Geology topics

Thomas C. Edwards

Publications and source records attributed to Thomas C. Edwards.

At least 19 recordsLinked to original sources

USGS2022

Connecting regional-scale tree distribution models with seed dispersal kernels

Regional scale forest distribution models are important tools for biogeography and understanding the structure of forest communities in space. These models take climate and geographic variables as input and are therefore helpful for long-term decision support and climate adaptation planning. Generally, local processes of tree germination and seedling survival are resolved probabilistically with explanatory variables such as elevation, latitude, exposure, soil type, moisture availability, climate and weather inputs and `trained’ using landscape and regional presence-absence data and machine learning techniques. How seeds are distributed in these models, that is, determining the dispersal kernel, is far more problematic. The challenge is that variables conditioning vertebrate seed dispersal (motility and probability of utilization or caching in response to cover type) are not represented in large scale distribution models, and in fact vary on scales (10-100 meters) that are much smaller than the smallest pixel size for the distribution model (1-10 kilometers). We present a homogenized seed digestion kernel (HSDK) which incorporates this scale separation. Homogenization naturally links highly variable small-scale processes (like seed foraging and caching by birds and rodents) with large scale effects (like dispersal of seeds over tens of kilometers). We develop a homogenization strategy to predict seed dispersal on landscape scales, analytically linking small-scale variables (landscape fraction cover by tree type, gut residence times and cover type utilization by frugivorous birds) with large scale behaviors. Closed form approximations are developed in two dimensions for two limiting cases of seed handling behavior, and the approach is illustrated using landscape data and piñon-pine dispersal in a 630,000 square kilometer region in the southwestern US.

Applied Mathematics and Computation↗

USGS2020

Lessons learned from the first worldwide accessible e-learning in Landscape Ecology

Massive open online courses (MOOCs) are distance learning tools for individualized learning. They allow students to learn at their own pace in a virtual classroom. We describe success and pitfalls of the MOOC Landscape Ecology, designed as an undergraduate University course taught by an international consortium of Professors covering theory and application of the field. The paper describes course performance with summary metrics, illustrates contents and didactic tools, and provides a list of suggestions for instructors who engage in distant learning. We identify the following five key success factors for this and related MOOCs: (1) commitment and passion of an international consortium of lecturers; (2) a sound mixture of theory and practice; (3) numerous field-videos; (4) content and skill-oriented practicums (here using R, GIS, remote sensing); and (5) interactive formats where students discuss and share their opinions. In all runs of our MOOC we experienced some difficulties with peer-assessed writing tasks due to widely differing “review cultures”. The instructor-paced MOOC attracted over 3500 students in 2018 and 2019, and had comparably high completion rates (14% and 11%, respectively), compared to typical MOOC completion rates ranging from 5% to 15%. Completion rates in our self-paced run in 2020 were 8-9% only.

Landscape Online↗

USGS2019

The development and delivery of species distribution models to inform decision-making

Information on where species occur is central to conservation and management decisions, but knowledge of distributions can be coarse or incomplete. Species distribution models provide a tool for mapping suitable habitat, and can produce credible, defensible, and repeatable predictive information with which to inform decisions. However, these models are sensitive to data inputs and methodological choices, making it important to assess the reliability and utility of model predictions. We provide a rubric that model developers can use to communicate a model’s attributes and its appropriate uses. We emphasize the importance of tailoring model development and delivery to the species of interest and the intended use, and the advantages of iterative modeling and validation. We highlight how species distribution models have been used to design surveys for new populations, prioritize actions across space, and support regulatory decision-making and compliance, tying these examples back to our rubric.

