Schedule for a one-semester course at Colorado State University (NR 575: Systems Ecology) introducing concepts of model–data assimilation to graduate students in ecology.

Regime shifts are massive, often irreversible, re-arrangements of non-linear ecological processes that occur when systems pass critical transition points. Ecological regime shifts sometimes have severe consequences for human well-being including eutrophication in lakes, desertification, and species extinctions. Theoretical and laboratory evidence suggests that statistical anomalies may be detectable leading indicators of regime shifts in ecological time series, making it possible to foresee and potentially avert incipient regime shifts. Conditional heteroskedasticity is persistent variance which is characteristic of time series with clustered volatility. Here, we analyze conditional heteroskedasticity as a potential leading indicator of regime shifts in ecological time series. We evaluate conditional heteroskedasticity using ecological models with and without four types of critical transition. On approaching transition points, all time series contain significant conditional heteroskedasticity. This signal is detected hundreds of time steps in advance of the regime shift. Time series without regime shifts do not have significant conditional heteroskedasticity. Because probability values are easily associated with tests for conditional heteroskedasticity, detection of false positives in time series without regime shifts is minimized. This property reduces the need for a reference system to compare with the perturbed system.

This project corresponds to the scripts and data files necessary to replicate the analysis in the manuscript "Seasonality, density dependence and spatial population synchrony" by Pedro G. Nicolau, Rolf A. Ims, Sigrunn H. Sørbye & Nigel G. Yoccoz The folder structure is Data: important files used to reproduce the code. Raw files are .csv and processed files are in .rds Scripts: R scripts necessary for analysis, numbered by order of sequence (some with subnumbering). 0 contains the important functions to compute Bayesian R^2 and correlograms; 01 contains processing for 1; 03 contains processing for 3. Plots: diverse plots used (or not) in the manuscript; not needed for analysis

Files descriptions:

All csv files refer to results from the different models (PAMM, AARs, Linear models, MRPPs) on each iteration of the simulation. One row being one iteration.
"results_perfect_detection.csv" refers to the results from the first simulation part with all the observations.
"results_imperfect_detection.csv" refers to the results from the first simulation part with randomly thinned observations to mimick imperfect detection.

ID_run: identified of the iteration (N: number of sites, D_AB: duration of the effect of A on B, D_BA: duration of the effect of B on A, AB: effect of A on B, BA: effect of B on A, Se: seed number of the iteration).
PAMM30: p-value of the PAMM running on the 30-days survey.
PAMM7: p-value of the PAMM running on the 7-days survey.
AAR1: ratio value for the Avoidance-Attraction-Ratio calculating AB/BA.
AAR2: ratio value for the Avoidance-Attraction-Ratio calculating BAB/BB.
Harmsen_P: p-value from the linear model with interaction Species1*Species2 from Harmsen et al. (2009).
Niedballa_P: p-value from the linear model comparing AB to BA (Niedballa et al. 2021).
Karanth_permA: rank of the observed interval duration median (AB and BA undifferenciated) compared to the randomized median distribution, when permuting on species A (Karanth et al. 2017).
MurphyAB_permA: rank of the observed AB interval duration median compared to the randomized median distribution, when permuting on species A (Murphy et al. 2021).
MurphyBA_permA: rank of the observed BA interval duration median compared to the randomized median distribution, when permuting on species A (Murphy et al. 2021).
Karanth_permB: rank of the observed interval duration median (AB and BA undifferenciated) compared to the randomized median distribution, when permuting on species B (Karanth et al. 2017).
MurphyAB_permB: rank of the observed AB interval duration median compared to the randomized median distribution, when permuting on species B (Murphy et al. 2021).
MurphyBA_permB: rank of the observed BA interval duration median compared to the randomized median distribution, when permuting on species B (Murphy et al. 2021).

