R &amp; QGIS tutorials for ecologists.

Choosing a decision threshold in R: the cost ratio hidden in every ecological cut-off, decision curves and net benefit, and the price of miscalibration.

Jul 16, 2026

Data leakage in ecological model validation

Four routes by which information crosses from a held-out fold into an ecological model fit, each measured in R: preprocessing, screening, grouped sites, tuning.

Jul 16, 2026

Calibrating predicted probabilities in R

Reliability diagrams, the Brier decomposition, Platt scaling and isotonic regression in base R for occupancy models whose probabilities cannot be trusted.

Jul 15, 2026

Class imbalance in species presence models

machine learning

model evaluation

species distributions

What class imbalance does to a species presence model in R: why the positive count and not the ratio limits the fit, and what downsampling really changes.

Simulating an acoustic recogniser in base R to show why automated detection counts are an affine index of true call rate, with a false-trigger floor underneath.

Jul 15, 2026

Checking a genetic clustering analysis

Four checks for a genetic clustering analysis in R: unbalanced sampling, linked loci, variation between runs, and whether the structure survives a subsample.

Jul 14, 2026

Assignment tests and self-assignment

Build genotype assignment likelihoods in base R, measure the leave-one-out bias in self-assignment, and run an exclusion test for an unsampled source.

Jul 14, 2026

Choosing K in genetic clustering

Implement the log-likelihood curve, cross-validation and the Evanno delta K in R, then watch all three choose clusters in a cline of demes that has none.

Jul 14, 2026

Circuit theory and effective resistance

Solve a landscape as an electrical circuit in base R, map current density, and measure the one thing least-cost paths cannot see: redundant routes.

Jul 12, 2026

Least-cost paths and resistance surfaces

Compute cost distance and least-cost paths in base R with iterative sweeps, then measure how much the answer depends on the resistance values you chose.

Jul 12, 2026

Patch metrics and fragmentation in R

Label habitat patches with base R, measure fragmentation at a fixed habitat amount, and watch the percolation threshold and the grain change the answer.

Jul 12, 2026

Checking a coalescent analysis

Four checks for coalescent inference in R: what one locus can say, what recombination buys, whether demography really mimics selection, and where to spend effort.

Tajima’s D and the frequency spectrum

Build Tajima’s D from the site frequency spectrum in R and measure what it detects: growth, structure and bottlenecks all move it, and so does selection.

Nucleotide diversity and Watterson theta

Put mutations on coalescent trees in R, compare the two classical estimators of theta, and measure why many short loci beat one long sequence of the same size.

The coalescent and gene trees

Simulate the coalescent in R, check the tree height and total branch length against theory, and see why a single gene tree says almost nothing about history.

Checking an effective size estimate

Four checks for an Ne estimate in R: which time window it covers, what sampling relatives does to it, how wide the interval is, and what linkage costs.

Bottlenecks and genetic diversity

Push a simulated population through a bottleneck in R and watch which measures of diversity notice: allelic richness, heterozygosity and the M ratio disagree.

Estimating Ne from genetic data

Implement the temporal and linkage disequilibrium estimators of effective size in R, and measure how much data each needs before its answer means anything.

Effective population size in R

Simulate what Ne means in R: the sex ratio, the variance in offspring number and the harmonic mean across bad years, each measured against its formula.

Checking a population genetics analysis

Four checks for an Fst analysis in R: loci against individuals, separating substructure from marker error, filtering rare variants, and outlier tests.

Drift, migration and isolation by distance

Simulate drift and gene flow in R, check the Fst equilibrium against 1/(1 + 4Nm), and see how little a measured Fst says about the number of migrants.

F-statistics and population structure

Compute Fis, Fst and Fit in R from three heterozygosities, see why Gst is capped by diversity within populations, and estimate Fst without the sampling bias.

The multivariate breeder’s equation

evolutionary ecology

quantitative genetics

natural selection

Predict multi-trait evolution in R with the Lande equation delta z = G beta: correlated responses, genetic constraints, and the line of least resistance.

Jul 6, 2026

The breeder’s equation in R

A raw forecast score means nothing without a baseline. In R, the same ecological forecast is skilful against one reference and useless against another.

Jun 25, 2026

The CRPS and the Brier score

forecasting

model evaluation

Two proper scores for ecological forecasts: the Brier score for presence, the CRPS for abundance, built from scratch in R with the identities linking them.

Why the first flowering date advances as monitoring effort grows while the mean date stays put: the extreme-value bias behind spurious phenological shifts, in R.

The Levins model of patch occupancy from scratch in R: the colonisation-extinction balance, the equilibrium fraction, and the extinction threshold.

