Environmental heterogeneity as a universal driver of species richness across taxa,biomes and spatial scales

Environmental heterogeneity is regarded as one of the most important factors governing species richness gradients. An increase in available niche space, provision of refuges and opportunities for isolation and divergent adaptation are thought to enhance species coexistence, persistence and diversification. However, the extent and generality of positive heterogeneity–richness relationships are still debated. Apart from widespread evidence supporting positive relationships, negative and hump‐shaped relationships have also been reported. In a meta‐analysis of 1148 data points from 192 studies worldwide, we examine the strength and direction of the relationship between spatial environmental heterogeneity and species richness of terrestrial plants and animals. We find that separate effects of heterogeneity in land cover, vegetation, climate, soil and topography are significantly positive, with vegetation and topographic heterogeneity showing particularly strong associations with species richness. The use of equal‐area study units, spatial grain and spatial extent emerge as key factors influencing the strength of heterogeneity–richness relationships, highlighting the pervasive influence of spatial scale in heterogeneity–richness studies. We provide the first quantitative support for the generality of positive heterogeneity–richness relationships across heterogeneity components, habitat types, taxa and spatial scales from landscape to global extents, and identify specific needs for future comparative heterogeneity–richness research.     

Guadalupe Island: Lost paradise recovered? Overgrazing impact on extinction in a remote oceanic island as estimated through accumulation functions

Guadalupe Island, an oceanic island in the northwest of Mexico, is an outlier of the California Floristic Province that has been disturbed by introduced goats for more than a century, with dramatic effects of goats on plant communities and local species extinctions. In 2004 the island went through a successful eradication program. Since then, six previously unrecorded species have been discovered and four supposed extinct species have been found again. Quantifying the true species richness of the island at the time of eradication, to set a benchmark for the future monitoring of this large-scale natural experiment, is both a challenge and a necessity. For this purpose, we estimated (a) current and (b) accumulated historical plant species richness of the island through accumulation functions. Estimation of current species richness was based on the geographical accumulation process of species richness (80 species) obtained from sampling 110 (50 m × 2 m) transects distributed along the island in year 2004. Historical species richness was estimated through the temporal accumulation of species richness (119 species) from botanical records (1,960 specimens reviewed) between 1875 and 2000. The predicted value of historical richness (213 species) is similar to known historical records (218 species), but estimation of current richness (203 species) is significantly higher than accepted extant plant richness (187 species). Our results suggest that currently there may be more plant species living in the island than estimated through recent botanical exploration. Future monitoring of the island as it recovers will clarify this hypothesis.     

Experimental evaluation of a new ground-based survey method for estimating the density and abundance of nesting Adélie penguins <Emphasis Type=Italic>Pygoscelis adeliae</Emphasis>

A new ground-based technique for estimating the density of nesting Adélie penguins (Pygoscelis adeliae) within occupied habitat is described and evaluated using an experimental approach with model penguins. In this set-up, an operator takes photos with a camera mounted on a 3-m pole at pre-determined sampling locations within habitat occupied by nesting penguins, and the actual boundaries of the plot in which penguin density is estimated are added post-survey. Density estimates are calculated by overlaying slope-specific templates delineating plot boundaries and distances of known dimensions onto the digital photos to allow a distance-sampling analysis to correct for any negative bias in counts arising from a proportion of penguins being obscured. Experimental assessment of biases in density estimates because of misclassification error was <2% when compared to true density, indicating that this survey method is effective at accurately estimating penguin density and, therefore, abundance.     

Motor unit number estimation--a Bayesian approach

All muscle contractions are dependent on the functioning of motor units. In diseases such as amyotrophic lateral sclerosis (ALS), progressive loss of motor units leads to gradual paralysis. A major difficulty in the search for a treatment for these diseases has been the lack of a reliable measure of disease progression. One possible measure would be an estimate of the number of surviving motor units. Despite over 30 years of motor unit number estimation (MUNE), all proposed methods have been met with practical and theoretical objections. Our aim is to develop a method of MUNE that overcomes these objections. We record the compound muscle action potential (CMAP) from a selected muscle in response to a graded electrical stimulation applied to the nerve. As the stimulus increases, the threshold of each motor unit is exceeded, and the size of the CMAP increases until a maximum response is obtained. However, the threshold potential required to excite an axon is not a precise value but fluctuates over a small range leading to probabilistic activation of motor units in response to a given stimulus. When the threshold ranges of motor units overlap, there may be alternation where the number of motor units that fire in response to the stimulus is variable. This means that increments in the value of the CMAP correspond to the firing of different combinations of motor units. At a fixed stimulus, variability in the CMAP, measured as variance, can be used to conduct MUNE using the "statistical" or the "Poisson" method. However, this method relies on the assumptions that the numbers of motor units that are firing probabilistically have the Poisson distribution and that all single motor unit action potentials (MUAP) have a fixed and identical size. These assumptions are not necessarily correct. We propose to develop a Bayesian statistical methodology to analyze electrophysiological data to provide an estimate of motor unit numbers. Our method of MUNE incorporates the variability of the threshold, the variability between and within single MUAPs, and baseline variability. Our model not only gives the most probable number of motor units but also provides information about both the population of units and individual units. We use Markov chain Monte Carlo to obtain information about the characteristics of individual motor units and about the population of motor units and the Bayesian information criterion for MUNE. We test our method of MUNE on three subjects. Our method provides a reproducible estimate for a patient with stable but severe ALS. In a serial study, we demonstrate a decline in the number of motor unit numbers with a patient with rapidly advancing disease. Finally, with our last patient, we show that our method has the capacity to estimate a larger number of motor units.     

Estimating density of Kirk’s dik‐dik (Madoqua kirkii Günther), impala (Aepyceros melampus Lichtenstein) and common zebra (Equus burchelli Gray) at Mpala,Laikipia District,Kenya

This study compares two methods of estimating the density of three ungulate species: Kirk’s dik‐dik, impala and common zebra, in a dry savannah ecosystem. Fixed strip width and distance sampling involving direct animal counts were used in parallel, and tested for bias and precision in two habitats. Distance sampling was the method that achieved a better balance between accuracy and precision. The differences in sightability of ungulates, according to body size, colour and habitat characteristics, are taken into account by the distance sampling method, but not by the strip method, which produced, what we suspect are, underestimates of animal density.     

On the design of closed recapture experiments

We propose a method to plan the number of occasions of recapture experiments for population size estimation. We do so by fixing the smallest number of capture occasions so that the expected length of the profile confidence interval is less than or equal to a fixed threshold. In some cases, we solve the optimization problem in closed form. For more complex models we use numerical optimization. We detail models assuming homogeneous, time‐varying, subject‐specific capture probabilities, behavioral response to capture, and combining behavioral response with subject‐specific effects. The principle we propose can be extended to plan any other model specification. We formally show the validity of the approach by proving distributional convergence. We illustrate with simulations and challenging examples in epidemiology and ecology. We report that in many cases adding as few as two sampling occasions may substantially reduce the length of confidence intervals.