Predicting suitable habitat for the European lobster (Homarus gammarus), on the Basque continental shelf (Bay of Biscay), using Ecological-Niche Factor Analysis

Predicting species distribution and habitat suitability (HS) modelling, across broad spatial scales, is now a major challenge in marine ecology. The resulting knowledge is of considerable use in supporting the implementation of environmental legislation, integrated coastal zone management and ecosystem-based fisheries management. This contribution considers the identification of seafloor morphological characteristics, together with wave energy conditions, that determine the presence of European lobster (Homarus gammarus); and it predicts suitable habitats over the Basque continental shelf (Bay of Biscay), in summer. The results obtained, by applying Ecological-Niche Factor Analysis (ENFA), indicate that lobster habitat differs considerably from the mean environmental condition over the study area; likewise, that it is restrictive in terms of the range of conditions in which they dwell. The best of the environmental predictors found to be: distance to the rock substrate; Benthic Position Index; wave flux over the seafloor; and the underlying bathymetry. A habitat suitability map was produced, with a high model quality (Boyce index: 0.98 ± 0.06). The most suitable habitat for European lobster are locations at the boundary between sedimentary- and rocky-bottoms, coincident with seafloor depressions with a steep slope, with medium to high wave energy conditions, and located within a range of water depths of 35–40 m. This approach demonstrates the applicability of the method in case studies where only presence data are available, together with the inclusion of environmental variables obtained from different sources.     

Relationship between depth, sediment, latitude, and the structure of benthic infaunal assemblages on the mainland shelf of southern California

A regional benthic survey was conducted in 1994, and the data were used to assess the relationship among three habitat factors (depth, sediment grain size, and latitude) and the distribution of benthic infaunal assemblages on the southern California coastal shelf. Benthic samples were collected with a 0.1 m² Van Veen grab from 251 sites on the continental shelf (10–200 m deep) from Point Conception, California, to the United States–Mexico international border. The relationship between habitat and assemblages was investigated by conducting a Q-mode cluster analysis to define groups of stations with similar species composition and then examining whether differences were present in physical habitat attributes among those groups of stations. Analysis of data from 175 uncontaminated sites yielded four habitat-related benthic infaunal assemblages along the southern California coastal shelf: a shallow-water assemblage from 10–32 m, a mid-depth assemblage between 32 and 115 m, and two deep-water (115–200 m) assemblages, one in fine and one in coarse sediments. These empirically defined points in the depth and sediment grain size gradients can be used to define reference habitats for the development of biocriteria. Benthic abundance and diversity were greatest in the mid-depth assemblage, conforming to predictions for benthic assemblages in regions of upwelling. Within the 500 km of coastline examined, latitude was not an important factor in defining assemblages. Received: 3 December 1999 / Accepted: 9 October 2000     

Abolition of set-aside schemes, associated impacts on habitat structure and modelling of potential effects of cross-farm regulation

In intensively farmed regions, habitat fragmentation represents a major pressure on biodiversity. Depending on its spatial setting, set-aside land can increase size and connectivity of habitats and thus counteract fragmentation. In 2008, the EU-wide set-aside obligation was suspended and a large proportion of set-aside land was re-cultivated. With Denmark as case we apply an indicator to measure the effect of set-aside land on spatial structure of semi-natural habitats in term of habitat size and connectivity. Furthermore, we model effects of a hypothetical spatial regulation, where set-aside land with the greatest benefit for habitat structure is retained as uncultivated, while set-aside land with the least effect is re-cultivated. The model is applied to individual farms and to farm agglomerations of increasing sizes, enabling us to explore potential effects of cross-farm regulation. The novelty of our approach is the application of observed land-uses changes for modelling a hypothetical regulation working on a range of spatial scales. Results show that after abolition of set-aside schemes the effect of set-aside land on habitat structure was more than halved. Modelled spatial regulation considerably reduces impacts. Effects increase with increasing size of farm agglomerations. However, marginal benefits become negligible at agglomeration sizes over 36 km².     

