Appendix B. Frequency of Type I and Type II errors in 12 different benchmark tests for the two proposed methods (EcoTest, BiogTest).

Frequency of Type I and Type II errors in 12 different benchmark tests for the two proposed methods (EcoTest, BiogTest)

Appendix A. Analytic estimators for rarefaction (interpolation) of Hill numbers of order q = 0, q = 1, and q = 2, given a reference sample.

Analytic estimators for rarefaction (interpolation) of Hill numbers of order q = 0, q = 1, and q = 2, given a reference sample

Grain size affects the relationship between species richness and above-ground biomass in semi-arid rangelands

Background: Discrepancies in the shape of the productivity–diversity relationship may arise from differences in spatial scale. We hypothesised that there is a grain size effect on the productivity–diversity relationship. Aims: To determine the effect of three sampling grain sizes on the productivity–diversity relationship. Methods: We applied generalised linear mixed effect models on community data from 735 vegetation plots in the Taleghan rangelands, Iran, sampled at three grain sizes (0.25, 1 and 2 m2) to ascertain plant productivity-diversity patterns, while accounting for the effects of site, plant community type, disturbance, and life form. Results: Overall, relationships between biomass and plant species richness were unimodal at grain sizes of 0.25 and 1 m2, and asymptotical at 2 m2. The spurious occurrence of a single large shrub may overwhelm a small-sized sampling unit, resulting in a high estimate of the sample’s biomass relative to species richness. However, the relationship between biomass and species richness at larger grain sizes is more likely to reach an asymptote. Conclusions: Shrubs are partly responsible for driving the relationship between plant biomass and species richness. Given that the frequency of shrubs is highly variable between small plots but not so in large plots, their presence may result in unimodal productivity–diversity relationships at small but not at large grain sizes.</p

Supplement 1. Data matrices and R source code of the software used in the paper.

<h2>File List</h2><div> <p><a href="RareNMtests.zip">RareNMtests.zip</a> (MD5: 4fd3828969780f87ee17ac2c5ce60640)</p> <p><a href="Example_of_negative_binomial_sampling_error.pdf">Example_of_negative_binomial_sampling_error.pdf</a> (MD5: 25738f0fe874ae2ea3ce38a7e931d7d5)</p> <p><a href="Benchmark_tests.R">Benchmark_tests.R</a> (MD5: 77b9f48830fbbc922c70ad9a782dd740)</p> <p><a href="Chiapas_dataset.csv">Chiapas_dataset.csv</a> (MD5: 1183bdcf5ae0a155731729fbc2648d0b)</p> </div><h2>Description</h2><div> <p>rareNMtests.zip-R package, with functions to implement ecological and biogeographical null model tests for comparing rarefaction curves. </p> <p>Example_of_negative_binomial_sampling_error.pdf – R code to exemplify how a negative binomial error is added to the abundance counts every time a sample is randomly drawn from the simulated assemblage in the <i>BiogTest</i> randomization algorithm. </p> <p>Benchmark_tests.R – R code used to run all benchmark tests.</p> <p>Chiapas_dataset.csv – Data set of 224 circular 0.1-ha plots from tropical montane cloud forests in three mountainous regions of southern Mexico.</p> </div

Is Ground Cover Vegetation an Effective Biological Control Enhancement Strategy against Olive Pests?

<div><p>Ground cover vegetation is often added or allowed to generate to promote conservation biological control, especially in perennial crops. Nevertheless, there is inconsistent evidence of its effectiveness, with studies reporting positive, nil or negative effects on pest control. This might arise from differences between studies at the local scale (e.g. orchard management and land use history), the landscape context (e.g. presence of patches of natural or semi-natural vegetation near the focal orchard), or regional factors, particularly climate in the year of the study. Here we present the findings from a long-term regional monitoring program conducted on four pest species (<i>Bactrocera oleae</i>, <i>Prays oleae</i>, <i>Euphyllura olivina</i>, <i>Saissetia oleae</i>) in 2,528 olive groves in Andalusia (Spain) from 2006 to 2012. Generalized linear mixed effect models were used to analyze the effect of ground cover on different response variables related to pest abundance, while accounting for variability at the local, landscape and regional scales. There were small and inconsistent effects of ground cover on the abundance of pests whilst local, landscape and regional variability explained a large proportion of the variability in pest response variables. This highlights the importance of local and landscape-related variables in biological control and the potential effects that might emerge from their interaction with practices, such as groundcover vegetation, implemented to promote natural enemy activity. The study points to perennial vegetation close to the focal crop as a promising alternative strategy for conservation biological control that should receive more attention.</p></div

Response variables used for each of the four species analysed in this study, family error used in the generalized linear mixed models, and number of observations (i.e. number of monitoring stations) available for each response variable for all the years.

<p>Response variables used for each of the four species analysed in this study, family error used in the generalized linear mixed models, and number of observations (i.e. number of monitoring stations) available for each response variable for all the years.</p

Best model estimations for the differences between ground cover and bare soil for all the response variables.

<p>Positive values represent an increase in pest abundance in the presence of ground cover (filled circle) whereas negative values represent a decrease in pest abundance in the presence of ground cover (filled triangle). Bars represent a 95% of the confidence interval of the random effects predictions (± σ²) at a local, landscape, and regional scale. If a random factor is not included in the selected best model (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0117265#pone.0117265.t002" target="_blank">Table 2</a>), no 95% confidence interval bars were drawn.</p

Map of the Andalusian region, Spain, and the monitoring stations used in this study.

<p>Red points represent ground cover MS while blue points represent bare soil MS before pairing.</p

Cover change classification and deforestation hotspots in Southern Mexico.

<p>Hotspots were identified where more than 10% of the total municipal land area was losing forest cover during the entire study period (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0042309#pone-0042309-g003" target="_blank">Figure 3</a>). A) Lacandon forest hotspot; B) Northern Chimalapas hotspot; and C) Benito Juárez-Isla Mujeres hotspot.</p

Study area.

<p>A) Ecological regions of Southern Mexico as proposed by Olson et al. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0042309#pone.0042309-Olson1" target="_blank">[37]</a>: gray areas consist in areas excluded from analysis. B) Large forest reserves.</p