Jun 21, 2026

Checking a coexistence analysis

Three diagnostics for an annual plant coexistence result in R: mutual invasibility, whether your design identifies self-limitation, and stability to model form.

Jun 21, 2026

Assumptions behind coexistence

A coexistence verdict extrapolates far past the competition data. See in R how model form and demographic rates decide the outcome where no data constrain it.

Niche and fitness differences

Turn fitted annual plant competition coefficients into niche and fitness differences in R, and see why the stronger competitor need not exclude its rival.

Fitting annual plant competition models

Fit Beverton-Holt competition coefficients to annual plant fecundity data in R, and see why the experimental design decides which coefficients you can estimate.

Checking a stable isotope mixing model

Three checks for a stable isotope mixing model in R: is the consumer inside the mixing polygon, is the posterior data-driven, and does source aggregation hold.

Discrimination and concentration in mixing models

In stable isotope mixing models the assumed discrimination factor and elemental concentrations drive the diet estimate more than the consumer data do.

Bayesian stable isotope mixing models

Why additive beta on the Gini-Simpson index is bounded by alpha, how Jost’s alpha fixes it, and the exact identity that turns Hill beta into the Sorensen index.

Jun 16, 2026

Checking a camera trap density estimate

camera traps

abundance

Three checks for a random encounter model density estimate in R: the independence filter, interval coverage in a patchy landscape, and where the error lives.

Jun 16, 2026

Staying time and the REST model in R

The REST model estimates animal density from how long animals stay in view, with no external speed. Little’s law and Cauchy’s chord identity, tested in R.

Three checks for approximate Bayesian computation in ecology: rank calibration for the sampler, posterior width for the answer, acceptance distance for the model.

Jun 14, 2026

ABC-MCMC and sequential ABC samplers

stats

approximate Bayesian computation

Rejection ABC draws from the prior and throws almost all of it away. ABC-MCMC and sequential Monte Carlo recycle the good draws; a Ricker model prices the gain.

Jun 14, 2026

Choosing summary statistics for ABC

simulation

In ABC the summaries are the data. Sufficiency, the price of a useless summary, distance scaling and semi-automatic ABC, measured in R on a Ricker model.

Connectance, generality, vulnerability and the specialisation indices H2’ and d’ for a plant-pollinator web, in base R, with the sampling caveats spelled out.

Jun 9, 2026

Checking a survival model

model checking

Diagnostics for survival models: the proportional hazards test, martingale residuals for functional form, and Cox-Snell residuals. None proves the model right.

Jun 9, 2026

Frailty and recurrent event models

Survival times that share a cluster or repeat within an individual are not independent: the naive model understates uncertainty, and units limit the correction.

Jun 9, 2026

Parametric survival and the AFT model

A parametric survival model reads a covariate two ways, as a clock acceleration or a hazard ratio, and predicts absolute survival that Cox cannot give directly.

Diagnosing a time series model in base R: residual whiteness with Ljung-Box, prewhitening before cross-correlation, and why passing every check proves nothing.

Jun 6, 2026

Spectral analysis of population cycles

time series

spectral analysis

Reading a periodogram for population cycles: why the raw version is noisy, how aliasing turns a fast cycle into a slow one, and why a peak needs a red-noise null.

Jun 6, 2026

Fitting ARIMA models to ecological time series

time series

forecasting

Identify an ARMA model from the ACF and PACF, fit it with arima() in base R, and avoid the twin traps of over-differencing and plug-in prediction intervals.

model checking

Deviance residuals mislead for beta, ordinal and Tweedie models. Use randomised quantile residuals, a proportional-odds test and held-out calibration instead.

Jun 5, 2026

Tweedie regression for biomass and cover with zeros

distributional regression

GLM

Biomass and catch per unit effort mix exact zeros with a skewed positive part. Fit Tweedie regression with mgcv, and see why logging with a constant is biased.

Jun 4, 2026

Model the spread, not just the mean. A base-R location-scale fit, why naive prediction intervals miscalibrate, and how it connects to quantile regression.

Jun 1, 2026

Heteroscedasticity and limiting factors

Building the centred, additive and isometric log-ratio transformations by hand, with Aitchison distance, perturbation and powering, using base R only.

Why single imputation misleads: mean imputation attenuates the slope, regression imputation inflates correlations, and one dataset hides the real uncertainty.

When both variables carry error, ordinary regression is biased either way. Deming regression uses the error-variance ratio to recover the true slope in R.

May 27, 2026

Measurement error and regression dilution

structural equation models

Split an ecological correlation into direct and indirect routes with lm in base R: standardised path coefficients, the tracing rules, and why a slope misleads.

