Moving Beyond Static Species Distribution Models in Support of Conservation Biogeography

Aim: To demonstrate that multi-modeling methods have effectively been used to combine static species distribution models (SDM), predicting the geographical pattern of suitable habitat, with dynamics landscape and population models in order to forecast the impacts of environmental change on species, an important goal of conservation biogeography. Methods: Three approaches were considered: a) incorporating models of species migration in order to understand the ability of a species to occupy suitable habitat in new locations; b) linking models of landscape disturbance and succession to models of habitat suitability; and, c) fully linking models of habitat suitability, habitat dynamics and spatially-explicit population dynamics. Results: Linking species-environment relationships, landscape dynamics and population dynamics in a multi-modeling framework allows the combined impacts of climate change (affecting species distribution and vital rates) and land cover dynamics (land use change, altered disturbance regimes) on species be predicted. This approach is only feasible if the life history parameters and habitat requirements of the species are well understood. Main Conclusions: Forecasts of the impacts of global change on species have been improved by considering multiple causes. A range of methods are available to address the interactions of changing habitat suitability, habitat dynamics and population response that vary in their complexity, realism and data requirements.

Canopy reflectance modeling in a tropical wooded grassland

Geometric/optical canopy reflectance modeling and spatial/spectral pattern recognition is used to study the form and structure of savanna in West Africa. An invertible plant canopy reflectance model is tested for its ability to estimate the amount of woody vegetation from remotely sensed data in areas of sparsely wooded grassland. Dry woodlands and wooded grasslands, commonly referred to as savannas, are important ecologically and economically in Africa, and cover approximately forty percent of the continent by some estimates. The Sahel and Sudan savannas make up the important and sensitive transition zone between the tropical forests and the arid Sahara region. The depletion of woody cover, used for fodder and fuel in these regions, has become a very severe problem for the people living there. LANDSAT Thematic Mapper (TM) data is used to stratify woodland and wooded grassland into areas of relatively homogeneous canopy cover, and then an invertible forest canopy reflectance model is applied to estimate directly the height and spacing of the trees in the stands. Because height and spacing are proportional to biomass in some cases, a successful application of the segmentation/modeling techniques will allow direct estimation of tree biomass, as well as cover density, over significant areas of these valuable and sensitive ecosystems. The model being tested in sites in two different bioclimatic zones in Mali, West Africa, will be used for testing the canopy model. Sudanian zone crop/woodland test sites were located in the Region of Segou, Mali

Improved canopy reflectance modeling and scene inference through improved understanding of scene pattern

The Li-Strahler reflectance model, driven by LANDSAT Thematic Mapper (TM) data, provided regional estimates of tree size and density within 20 percent of sampled values in two bioclimatic zones in West Africa. This model exploits tree geometry in an inversion technique to predict average tree size and density from reflectance data using a few simple parameters measured in the field (spatial pattern, shape, and size distribution of trees) and in the imagery (spectral signatures of scene components). Trees are treated as simply shaped objects, and multispectral reflectance of a pixel is assumed to be related only to the proportions of tree crown, shadow, and understory in the pixel. These, in turn, are a direct function of the number and size of trees, the solar illumination angle, and the spectral signatures of crown, shadow and understory. Given the variance in reflectance from pixel to pixel within a homogeneous area of woodland, caused by the variation in the number and size of trees, the model can be inverted to give estimates of average tree size and density. Because the inversion is sensitive to correct determination of component signatures, predictions are not accurate for small areas

A Spatially Explicit Census Reveals Population Structure and Recruitment Patterns for a Narrowly Endemic Pine, Pinus torreyana

We conducted a census of the rare pine, Pinus torreyana ssp.  torreyana, in order to determine: a) what is the population size and is it stable, growing or declining; b) what is the spatial variation in population structure; c) what is the spatial patterning of trees in different life stages; and, d) what environmental factors are related to seedling recruitment?  Trees were classified into four stages classes: adult (160 cm tall with cones); sub-adult (160 cm without cones); saplings (30-160 cm), and seedlings (30 cm).  Stem diameter was measured for adults and sub-adults, and height for saplings and seedlings.  Stands were defined by spatial clustering of the tree map.  Univariate and bivariate point pattern analyses were used to explore spatial patterns for adult and juvenile trees and identify potential stand development processes such as density dependence, dispersal limitations, and patchy recruitment.  Logistic regression was used to analyze seedling establishment and survival in relation to environmental variables derived from digital maps.  We expected to find little or no recruitment based on earlier studies.  Instead, 5422 trees were mapped and measured, and tree size had “reverse J-shaped†distribution suggestive of a recruiting population.  However, population structure was variable among stands.  The predominant spatial pattern detected for adult and juvenile trees was clustering at lag distances 10 m.  Bivariate pattern analysis did not suggest repulsion between adult and juvenile size classes.  Seedlings tended to be found close to adults and on certain soil types.  Taken together, this suggests that the clustered patterns resulting from patchy recruitment and survival of juveniles persist over time.