"results_int_dir_perf_det.csv" refers to the results from the second simulation part, with all the observations.
"results_int_dir_imperf_det.csv" refers to the results from the second simulation part, with randomly thinned observations to mimick imperfect detection.
ID_run: identified of the iteration (N: number of sites, D_AB: duration of the effect of A on B, D_BA: duration of the effect of B on A, AB: effect of A on B, BA: effect of B on A, Se: seed number of the iteration).
p_pamm7_AB: p-value of the PAMM running on the 7-days survey testing for the effect of A on B.
p_pamm7_AB: p-value of the PAMM running on the 7-days survey testing for the effect of B on A.
AAR1: ratio value for the Avoidance-Attraction-Ratio calculating AB/BA.
AAR2_BAB: ratio value for the Avoidance-Attraction-Ratio calculating BAB/BB.
AAR2_ABA: ratio value for the Avoidance-Attraction-Ratio calculating ABA/AA.
Harmsen_P: p-value from the linear model with interaction Species1*Species2 from Harmsen et al. (2009).
Niedballa_P: p-value from the linear model comparing AB to BA (Niedballa et al. 2021).
Karanth_permA: rank of the observed interval duration median (AB and BA undifferenciated) compared to the randomized median distribution, when permuting on species A (Karanth et al. 2017).
MurphyAB_permA: rank of the observed AB interval duration median compared to the randomized median distribution, when permuting on species A (Murphy et al. 2021).
MurphyBA_permA: rank of the observed BA interval duration median compared to the randomized median distribution, when permuting on species A (Murphy et al. 2021).
Karanth_permB: rank of the observed interval duration median (AB and BA undifferenciated) compared to the randomized median distribution, when permuting on species B (Karanth et al. 2017).
MurphyAB_permB: rank of the observed AB interval duration median compared to the randomized median distribution, when permuting on species B (Murphy et al. 2021).
MurphyBA_permB: rank of the observed BA interval duration median compared to the randomized median distribution, when permuting on species B (Murphy et al. 2021).

Scripts files description:
1_Functions: R script containing the functions:
- MRPP from Karanth et al. (2017) adapted here for time efficiency.
- MRPP from Murphy et al. (2021) adapted here for time efficiency.
- Version of the ct_to_recurrent() function from the recurrent package adapted to process parallized on the simulation datasets.
- The simulation() function used to simulate two species observations with reciprocal effect on each other.
2_Simulations: R script containing the parameters definitions for all iterations (for the two parts of the simulations), the simulation paralellization and the random thinning mimicking imperfect detection.
3_Approaches comparison: R script containing the fit of the different models tested on the simulated data.
3_1_Real data comparison: R script containing the fit of the different models tested on the real data example from Murphy et al. 2021.
4_Graphs: R script containing the code for plotting results from the simulation part and appendices.
5_1_Appendix - Check for similarity between codes for Karanth et al 2017 method: R script containing Karanth et al. (2017) and Murphy et al. (2021) codes lines and the adapted version for time-efficiency matter and a comparison to verify similarity of results.
5_2_Appendix - Multi-response procedure permutation difference: R script containing R code to test for difference of the MRPPs approaches according to the species on which permutation are done.

A fundamental tenet of modern ecology and conservation science is that species occurrence in habitat patches can be determined by patch area and isolation. But such island biogeographic models often poorly predict actual species occurrences in structurally complex landscapes that typify most ecosystems. Recent advances in circuit theory have enhanced estimates of species dispersal, and through integration with island biogeography, can provide powerful ways to predict landscape-scale distribution of species assemblages. Applying such an integrative analytical framework to 43 bird species in Tanzania improved model fit by an average of 2.2-fold over models where patch isolation was estimated without accounting for landscape matrix heterogeneity. This approach also allowed us to assess species-specific dispersal rates and quantify differences among land cover types in their permeability to animal movement. These results reaffirm the utility of foundational island biogeographic principles, yet with an important caveat. Two-thirds of the variance in species occurrence in habitat fragments can be explained simply by patch area and isolation, conditional on isolation explicitly accounting for the spatial configuration of different land cover types in the landscape matrix.