May 24, 2026

Checking for publication bias

meta-analysis

publication bias

Detect small-study effects in an ecological meta-analysis with base R: funnel plots, Egger’s regression test, and trim-and-fill, with the limits of each.

May 24, 2026

Meta-regression with moderators

meta-analysis

meta-regression

Explain heterogeneity with a study-level moderator in base R: weighted meta-regression, residual tau-squared, an R-squared analogue, and error control.

May 24, 2026

Heterogeneity in meta-analysis

meta-analysis

heterogeneity

Measure disagreement among ecological studies in base R: Cochran’s Q, I-squared with an interval, tau-squared, and a prediction interval for a new study.

A community occupancy model can return sensible parameters yet fit badly. A posterior predictive check in base R catches unmodelled detection heterogeneity.

May 23, 2026

Community covariates and species traits

Give each species its own slope on a site gradient, and use a trait to explain the variation. A hierarchical model in base R beats a naive two-stage fit.

May 23, 2026

Species richness by data augmentation

Estimate how many species a survey missed. A community occupancy model with data augmentation counts the undetected tail in base R, and beats a Chao estimator.

May 23, 2026

Multi-species occupancy models

Build a Gibbs sampler in base R for the mean and variance of a trait, and see why the mean’s posterior is a t distribution rather than a narrow normal.

Metropolis-Hastings from scratch

Code a random-walk Metropolis sampler by hand in R to estimate a survival probability, and see why the posterior beats a Wald interval that dips below zero.

Validating a step selection model

Used-habitat calibration plots from scratch: simulate used locations from a fitted step selection model and check if it reproduces the chosen habitat.

Availability sampling for step selection

habitat selection

How many available steps per used step, and from which distribution: more steps buy precision, while iSSA barely depends on the tentative sampling kernel.

Integrated step selection analysis in R

habitat selection

Integrated step selection analysis from scratch: add movement covariates to a conditional model to debias habitat selection and recover the movement kernel.

Step selection functions for animal movement

habitat selection

Fit step selection functions in R from scratch: matched steps, a conditional logistic likelihood by hand, and why pooling strata attenuates selection.

Checking a state-space movement model

Check a fitted state-space movement model with one-step-ahead residuals in base R: QQ plots and autocorrelation reveal what a misspecified model quietly hides.

A particle filter for animal movement

Build a bootstrap particle filter from scratch in base R for animal tracks with heavy-tailed Argos errors that drag a Gaussian Kalman filter off course.

Regularising irregular animal tracks

Turn gappy, irregular animal tracks into a regular series with a continuous-time integrated random walk and Kalman smoother in base R, with honest gap error.

State-space models for animal movement

Fit a linear-Gaussian correlated random walk to noisy telemetry in base R: a hand-coded Kalman filter and smoother that split movement from observation error.

Checking a movement HMM

hidden markov models

Two checks a fitted movement HMM needs: forecast pseudo-residuals to test the model, and forward-backward posterior probabilities for what Viterbi hides.

Covariates in a movement HMM

hidden markov models

Let HMM transition probabilities depend on time of day through a multinomial logit, recover a diurnal activity budget, and see what a homogeneous model hides.

How many states in a movement HMM?

hidden markov models

Choose how many states a movement HMM needs without overfitting: AIC and BIC across candidate models, why an extra state splits a real one, and label switching.

Fitting the theta-logistic population growth model in R by maximum likelihood, and why the shape parameter theta is barely identifiable from one time series.

May 15, 2026

Estimating population trends in R

Why one linear rate can hide a population crash and recovery, using a GAM in R to show trend shape, and why count data need a count model rather than log-OLS.

May 15, 2026

Gompertz state-space model in R

Separating process noise from observation error in a population time series with a Gompertz state-space model in R, fitted by a hand-coded Kalman filter.

May 15, 2026

Detecting density dependence in R

Fitting a Gompertz model to a population time series in R, why the ordinary regression test for density dependence over-rejects, and the bootstrap fix.

Cormack-Jolly-Seber survival models in R

capture-recapture

Estimate apparent survival and recapture from open capture histories in R: build the m-array and fit a Cormack-Jolly-Seber model by maximum likelihood.

Nest survival with logistic exposure in R

nest survival

Estimate daily nest survival in R with the logistic-exposure model, compare it against the Mayfield estimate, and let daily survival vary with nest age.

Cox proportional hazards and the PH assumption

model diagnostics

Fit a Cox proportional hazards model in R, read the hazard ratios, test the proportional-hazards assumption with Schoenfeld residuals, and stratify to fix it.

SDM

cross-validation

spatial autocorrelation

mgcv

Why random cross-validation overstates SDM accuracy when occurrences cluster in space, and how spatial block cross-validation gives an honest estimate, in R.

