North American Breeding Bird Survey Underestimates Regional Bird Richness Compared to Breeding Bird Atlases

Standardized data on large-scale and long-term patterns of species richness are critical for understanding the consequences of natural and anthropogenic changes in the environment. The North American Breeding Bird Survey (BBS) is one of the largest and most widely used sources of such data, but so far, little is known about the degree to which BBS data provide accurate estimates of regional richness. Here, we test this question by comparing estimates of regional richness based on BBS data with spatially and temporally matched estimates based on state Breeding Bird Atlases (BBA). We expected that estimates based on BBA data would provide a more complete (and therefore, more accurate) representation of regional richness due to their larger number of observation units and higher sampling effort within the observation units. Our results were only partially consistent with these predictions: while estimates of regional richness based on BBA data were higher than those based on BBS data, estimates of local richness (number of species per observation unit) were higher in BBS data. The latter result is attributed to higher land-cover heterogeneity in BBS units and higher effectiveness of bird detection (more species are detected per unit time). Interestingly, estimates of regional richness based on BBA blocks were higher than those based on BBS data even when differences in the number of observation units were controlled for. Our analysis indicates that this difference was due to higher compositional turnover between BBA units, probably due to larger differences in habitat conditions between BBA units and a higher likelihood of observing geographically restricted species. Our overall results indicate that estimates of regional richness based on BBS data suffer from incomplete detection of a large number of rare species, and that corrections of these estimates based on standard extrapolation techniques are not sufficient to remove this bias. Future applications of BBS data in ecology and conservation, and in particular, applications in which the representation of rare species is important (e.g., those focusing on biodiversity conservation), should be aware of this bias, and should integrate BBA data whenever possible

Lizards' foraging behavior indices

These file contain already published lizards' foraging behavior indices (MPM and PTM) and additional information about the duration of the original observation as reported by the authors. We corrected the MPM values to reduce the inherent bias and report how the new calculated MPM' values deviate from the reported ones

Temporal fluctuation scaling in populations and communities

Taylor’s law, one of the most widely accepted generalizations in ecology, states that the variance of a population abundance time series scales as a power law of its mean. Here we reexamine this law and the empirical evidence presented in support of it. Specifically, we show that the exponent generally depends on the length of the time series, and its value reflects the combined effect of many underlying mechanisms. Moreover, sampling errors alone, when presented on a double logarithmic scale, are sufficient to produce an apparent power law. This raises questions regarding the usefulness of Taylor’s law for understanding ecological processes. As an alternative approach, we focus on short-term fluctuations and derive a generic null model for the variance-to-mean ratio in population time series from a demographic model that incorporates the combined effects of demographic and environmental stochasticity. After comparing the predictions of the proposed null model with the fluctuations observed in empirical data sets, we suggest an alternative expression for fluctuation scaling in population time series. Analyzing population fluctuations as we have proposed here may provide new applied (e.g., estimation of species persistence times) and theoretical (e.g., the neutral theory of biodiversity) insights that can be derived from more generally available short-term monitoring data

Putative Kappa Opioid Heteromers As Targets for Developing Analgesics Free of Adverse Effects

It is now generally recognized that upon activation by an agonist, β-arrestin associates with G protein-coupled receptors and acts as a scaffold in creating a diverse signaling network that could lead to adverse effects. As an approach to reducing side effects associated with κ opioid agonists, a series of β-naltrexamides <b>3</b>–<b>10</b> was synthesized in an effort to selectively target putative κ opioid heteromers without recruiting β-arrestin upon activation. The most potent derivative <b>3</b> (INTA) strongly activated KOR-DOR and KOR-MOR heteromers in HEK293 cells. In vivo studies revealed <b>3</b> to produce potent antinociception, which, when taken together with antagonism data, was consistent with the activation of both heteromers. <b>3</b> was devoid of tolerance, dependence, and showed no aversive effect in the conditioned place preference assay. As immunofluorescence studies indicated no recruitment of β-arrestin2 to membranes in coexpressed KOR-DOR cells, this study suggests that targeting of specific putative heteromers has the potential to identify leads for analgesics devoid of adverse effects

Supplement 1. Fortran code simulating a neutral community with demographic noise, used to generate Fig. 5.

<h2>File List</h2><div> <p><a href="Neutral_fortran.txt">Neutral_fortran.txt</a> (MD5: 7d5175db22c1bf3d1724b608611568de)</p> </div><h2>Description</h2><div> <p>Neutral_Fortran is a program that simulates neutral dynamics (the selection strength is alpha, so alpha = 0 is neutral) for a panmictic population of n individuals. The initial conditions are 50 species, mutation rate is dmu. The output is the file st1.dat, which is a list of abundance of species (of families) - the list is not sorted so if st1.dat contains the numbers 13,25,11 it implies that one species has abundance 13, another has 25, another has 11 and so on.</p> </div

Appendix A. The dependence of the exponent z on the width of the time window.

The dependence of the exponent z on the width of the time window

Supplement 3. Matlab code calculating the variance of Y for different time lags, used to analyze the BBS data.

<h2>File List</h2><div> <p><a href="varY_lag.m">varY_lag.m</a> (MD5: ac6cef05a4f087a2f14fc91ab62d0cd4) </p> </div><h2>Description</h2><div> <p>varY_lag.m is a matlab script that calculates the variance of <i>Y</i> over increasing timelags. The main output is the y_lag matrix of calculated variances, with columns denoting time lag and rows - size group. The code receives as output a very specific data structure: a STRUCT vector of sites, each site containing a STRUCT vector of species (+some metadata, i.e., all the lags and species at that site), each species having it's time series at this site. the time series is represented by a matrix with the first row denoting years and the second row denoting population size at that year.</p> <p>For example, sites(3).species(10).ts=[1970 1971; 10 15] means that at site3  for species 10, in 1970 there were 10 individuals and in 1971 there were 15.</p> <p>Some metadata are sites.all_lags (all available lags for this site) and sites.tot_abundance (which species are available at the site?). The grouping requires entering the minimum edges of each group. For example, if group_min = [1 31], the variance would be calculated separately for two groups, one with initial size 1-30, the other 30 and beyond.</p> </div