BioScience↗

USGS2018

A landscape-level assessment of whitebark pine regeneration in the Rocky Mountains, USA

Whitebark pine (Pinus albicaulis Engelm.) has recently experienced high mortality due to multiple stressors, and future population viability may rely on natural regeneration. We assessed whitebark pine seedling densities throughout the US Rocky Mountains and identified stand, site, and climatic variables related to seedling presence based on data from 1,217 USDA Forest Service Forest Inventory and Analysis plots. Although mean densities were highest in the whitebark pine forest type, 83% of sites with seedlings present occurred in non-whitebark pine forest types, and the highest densities occurred in the lodgepole pine forest type. To identify factors related to whitebark pine seedling presence, we compared the results generated from three statistical models: logistic regression, classification tree, and random forests. All three models identified cover of grouse whortleberry (Vaccinium scoparium Leiberg ex Coville) as an important predictor, two models distinguished live and dead whitebark pine basal area and elevation, and one model recognized seasonal temperature. None of the models identified forest type as an important predictor. Understanding these factors may help managers identify areas where natural regeneration of whitebark pine is likely to occur, including sites in non-whitebark pine forest types.

Rocky Mountains↗

USGS2018

Climatically driven changes in primary production propagate through trophic levels

Climate and land‐use change are the major drivers of global biodiversity loss. Their effects are particularly acute for wide‐ranging consumers, but little is known about how these factors interact to affect the abundance of large carnivores and their herbivore prey. We analyzed population densities of a primary and secondary consumer (mule deer, Odocoileus hemionus , and mountain lion, Puma concolor ) across a climatic gradient in western North America by combining satellite‐based maps of plant productivity with estimates of animal abundance and foraging area derived from Global Positioning Systems telemetry data (GPS). Mule deer density exhibited a positive, linear relationship with plant productivity ( r 2 = 0.58), varying by a factor of 18 across the climate‐vegetation gradient (range: 38–697 individuals/100 km 2 ). Mountain lion home range size decreased in response to increasing primary productivity and consequent changes in the abundance of their herbivore prey (range: 20–450 km 2 ). This pattern resulted in a strong, positive association between plant productivity and mountain lion density ( r 2 = 0.67). Despite varying densities, the ratio of prey to predator remained constant across the climatic gradient (mean ± SE = 363 ± 29 mule deer/mountain lion), suggesting that the determinacy of the effect of primary productivity on consumer density was conserved across trophic levels. As droughts and longer term climate changes reduce the suitability of marginal habitats, consumer home ranges will expand in order for individuals to meet basic nutritional requirements. These changes portend decreases in the abundance of large‐bodied, wide‐ranging wildlife through climatically driven reductions in carrying capacity, as well as increased human–wildlife interactions stemming from anthropogenic land use and habitat fragmentation.

Colorado Plateau, Great Basin, Mojave Desert↗

USGS2018

Geomorphometry in landscape ecology: Issues of scale, physiography, and application

Topographic measures are frequently used in a variety of landscape ecology applications, in their simplest form as elevation, slope, and aspect, but increasingly more complex measures are being employed. We explore terrain metric similarity with changes in scale, both grain and extent, and examine how selecting the best measures is sensitive to changes in application. There are three types of topographic measures: 1) those that relate to orientation for approximating solar input, 2) those that capture variability in terrain configuration, and 3) those that provide metrics about landform features. Many biodiversity hotspots and predators have been found to coincide with areas of complexity, yet most complexity measures cannot differentiate between terrain steepness and uneven and broken terrain. Currently characterizing terrain in landscape-level analyses can be challenging, especially at coarser spatial resolutions but developing methods that improve landscape-level assessments include multivariate approaches and the use of neighborhood statistics. Some measures are sensitive to the spatial grain of calculation, the physiography of the landscape, and the scale of application. We show which measures have the potential to be multi-collinear, and illustrate with a case study how the selection of the best measures can change depending on the question at hand using mountain lion (Puma concolor) occurrence data. The case study showed a combination of infrequently employed metrics, such as view-shed analysis and focal statistics, outperform more commonly employed singular metrics. The use of focal statistics as a measure of topographic complexity shows promise for improving how mountain lions use terrain features.