May 11, 2026

Evaluating species distribution models in R

Test an SDM in R against held-out data: the ROC curve and AUC, a confusion matrix with sensitivity, specificity and TSS, and how to pick a good threshold.

May 10, 2026

Pseudo-absence and background points for SDMs

How many background points an SDM needs, why predicted values are relative rather than probabilities, and how case weights stabilise the scale, worked in R.

May 10, 2026

Species distribution modelling with GLM in R

Fit a species distribution model in R with a binomial GLM: draw presence and background points, model humped responses, and map predicted habitat suitability.

Occurrence records from GBIF and iNaturalist need cleaning. In R: drop impossible and duplicate coordinates, flag low precision, and map the result with sf.

Species-area relationships in R

Fit species-area relationships in R: the power law and the logarithmic model, why the power exponent z matters, and how nls differs from log-log regression.

Additive diversity partitioning in R

Split gamma diversity into alpha and beta additively in R with vegan adipart, test it with a null model, and see why Hill numbers matter for diversity indices.

Nestedness, NODF, and null models in R

Measure community nestedness with the NODF metric in R using vegan, then test it with fixed-margin null models and see why the null choice decides the verdict.

Distance decay of community similarity in R

Run a power analysis by simulation in R: build a power curve over sample size, check the Type-I rate, and see why post-hoc power and low-power studies mislead.

May 6, 2026

GLM residual diagnostics with simulation in R

Why Pearson and deviance residuals mislead for GLMs, and how to build simulation-based quantile residuals in R to spot overdispersion and zero-inflation.

May 6, 2026

Model selection with AIC in R for ecology

model selection

RDA vs CCA for community ecology: by gradient length

ordination

constrained ordination

vegan

When to use redundancy analysis versus canonical correspondence analysis on community data, why the DCA gradient-length rule decides, and where Hellinger fits.

May 4, 2026

From QGIS to R: a spatial join with GeoPackage

Load occurrence points in QGIS, tag each with the habitat patch it falls in by a spatial join, export to GeoPackage, and continue the analysis in R with sf.

May 4, 2026

Spatial autocorrelation and Moran’s I in R

Nearby sites resemble each other, breaking the independence regression assumes. Moran’s I measures it, a correlogram shows its scale, a residual test finds it.

capscale vs dbrda: same axes, different test

vegan has two functions for distance-based constrained ordination. On Bray-Curtis they return the same axes but a different pseudo-F: the negative eigenvalues.

Modelling nonlinear species responses with GAMs

A count GLM forces a single direction of response. When a species peaks at an intermediate optimum, a generalised additive model lets the data choose the shape.

Raster data in R with terra: DEMs and extraction

spatial

raster data

What a raster really is, how to read its structure, map algebra and slope from an elevation model, what coarsening the grain does, and pulling values at points.

Poisson and negative binomial GLMs in R

count data

Why logging abundance counts is a poor reflex, how to fit a Poisson GLM, how to tell it is too tight, and how a negative binomial and an effort offset fix it.

Turn a dissimilarity matrix into groups with hierarchical clustering: a dendrogram from hclust, a linkage choice, a cut into clusters, checked against an NMDS.

Rarefaction and accumulation curves in R

diversity

More individuals and more plots both raise the species count, whatever the true richness. Rarefaction and accumulation curves separate diversity from effort.

Indicator species analysis with IndVal in R

indicator species

PERMANOVA says groups differ; indicator species analysis says which species drive it. A walk through multipatt, the IndVal index, specificity and fidelity.

Beta diversity in R: turnover vs nestedness (betapart)

beta diversity

A single dissimilarity number hides two processes: species replacement and species loss. Separate them in R with the betapart package and the Baselga framework.

Constrained ordination with distance-based RDA in R

vegan

ordination

constrained ordination

NMDS shows all compositional variation and overlays environment afterward; distance-based RDA builds the ordination from predictors and tests what they explain.

Mapping species richness in R with sf

Turn species occurrence records into a gridded richness map in R with the sf package, then export a GeoPackage you can open and style further in QGIS.

Statistics on an ordination: envfit and PERMANOVA in R

An NMDS shows the pattern. Use envfit to fit environmental variables onto it, adonis2 to test whether groups differ, and betadisper to keep the test honest.

Ordination with NMDS in R

Turn a site-by-species table into a map of community composition with vegdist and metaMDS, then read the stress and decide how many dimensions you need.

Computing diversity indices in R with vegan

biodiversity

Compute species richness, Shannon and Simpson diversity from a site-by-species community matrix in R with vegan, with reusable code and a small worked example.