Individual-based modeling of flooding and salinity effects on a coastal swamp forest

Coastal swamps are among the rapidly vanishing wetland habitats in Louisiana. Increased flooding, nutrient and sediment deprivation, and salt-water intrusion have been implicated as probable causes of the decline of coastal swamps. We developed a two-species individual-based forest succession model to compare the growth and composition of a cypress-tupelo swamp under various combinations of flooding intensity and salinity levels, using historical time-series of stage and salinity data as inputs. Our model simulates forest succession over 500 years by representing the growth, mortality, and reproduction of individual Taxodium distichum (baldcypress) and Nyssa aquatica (water tupelo) trees in a 1-km² spatial grid of 10 m × 10 m cells that vary in water levels and salinity through differences in elevation. We independently adjusted the elevations of each cell to obtain different grid-wide mean elevations and standard deviations of elevation; this affected the temporal and spatial pattern of flooding. We calibrated the model by adjusting selected parameters until averaged basal area, stem density and wood production rates under two different mean elevations (partially versus highly flooded) were qualitatively similar to comparable values reported for swamps in the literature. Corroboration involved comparing model predictions to four well-monitored contrasting habitat sites within the Maurepas Basin, Louisiana, USA. Model predictions of both species combined showed the same patterns among sites as the data, but the model overestimated wood production and the dominance of T. distichum. Exploratory simulations predicted that increased flooding leads to swamps with reduced basal areas and stem densities, while increased salinity resulted in lower basal areas at low salinity concentration (∼1-3 psu) and complete tree mortality at higher salinity concentrations (∼2-6 psu). Our model can provide insight into the succession dynamics of coastal swamps and information for the effective design of restoration actions.     

Quantifying relationships between bird and butterfly community shifts and environmental change.

Quantifying the manner in which ecological communities respond during a time of decreasing precipitation is a first step in understanding how they will respond to longer-term climate change. Here we coupled analysis of interannual variability in remotely sensed data with analyses of bird and butterfly community changes in montane meadow communities of the Greater Yellowstone Ecosystem. Landsat satellite imagery was used to classify these meadows into six types along a hydrological gradient. The northern portion of the ecosystem, or Gallatin region, has smaller mean patch sizes separated by ridges of mountains, whereas the southern portion of the ecosystem, or Teton region, has much larger patches within the Jackson Hole valley. Both support a similar suite of butterfly and bird species. The Gallatin region showed more overall among-year variation in the normalized difference vegetation index (NDVI) when meadow types were pooled within regions, perhaps because the patch sizes are smaller on average. Bird and butterfly communities showed significant relationships relative to meadow type and NDVI. We identified several key species that are tightly associated with specific meadow types along the hydrological gradient. Comparing taxonomic groups, fewer birds showed specific habitat affinities than butterflies, perhaps because birds are responding to differences in habitat structure among meadow types and using the landscape at a coarser scale than the butterflies. Comparing regions, the Teton region showed higher predictability of community assemblages as compared to the Gallatin region. The Gallatin region exhibited more significant temporal trends with respect to butterflies. Butterfly communities in wet meadows showed a distinctive shift along the hydrological gradient during a drought period (1997-2000). These results imply that the larger Teton meadows will show more predictable (i.e., static) species-habitat associations over the long term, but that the smaller Gallatin meadows may be an area that will exhibit the effects of global climate change faster.     

Evaluating the effects of foraging habitat restoration on shorebird reproduction: the importance of performance criteria and comparative design

Coastal development and engineering projects preclude ecosystem processes that provide habitat for beach nesting birds. Management for coastal species may depend on actions that attempt to restore important habitat features and mitigate disturbance effects. However, species response to restoration or other management actions may be difficult to predict or measure. At Jones Beach State Park, on Long Island, New York, a 0.49 ha restoration project provided moist substrate foraging habitat for breeding Piping Plovers (Charadrius melodus) from 2002 to 2005. We examined whether foraging habitat restoration affected Piping Plover breeding population size, productivity, and fledgling production within 300 m of the restoration site. We found a positive relationship between habitat restoration and the number of fledglings produced per year. However, foraging habitat restoration did not significantly increase the number of Plover pairs breeding at Jones Beach. Our ability to evaluate restoration effects on Plovers depended on: 1) use of multiple performance criteria; 2) a design that allowed comparison of pre- and post-restoration data; and 3) a spatial control that allowed comparison of similar areas that were near and far from the restoration site. Despite the small size of the restoration project, there were measurable benefits to Plovers, indicating that foraging habitat restoration may be an effective tool for species recovery.     