Environment and Ecology Research↗

USGS2017

Quantifying animal movement for caching foragers: the path identification index (PII) and cougars, Puma concolor

Relocation studies of animal movement have focused on directed versus area restricted movement, which rely on correlations between step-length and turn angles, along with a degree of stationarity through time to define behavioral states. Although these approaches may work well for grazing foraging strategies in a patchy landscape, species that do not spend a significant amount of time searching out and gathering small dispersed food items, but instead feed for short periods on large, concentrated sources or cache food result in movements that maybe difficult to analyze using turning and velocity alone. We use GPS telemetry collected from a prey-caching predator, the cougar (Puma concolor), to test whether adding additional movement metrics capturing site recursion, to the more traditional velocity and turning, improve the ability to identify behaviors. We evaluated our movement index’s ability to identify behaviors using field investigations. We further tested for statistical stationarity across behaviors for use of topographic view-sheds. We found little correlation between turn angle, velocity, tortuosity, and site fidelity and combined them into a movement index used to identify movement paths (temporally autocorrelated movements) related to fast directed movements (taxis), area restricted movements (search), and prey caching (foraging). Changes in the frequency and duration of these movements were helpful for identifying seasonal activities such as migration and denning in females. Comparison of field investigations of cougar activities to behavioral classes defined using the movement index and found an overall classification accuracy of 81%. Changes in behaviors resulted in changes in how cougars used topographic view-sheds, showing statistical non-stationarity over time. The movement index shows promise for identifying behaviors in species that frequently return to specific locations such as food caches, watering holes, or dens, and highlights the role memory and cognitive abilities may play in determining animal movements. With the addition of measures capturing site recursion the temporal structure in movements of a caching forager was revealed.

Movement Ecology↗

USGS2017

Variable terrestrial GPS telemetry detection rates: Addressing the probability of successful acquisitions

Studies using global positioning system (GPS) telemetry rarely result in 100% fix success rates (FSR), which may bias datasets because data loss is systematic rather than a random process. Previous spatially explicit models developed to correct for sampling bias have been limited to small study areas, a small range of data loss, or were study-area specific. We modeled environmental effects on FSR from desert to alpine biomes, investigated the full range of potential data loss (0–100% FSR), and evaluated whether animal body position can contribute to lower FSR because of changes in antenna orientation based on GPS detection rates for 4 focal species: cougars ( Puma concolor ), desert bighorn sheep ( Ovis canadensis nelsoni ), Rocky Mountain elk ( Cervus elaphus nelsoni ), and mule deer ( Odocoileus hemionus ). Terrain exposure and height of over story vegetation were the most influential factors affecting FSR. Model evaluation showed a strong correlation (0.88) between observed and predicted FSR and no significant differences between predicted and observed FSRs using 2 independent validation datasets. We found that cougars and canyon-dwelling bighorn sheep may select for environmental features that influence their detectability by GPS technology, mule deer may select against these features, and elk appear to be nonselective. We observed temporal patterns in missed fixes only for cougars. We provide a model for cougars, predicting fix success by time of day that is likely due to circadian changes in collar orientation and selection of daybed sites. We also provide a model predicting the probability of GPS fix acquisitions given environmental conditions, which had a strong relationship ( r 2 = 0.82) with deployed collar FSRs across species.

Wildlife Society Bulletin↗

USGS2016

Ungulate reproductive parameters track satellite observations of plant phenology across latitude and climatological regimes