Ecology-Statistical methods-Data processing is a book subject. It includes 4 books, written by 4 different authors.

Henslow's sparrow count dataCounts of Henslow's sparrows at Big Oaks National Wildlife Refuge with covariates for years since prescribed fire.data.csv

Mathematical details of random-effects ordination.

Biology students’ understanding of statistics is incomplete due to poor integration of these two disciplines. In some cases, students fail to learn statistics at the undergraduate level due to poor student interest and cursory teaching of concepts, highlighting a need for new and unique approaches to the teaching of statistics in the undergraduate biology curriculum. The most effective method of teaching statistics is to provide opportunities for students to apply concepts, not just learn facts. Opportunities to learn statistics also need to be prevalent throughout a student’s education to reinforce learning. The purpose of developing and implementing curriculum that integrates a topic in biology with an emphasis on statistical analysis was to improve students’ quantitative thinking skills. Our lesson focuses on the change in the richness of native species for a specified area with the aid of iNaturalist and the capacity for analysis afforded by Google Sheets. We emphasized the skills of data entry, storage, organization, curation and analysis. Students then had to report their findings, as well as discuss biases and other confounding factors. Pre- and post-lesson assessment revealed students’ quantitative thinking skills, as measured by a paired-samples t test, improved. At the end of the lesson, students had an increased understanding of basic statistical concepts, such as bias in research and making data-based claims, within the framework of biology.

Primary Image: Website screenshot of an iNaturalist observation (Clasping Milkweed – Asclepias amplexicalis). This image is an example of a data entry on iNaturalist. The data students export from iNaturalist is made up of hundreds, or even thousands, of observations like this one. This image is licensed under Creative Commons Attribution - Share Alike 4.0 International license. Source: Observation by cassi saari, 2014.

Ecology-Research-Statistical methods is a book subject. It includes 5 books, written by 4 different authors.

Ecological flow (EFlow) statistics have been designated to characterize the magnitude, frequency, and duration of extreme high- and low-flows, the timing of seasonal flows, and the consistency of the historic regime. This Child Item contains a table of 178 EFlows for the time periods 1940-1969, 1970-1999, and 2000-2018, with absolute and percent change between periods, where applicable. Statistics were computed by Water Year (WY) for all 178 metrics and absolute and percent change were calculated by comparing metrics between combinations of two of the three time periods (1940-1969 and 1970-1999; 1940-1969 and 2000-2018; 1970-1999 and 2000-2018). Streamgages from the original dataset (n = 409) were excluded from one or more time periods of analysis because of extensive data gaps that would yield incomplete EFlows; therefore, stations were indexed into the earliest possible time period relative to their installation date (for example, a streamgage with an operating start year of 1958 would be included in the analysis for the time periods 1970-1999 and 2000-2018), which resulted in different sample sizes for each period: 1940-1969 (n = 90), 1970-1999 (n = 167), and 2000-2018 (n = 243). Similarly, multiple stations were wholly excluded because of frequent discontinuities in the daily mean streamflow through all three time periods. Finally, a streamgage must have fallen within at least two time periods to have a change value. As such, not all stations are represented in the change analysis (change between 1940-1969 and 1970-1999 [n = 90]; change between 1940-1969 and 2000-2018 [n = 90]; change between 1970-1999 and 2000-2018 [n = 167]).

Results from the ANOVA at the end of the experiment, using log transformed data. The median and interquartile range (IQR) are reported for the last sampling date in the original units of the metric (i.e. not log transformed). The ‘Flatness ’, ‘Levels ’, and ‘Parallelism’ columns show the p-values for each of these tests in the profile analysis (see text). Metrics without these tests were only measured once at the end of the experiment. PAR is photosynthetically active radiation, is the absorption co-efficient at 320 nm. Loadings for the first three axes from the linear discriminant analysis (LDA) explain 20%, 19%, and 15% of the discriminant function, respectively.