Fractal properties of spatial distribution of intertidal benthic communities

We studied the spatial distribution of intertidal macrozoobenthos, microphytobenthos (diatom algae) and sediments at scales from decimeters to kilometers using an index of spatial homogeneity, D _I. Sediments were found to be randomly distributed, making up a mosaic of silty and sandy sites. On the contrary, the estimated spatial variability of macrofauna within all the scales up to 5500 m depended upon neither extent (total area covered) nor grain (finest spatial resolution) but only their ratio. We treat this as evidence of statistical self-similarity (fractal property) of the pattern. For diatoms, spatial heterogeneity of community structure was also self-similar in the range from 0.25 to 75 m (within a single bight). At larger scales, microalgae showed a combination of patchy structure with pronounced gradient along the shoreline from brackish-water to marine flora. Thus, fractal properties of both groups became manifested at scales corresponding to their mean body size. The ranges of fractal patterns were approximately equal to 10³–10⁵ if measured in body size units. We suggest that fractal-like spatial structures may be a general feature of communities, and speculate on the nature of such patterns. Received: 16 February 1999 / Accepted: 30 December 1999     

A spatial model of white sturgeon rearing habitat in the lower Columbia River,USA

Concerns over the potential effects of in-water placement of dredged materials prompted us to develop a GIS-based model that characterizes in a spatially explicit manner white sturgeon Acipenser transmontanus rearing habitat in the lower Columbia River, USA. The spatial model was developed using water depth, riverbed slope and roughness, fish positions collected in 2002, and Mahalanobis distance (D²). We created a habitat suitability map by identifying a Mahalanobis distance under which >50% of white sturgeon locations occurred in 2002 (i.e., high-probability habitat). White sturgeon preferred relatively moderate to high water depths, and low to moderate riverbed slope and roughness values. The eigenvectors indicated that riverbed slope and roughness were slightly more important than water depth, but all three variables were important. We estimated the impacts that fill might have on sturgeon habitat by simulating the addition of fill to the thalweg, in 3-m increments, and recomputing Mahalanobis distances. Channel filling simulations revealed that up to 9 m of fill would have little impact on high-probability habitat, but 12 and 15 m of fill resulted in habitat declines of ∼12% and ∼45%, respectively. This is the first spatially explicit predictive model of white sturgeon rearing habitat in the lower Columbia River, and the first to quantitatively predict the impacts of dredging operations on sturgeon habitat. Future research should consider whether water velocity improves the accuracy and specificity of the model, and to assess its applicability to other areas in the Columbia River.     

Estimating Plecoglossus altivelis altivelis migration using a mass balance model expressed by hydrological distribution parameters in a major limpid river basin in Japan

We examined the influence of several hydrological and meteorological parameters on the migratory movements of ayu Plecoglossus altivelis altivelis in central Japan. When comprehensively evaluating rivers and ayu behaviour on a catchment scale, the subjects of analysis typically include human activities and hydrological and meteorological phenomena. However, limiting analyses to such factors may be too restrictive when human activities are being conducted. Accordingly, we incorporated a biological viewpoint into the evaluation method, analysing hydrological data (river discharge, river water temperature, sea water temperature) to determine watershed characteristics and examining the relationship between these characteristics and the habitat conditions of ayu. Then we constructed a numerical model for ayu migratory runs that incorporated ayu ecology and watershed characteristics. Analyses of ayu movements from a lower estuarine dam demonstrated that downstream displacements were associated with high water flows of more than 200 m³ s⁻¹ at the beginning of summer. We conclude that it is important to consider the effects of environmental parameters on the movements of different fish species to understand the causes of spatial variation in fish distribution in lowland rivers.