The effect of climatically-driven plant phenology on mammalian reproduction is one key to predicting species-specific demographic responses to climate change. Large ungulates face their greatest energetic demands from the later stages of pregnancy through weaning, and so in seasonal environments parturition dates should match periods of high primary productivity. Interannual variation in weather influences the quality and timing of forage availability, which can influence neonatal survival. Here, we evaluated macro-scale patterns in reproductive performance of a widely distributed ungulate (mule deer, Odocoileus hemionus ) across contrasting climatological regimes using satellite-derived indices of primary productivity and plant phenology over eight degrees of latitude (890 km) in the American Southwest. The dataset comprised > 180,000 animal observations taken from 54 populations over eight years (2004–2011). Regionally, both the start and peak of growing season (“Start” and “Peak”, respectively) are negatively and significantly correlated with latitude, an unusual pattern stemming from a change in the dominance of spring snowmelt in the north to the influence of the North American Monsoon in the south. Corresponding to the timing and variation in both the Start and Peak, mule deer reproduction was latest, lowest, and most variable at lower latitudes where plant phenology is timed to the onset of monsoonal moisture. Parturition dates closely tracked the growing season across space, lagging behind the Start and preceding the Peak by 27 and 23 days, respectively. Mean juvenile production increased, and variation decreased, with increasing latitude. Temporally, juvenile production was best predicted by primary productivity during summer, which encompassed late pregnancy, parturition, and early lactation. Our findings offer a parsimonious explanation of two key reproductive parameters in ungulate demography, timing of parturition and mean annual production, across latitude and changing climatological regimes. Practically, this demonstrates the potential for broad-scale modeling of couplings between climate, plant phenology, and animal populations using space-borne observations.

Arizona, Colorado, Utah↗

USGS2015

Legacy effects of no-analogue disturbances alter plant community diversity and composition in semi-arid sagebrush steppe

Questions (i) What role does the type of managed disturbance play in structuring sagebrush steppe plant communities? (ii) How does the composition of post-disturbance plant communities change with time since disturbance? (iii) Does plant community diversity change over time following managed disturbance? Location Field study within the sagebrush steppe ecosystem. Rich County, Utah, USA. Methods We developed a chronosequence spanning up to 50 yrs post-treatment to study sagebrush steppe vegetation dynamics. Direct ordination was used to examine plant community composition by managed disturbance type and time since disturbance, and factorial analysis of covariance was used to examine diversity dynamics following disturbance. Indicator species values were calculated in order to identify characteristic species for each disturbance type. Results Plant communities experienced a shift toward distinct community composition for each of the three managed disturbance types, and gave no indication of returning to untreated community composition or diversity. Small post-disturbance increases in the number of non-native grass species were observed in the treatments relative to reference, with native forb species making the largest contribution to altered composition. On fire- and chemically-treated sites the proportional native forb species richness increased over time since disturbance, while the proportional contribution of non-native forbs to total species richness decreased. For all three treatment types, native grasses contributed less on average to total richness than on reference sites, while non-native grasses made up a higher proportion of total richness. Conclusions Common shrubland management techniques have legacy effects on the composition and diversity of sagebrush steppe plant communities, and no-analogue disturbances, such as chemical or mechanical treatments, have more pronounced legacy effects than treatments similar to natural disturbance regimes (fire). This study informs a broader understanding of how management actions affect natural systems by highlighting the importance of long-term management legacies as drivers of plant community structure and function.