Variation in Spatial Predictions Among Species Distribution Modeling Methods

<p>Prediction maps produced by species distribution models (SDMs) influence decision-making in resource management or designation of land in conservation planning. Many studies have compared the prediction accuracy of different SDM modeling methods, but few have quantified the similarity among prediction maps. There has also been little systematic exploration of how the relative importance of different predictor variables varies among model types. Our objective was to expand the evaluation of SDM performance for 45 plant species in southern California to better understand how map predictions vary among model types, and to explain what factors may affect spatial correspondence, including the selection and relative importance of different environmental variables. Four types of models were tested. Correlation among maps was highest between generalized linear models (GLMs) and generalized additive models (GAMs) and lowest between classification trees and GAMs or GLMs. Correlation between Random Forests (RFs) and GAMs was the same as between RFs and classification trees. Spatial correspondence among maps was influenced the most by model prediction accuracy (AUC) and species prevalence; map correspondence was highest when accuracy was high and prevalence was intermediate. Species functional type and the selection of climate variables also influenced map correspondence. For most (but not all) species, climate variables were more important than terrain or soil in predicting their distributions. Environmental variable selection varied according to modeling method, but the largest differences were between RFs and GLMs or GAMs. Although prediction accuracy was equal for GLMs, GAMs, and RFs, the differences in spatial predictions suggest that it may be important to evaluate the results of more than one model to estimate a range of spatial uncertainty before making planning decisions based on map outputs. This may be particularly important if models have low accuracy or if species prevalence is not intermediate.</p>

Canopy reflectance modelling of semiarid vegetation

Three different types of remote sensing algorithms for estimating vegetation amount and other land surface biophysical parameters were tested for semiarid environments. These included statistical linear models, the Li-Strahler geometric-optical canopy model, and linear spectral mixture analysis. The two study areas were the National Science Foundation's Jornada Long Term Ecological Research site near Las Cruces, NM, in the northern Chihuahuan desert, and the HAPEX-Sahel site near Niamey, Niger, in West Africa, comprising semiarid rangeland and subtropical crop land. The statistical approach (simple and multiple regression) resulted in high correlations between SPOT satellite spectral reflectance and shrub and grass cover, although these correlations varied with the spatial scale of aggregation of the measurements. The Li-Strahler model produced estimated of shrub size and density for both study sites with large standard errors. In the Jornada, the estimates were accurate enough to be useful for characterizing structural differences among three shrub strata. In Niger, the range of shrub cover and size in short-fallow shrublands is so low that the necessity of spatially distributed estimation of shrub size and density is questionable. Spectral mixture analysis of multiscale, multitemporal, multispectral radiometer data and imagery for Niger showed a positive relationship between fractions of spectral endmembers and surface parameters of interest including soil cover, vegetation cover, and leaf area index

Winter bird communities in pine woodland vs. broadleaf forest on Abaco

We assessed the relative abundance of winter resident birds (species that breed in North America but spend the winter in The Bahamas) through 115 point counts conducted during December and January 2007–2012 on Abaco, The Bahamas. We also analyzed structure and composition of the woody vegetation in Abaco’s two widespread terrestrial habitats: Pineland, an open woodland of Pinus caribaea var. bahamensis (Bahamas Pine) that covers most of the island; and Coppice, a broadleaf, relatively diverse forest that covers &lt;10% of Abaco. Winter resident birds were more abundant in Pineland (8.61 individuals/point) than in Coppice (2.28 individuals/point). Setophaga palmarum (Palm Warbler) and S. coronata (Yellow-rumped Warbler) dominated the winter bird communities in Pineland (44.6% and 37.4% of all detected individuals, respectively), with the next three most common species (S. discolor [Prairie Warbler], Dumetella carolinensis [Gray Catbird], and Geothlypis trichas [Common Yellowthroat]) ranging from 6.5 to 2.0%. The winter species composition in Coppice was more even than in Pineland, featuring S. ruticilla [American Redstart] (39.8%), Prairie Warbler (16.2%), Gray Catbird (13.1%), and the next five most common species (S. americana [Northern Parula], Seiurus aurocapillus [Ovenbird], S. caerulescens [Black-throated Blue Warbler], Mnio- tilta varia [Black-and-white Warbler], and S. citrina [Hooded Warbler]) ranging from 7.3 to 3.1%. In Pineland, bird community composition varied with overstory cover and height, understory cover and type, and ground cover. In contrast, patterns of bird community composition in Coppice were not associated strongly with variation in habitat structure or forest composition. This may reflect that Coppice, experiencing frequent hurricane disturbance, has considerable small-scale heterogeneity in the availability of food for winter resident birds. Comparing our data with those from previous surveys of winter birds conducted 5–40 years earlier in the northern Bahamas, we see little evidence of major changes in this bird community