All statistics were done in R Studio

Scientific advances in microbial ecology rely on both high-quality data and rigorous analysis. At present, Statistical Analysis sections of many microbiome papers lack essential detail and justification. To support researchers in clearly and transparently presenting their methods, we provide guidelines for describing a microbiome data analysis. The guidelines span data transformations, justification for modeling choices, parameter interpretation, sensitivity analyses, code and data availability, and more. These guidelines are available under a Creative Commons Zero (CC0) license. We hope to accelerate the accumulation and dissemination of scientific knowledge by permitting their condition-free distribution, adaptation, and development. Methods These guidelines were drafted by the authors.

Current practice in the normalization of microbiome count data is inefficient in the statistical sense. For apparently historical reasons, the common approach is either to use simple proportions (which does not address heteroscedasticity) or to use rarefying of counts, even though both of these approaches are inappropriate for detection of differentially abundant species. Well-established statistical theory is available that simultaneously accounts for library size differences and biological variability using an appropriate mixture model. Moreover, specific implementations for DNA sequencing read count data (based on a Negative Binomial model for instance) are already available in RNA-Seq focused R packages such as edgeR and DESeq. Here we summarize the supporting statistical theory and use simulations and empirical data to demonstrate substantial improvements provided by a relevant mixture model framework over simple proportions or rarefying. We show how both proportions and rarefied counts result in a high rate of false positives in tests for species that are differentially abundant across sample classes. Regarding microbiome sample-wise clustering, we also show that the rarefying procedure often discards samples that can be accurately clustered by alternative methods. We further compare different Negative Binomial methods with a recently-described zero-inflated Gaussian mixture, implemented in a package called metagenomeSeq. We find that metagenomeSeq performs well when there is an adequate number of biological replicates, but it nevertheless tends toward a higher false positive rate. Based on these results and well-established statistical theory, we advocate that investigators avoid rarefying altogether. We have provided microbiome-specific extensions to these tools in the R package, phyloseq.

This statistic presents the of main concerns expressed by the French surveyed on ecology and sustainable development in France in 2018, distributed by gender. It reveals that the majority of both women and men declared that the impact of environmental pollution on health was a topic of concern on ecology and sustainable development. On the other hand, eleven percent of male respondents and 15 percent of women mentioned nuclear risks as an issue in terms of ecology.

Descriptive statistics for sample carbon biomass data.

UNSD Environmental Indicators disseminate global environment statistics on ten indicator themes compiled from a wide range of data sources. The themes and indicator tables were selected based on the current demands for international environmental statistics and the availability of internationally comparable data. Statistics on Water and Waste are based on official statistics supplied by national statistical offices and/or ministries of environment (or equivalent institutions) in response to the biennial UNSD/UNEP Questionnaire on Environment Statistics, complemented with comparable statistics from OECD and Eurostat, and water resources data from FAO Aquastat. Statistics on other themes were compiled by UNSD from other international sources. In a few cases, UNSD has made some calculations in order to derive the indicators. However, generally no adjustments have been made to the values received from the source. UNSD is not responsible for the quality, completeness/availability, and validity of the data. Environment statistics is still in an early stage of development in many countries, and data are often sparse. The indicators selected here are those of relatively good quality and geographic coverage. Information on data quality and comparability is given at the end of each table together with other important metadata.

This statistic illustrates the opinion of the French on the most effective individual actions to protect the environment in 2015. It can be read that more than 40% of the respondents considered the reduction of waste as the most effective eco-citizen gesture.

Humanity has been in an ecological overshoot since the 1970s, where demand for natural resources exceeded that of what the Earth can regenerate. As of 2022, if the world's entire population lived like those in the United States, we would need resources equivalent to five times what our Earth can regenerate to satisfy the global demand.