Utah↗

USGS2015

Machine learning for predicting soil classes in three semi-arid landscapes

Mapping the spatial distribution of soil taxonomic classes is important for informing soil use and management decisions. Digital soil mapping (DSM) can quantitatively predict the spatial distribution of soil taxonomic classes. Key components of DSM are the method and the set of environmental covariates used to predict soil classes. Machine learning is a general term for a broad set of statistical modeling techniques. Many different machine learning models have been applied in the literature and there are different approaches for selecting covariates for DSM. However, there is little guidance as to which, if any, machine learning model and covariate set might be optimal for predicting soil classes across different landscapes. Our objective was to compare multiple machine learning models and covariate sets for predicting soil taxonomic classes at three geographically distinct areas in the semi-arid western United States of America (southern New Mexico, southwestern Utah, and northeastern Wyoming). All three areas were the focus of digital soil mapping studies. Sampling sites at each study area were selected using conditioned Latin hypercube sampling (cLHS). We compared models that had been used in other DSM studies, including clustering algorithms, discriminant analysis, multinomial logistic regression, neural networks, tree based methods, and support vector machine classifiers. Tested machine learning models were divided into three groups based on model complexity: simple, moderate, and complex. We also compared environmental covariates derived from digital elevation models and Landsat imagery that were divided into three different sets: 1) covariates selected a priori by soil scientists familiar with each area and used as input into cLHS, 2) the covariates in set 1 plus 113 additional covariates, and 3) covariates selected using recursive feature elimination. Overall, complex models were consistently more accurate than simple or moderately complex models. Random forests (RF) using covariates selected via recursive feature elimination was consistently the most accurate, or was among the most accurate, classifiers between study areas and between covariate sets within each study area. We recommend that for soil taxonomic class prediction, complex models and covariates selected by recursive feature elimination be used. Overall classification accuracy in each study area was largely dependent upon the number of soil taxonomic classes and the frequency distribution of pedon observations between taxonomic classes. Individual subgroup class accuracy was generally dependent upon the number of soil pedon observations in each taxonomic class. The number of soil classes is related to the inherent variability of a given area. The imbalance of soil pedon observations between classes is likely related to cLHS. Imbalanced frequency distributions of soil pedon observations between classes must be addressed to improve model accuracy. Solutions include increasing the number of soil pedon observations in classes with few observations or decreasing the number of classes. Spatial predictions using the most accurate models generally agree with expected soil–landscape relationships. Spatial prediction uncertainty was lowest in areas of relatively low relief for each study area.

New Mexico, Utah, Wyoming↗

USGS2014

Does probability of occurrence relate to population dynamics?

Hutchinson defined species' realized niche as the set of environmental conditions in which populations can persist in the presence of competitors. In terms of demography, the realized niche corresponds to the environments where the intrinsic growth rate ( r ) of populations is positive. Observed species occurrences should reflect the realized niche when additional processes like dispersal and local extinction lags do not have overwhelming effects. Despite the foundational nature of these ideas, quantitative assessments of the relationship between range-wide demographic performance and occurrence probability have not been made. This assessment is needed both to improve our conceptual understanding of species' niches and ranges and to develop reliable mechanistic models of species geographic distributions that incorporate demography and species interactions. The objective of this study is to analyse how demographic parameters (intrinsic growth rate r and carrying capacity K ) and population density ( N ) relate to occurrence probability ( P occ ). We hypothesized that these relationships vary with species' competitive ability. Demographic parameters, density, and occurrence probability were estimated for 108 tree species from four temperate forest inventory surveys (Québec, western USA, France and Switzerland). We used published information of shade tolerance as indicators of light competition strategy, assuming that high tolerance denotes high competitive capacity in stable forest environments. Interestingly, relationships between demographic parameters and occurrence probability did not vary substantially across degrees of shade tolerance and regions. Although they were influenced by the uncertainty in the estimation of the demographic parameters, we found that r was generally negatively correlated with P occ , while N, and for most regions K, was generally positively correlated with P occ . Thus, in temperate forest trees the regions of highest occurrence probability are those with high densities but slow intrinsic population growth rates. The uncertain relationships between demography and occurrence probability suggests caution when linking species distribution and demographic models.

Ecography↗

USGS2014

What do we gain from simplicity versus complexity in species distribution models?

