Slopes of Avian Species-Area Relationships, Human Population Density, and Environmental Factors

There is increasing interest in how humans influence spatial patterns in biodiversity. One of the most frequently noted and marked of these patterns is the increase in species richness with area, the species–area relationship (SAR). SARs are used for a number of conservation purposes, including predicting extinction rates, setting conservation targets, and identifying biodiversity hotspots. Such applications can be improved by a detailed understanding of the factors promoting spatial variation in the slope of SARs, which is currently the subject of a vigorous debate. Moreover, very few studies have considered the anthropogenic influences on the slopes of SARs; this is particularly surprising given that in much of the world areas with high human population density are typically those with a high number of species, which generates conservation conflicts. Here we determine correlates of spatial variation in the slopes of species–area relationships, using the British avifauna as a case study. Whilst we focus on human population density, a widely used index of human activities, we also take into account (1) the rate of increase in habitat heterogeneity with increasing area, which is frequently proposed to drive SARs, (2) environmental energy availability, which may influence SARs by affecting species occupancy patterns, and (3) species richness. We consider environmental variables measured at both local (10 km × 10 km) and regional (290 km × 290 km) spatial grains, but find that the former consistently provides a better fit to the data. In our case study, the effect of species richness on the slope SARs appears to be scale dependent, being negative at local scales but positive at regional scales. In univariate tests, the slope of the SAR correlates negatively with human population density and environmental energy availability, and positively with the rate of increase in habitat heterogeneity. We conducted two sets of multiple regression analyses, with and without species richness as a predictor. When species richness is included it exerts a dominant effect, but when it is excluded temperature has the dominant effect on the slope of the SAR, and the effects of other predictors are marginal.

A Field Evaluation of the Time-of-Detection Method to Estimate Population Size and Density for Aural Avian Point Counts

The time-of-detection method for aural avian point counts is a new method of estimating abundance, allowing for uncertain probability of detection. The method has been specifically designed to allow for variation in singing rates of birds. It involves dividing the time interval of the point count into several subintervals and recording the detection history of the subintervals when each bird sings. The method can be viewed as generating data equivalent to closed capture–recapture information. The method is different from the distance and multiple-observer methods in that it is not required that all the birds sing during the point count. As this method is new and there is some concern as to how well individual birds can be followed, we carried out a field test of the method using simulated known populations of singing birds, using a laptop computer to send signals to audio stations distributed around a point. The system mimics actual aural avian point counts, but also allows us to know the size and spatial distribution of the populations we are sampling. Fifty 8-min point counts (broken into four 2-min intervals) using eight species of birds were simulated. Singing rate of an individual bird of a species was simulated following a Markovian process (singing bouts followed by periods of silence), which we felt was more realistic than a truly random process. The main emphasis of our paper is to compare results from species singing at (high and low) homogenous rates per interval with those singing at (high and low) heterogeneous rates. Population size was estimated accurately for the species simulated, with a high homogeneous probability of singing. Populations of simulated species with lower but homogeneous singing probabilities were somewhat underestimated. Populations of species simulated with heterogeneous singing probabilities were substantially underestimated. Underestimation was caused by both the very low detection probabilities of all distant individuals and by individuals with low singing rates also having very low detection probabilities.

Density dependence and phenological mismatch: consequences for growth and survival of sub-arctic nesting Canada Geese

The extent to which species are plastic in the timing of their reproductive events relative to phenology suggests how climate change might affect their demography. An ecological mismatch between the timing of hatch for avian species and the peak availability in quality and quantity of forage for rapidly growing offspring might ultimately affect recruitment to the breeding population unless individuals can adjust the timing of breeding to adapt to changing phenology. We evaluated effects of goose density, hatch timing relative to forage plant phenology, and weather indices on annual growth of pre-fledging Canada geese (Branta canadensis) from 1993-2010 at Akimiski Island, Nunavut. We found effects of both density and hatch timing relative to forage plant phenology; the earlier that eggs hatched relative to forage plant phenology, the larger the mean gosling size near fledging. Goslings were smallest in years when hatch was latest relative to forage plant phenology, and when local abundance of breeding adults was highest. We found no evidence for a trend in relative hatch timing, but it was apparent that in early springs, Canada geese tended to hatch later relative to vegetation phenology, suggesting that geese were not always able to adjust the timing of nesting as rapidly as vegetation phenology was advanced. Analyses using forage biomass information revealed a positive relationship between gosling size and per capita biomass availability, suggesting a causal mechanism for the density effect. The effects of weather parameters explained additional variation in mean annual gosling size, although total June and July rainfall had a small additive effect on gosling size. Modelling of annual first-year survival probability using mean annual gosling size as an annual covariate revealed a positive relationship, suggesting that reduced gosling growth negatively impacts recruitment.