Species distribution models (SDMs) are widely used to explain and predict species ranges and environmental niches. They are most commonly constructed by inferring species' occurrence–environment relationships using statistical and machine-learning methods. The variety of methods that can be used to construct SDMs (e.g. generalized linear/additive models, tree-based models, maximum entropy, etc.), and the variety of ways that such models can be implemented, permits substantial flexibility in SDM complexity. Building models with an appropriate amount of complexity for the study objectives is critical for robust inference. We characterize complexity as the shape of the inferred occurrence–environment relationships and the number of parameters used to describe them, and search for insights into whether additional complexity is informative or superfluous. By building ‘under fit’ models, having insufficient flexibility to describe observed occurrence–environment relationships, we risk misunderstanding the factors shaping species distributions. By building ‘over fit’ models, with excessive flexibility, we risk inadvertently ascribing pattern to noise or building opaque models. However, model selection can be challenging, especially when comparing models constructed under different modeling approaches. Here we argue for a more pragmatic approach: researchers should constrain the complexity of their models based on study objective, attributes of the data, and an understanding of how these interact with the underlying biological processes. We discuss guidelines for balancing under fitting with over fitting and consequently how complexity affects decisions made during model building. Although some generalities are possible, our discussion reflects differences in opinions that favor simpler versus more complex models. We conclude that combining insights from both simple and complex SDM building approaches best advances our knowledge of current and future species ranges.

Ecography↗

USGS2014

The influence of interspecific interactions on species range expansion rates

Ongoing and predicted global change makes understanding and predicting species’ range shifts an urgent scientific priority. Here, we provide a synthetic perspective on the so far poorly understood effects of interspecific interactions on range expansion rates. We present theoretical foundations for how interspecific interactions may modulate range expansion rates, consider examples from empirical studies of biological invasions and natural range expansions as well as process-based simulations, and discuss how interspecific interactions can be more broadly represented in process-based, spatiotemporally explicit range forecasts. Theory tells us that interspecific interactions affect expansion rates via alteration of local population growth rates and spatial displacement rates, but also via effects on other demographic parameters. The best empirical evidence for interspecific effects on expansion rates comes from studies of biological invasions. Notably, invasion studies indicate that competitive dominance and release from specialized enemies can enhance expansion rates. Studies of natural range expansions especially point to the potential for competition from resident species to reduce expansion rates. Overall, it is clear that interspecific interactions may have important consequences for range dynamics, but also that their effects have received too little attention to robustly generalize on their importance. We then discuss how interspecific interactions effects can be more widely incorporated in dynamic modeling of range expansions. Importantly, models must describe spatiotemporal variation in both local population dynamics and dispersal. Finally, we derive the following guidelines for when it is particularly important to explicitly represent interspecific interactions in dynamic range expansion forecasts: if most interacting species show correlated spatial or temporal trends in their effects on the target species, if the number of interacting species is low, and if the abundance of one or more strongly interacting species is not closely linked to the abundance of the target species.

Ecography↗

USGS2013

Rodent-Mediated Interactions Among Seed Species of Differing Quality in a Shrubsteppe Ecosystem

Interactions among seeds, mediated by granivorous rodents, are likely to play a strong role in shrubsteppe ecosystem restoration. Past studies typically consider only pairwise interactions between preferred and less preferred seed species, whereas rangeland seedings are likely to contain more than 2 seed species, potentially leading to complex interactions. We examined how the relative proportion of seeds in a 3-species polyculture changes rodent seed selectivity (i.e., removal) and indirect interactions among seeds. We presented 2 rodent species, Peromyscus maniculatus (deer mice) and Perognathus parvus (pocket mice), in arenas with 3-species seed mixtures that varied in the proportion of a highly preferred, moderately preferred, and least preferred seed species, based on preferences determined in this study. We then conducted a field experiment in a pocket mouse—dominated ecosystem with the same 3-species seed mixtures in both “treated” (reduced shrub and increased forb cover) and “untreated” shrubsteppe. In the arena experiment, we found that rodents removed more of the highly preferred seed when the proportions of all 3 seeds were equal. Moderately preferred seeds experienced increased removal when the least preferred seed was in highest proportion. Removal of the least preferred seed increased when the highly preferred seed was in highest proportion. In the field experiment, results were similar to those from the arena experiment and did not differ between treated and untreated shrubsteppe areas. Though our results suggest that 3-species mixtures induce complex interactions among seeds, managers applying these results to restoration efforts should carefully consider the rodent community present and the potential fate of removed seeds.

Utah↗

USGS2012

Spatial ecology of refuge selection by an herbivore under risk of predation

Prey species use structures such as burrows to minimize predation risk. The spatial arrangement of these resources can have important implications for individual and population fitness. For example, there is evidence that clustered resources can benefit individuals by reducing predation risk and increasing foraging opportunity concurrently, which leads to higher population density. However, the scale of clustering that is important in these processes has been ignored during theoretical and empirical development of resource models. Ecological understanding of refuge exploitation by prey can be improved by spatial analysis of refuge use and availability that incorporates the effect of scale. We measured the spatial distribution of pygmy rabbit (Brachylagus idahoensis) refugia (burrows) through censuses in four 6-ha sites. Point pattern analyses were used to evaluate burrow selection by comparing the spatial distribution of used and available burrows. The presence of food resources and additional overstory cover resources was further examined using logistic regression. Burrows were spatially clustered at scales up to approximately 25 m, and then regularly spaced at distances beyond ~40 m. Pygmy rabbit exploitation of burrows did not match availability. Burrows used by pygmy rabbits were likely to be located in areas with high overall burrow density (resource clusters) and high overstory cover, which together minimized predation risk. However, in some cases we observed an interaction between either overstory cover (safety) or understory cover (forage) and burrow density. The interactions show that pygmy rabbits will use burrows in areas with low relative burrow density (high relative predation risk) if understory food resources are high. This points to a potential trade-off whereby rabbits must sacrifice some safety afforded by additional nearby burrows to obtain ample forage resources. Observed patterns of clustered burrows and non-random burrow use improve understanding of the importance of spatial distribution of refugia for burrowing herbivores. The analyses used allowed for the estimation of the spatial scale where subtle trade-offs between predation avoidance and foraging opportunity are likely to occur in a natural system.

Ecosphere↗

USGS2010

Balancing forest-regeneration probabilities and maintenance costs in dry grasslands of high conservation priority

Abandonment of agricultural land has resulted in forest regeneration in species-rich dry grasslands across European mountain regions and threatens conservation efforts in this vegetation type. To support national conservation strategies, we used a site-selection algorithm (MARXAN) to find optimum sets of floristic regions (reporting units) that contain grasslands of high conservation priority. We sought optimum sets that would accommodate 136 important dry-grassland species and that would minimize forest regeneration and costs of management needed to forestall predicted forest regeneration. We did not consider other conservation elements of dry grasslands, such as animal species richness, cultural heritage, and changes due to climate change. Optimal sets that included 95–100% of the dry grassland species encompassed an average of 56–59 floristic regions (standard deviation, SD 5). This is about 15% of approximately 400 floristic regions that contain dry-grassland sites and translates to 4800–5300 ha of dry grassland out of a total of approximately 23,000 ha for the entire study area. Projected costs to manage the grasslands in these optimum sets ranged from CHF (Swiss francs) 5.2 to 6.0 million/year. This is only 15–20% of the current total estimated cost of approximately CHF30–45 million/year required if all dry grasslands were to be protected. The grasslands of the optimal sets may be viewed as core sites in a national conservation strategy.

Conservation Biology↗

USGS2010

New trends in species distribution modelling

Species distribution modelling has its origin in the late 1970s when computing capacity was limited. Early work in the field concentrated mostly on the development of methods to model effectively the shape of a species' response to environmental gradients (Austin 1987, Austin et al. 1990). The methodology and its framework were summarized in reviews 10–15 yr ago (Franklin 1995, Guisan and Zimmermann 2000), and these syntheses are still widely used as reference landmarks in the current distribution modelling literature. However, enormous advancements have occurred over the last decade, with hundreds – if not thousands – of publications on species distribution model (SDM) methodologies and their application to a broad set of conservation, ecological and evolutionary questions. With this special issue, originating from the third of a set of specialized SDM workshops (2008 Riederalp) entitled 'The Utility of Species Distribution Models as Tools for Conservation Ecology', we reflect on current trends and the progress achieved over the last decade.

Ecography↗