Learn landscape ecology¶
This is a short course on the ideas behind every tool of Landschaft. It is written for readers who have never studied landscape ecology: students, analysts who were handed a land-cover map and a question, researchers from neighbouring fields. By the end you should be able to say what each analysis measures, why ecologists measure it, when it is the right tool, and how to read and defend its results.
The chapters follow the order of a university course. Each one starts from the ecological question, introduces the concepts and, where it helps, the mathematics behind them, then puts them to work on the maps that ship with Landschaft. Everything is cited to the primary literature, so you can follow any thread further.
How to read this course
- Read in order the first time. Later chapters rely on the vocabulary of the earlier ones (patch, matrix, grain, extent, edge...).
- Run the code. Every code block runs as it is, in order within a chapter: open a notebook next to the page, with Landschaft installed.
- Answer the questions at the end of each chapter before opening the answers.
- Come back to chapter 15 when you start a project: it goes from questions to tools.
Chapters¶
-
Pattern, process and scale; patches, corridors and the matrix; island biogeography and metapopulations; from a landscape to a map.
-
2 · Scale
Grain, extent and thematic resolution; why every measurement depends on them; the scale of effect; choosing windows and resolutions.
-
3 · Habitat loss and fragmentation
Loss versus fragmentation per se, edge effects, thresholds and percolation; density, fragmentation classes and landscape mosaics.
-
Mathematical morphology in plain words; cores, edges, bridges and islets (MSPA); objects by size; parcels and contorted boundaries.
-
Composition and configuration; patch, class and landscape levels; the families of metrics, their redundancy and their pitfalls; sampling designs.
-
Structural and functional connectivity, dispersal, resistance surfaces and cost distance; the three families of connectivity models.
-
7 · Habitat networks
Graph theory for ecologists: IIC, PC and the equivalent connected area, the importance of patches and links, protected-area connectivity.
-
8 · Circuit theory
Random walkers and electric current: effective resistance, current density, pinch points and omnidirectional connectivity.
-
9 · Distance, cost and restoration
Distance and cost surfaces, least-cost paths and corridors, how to rank restoration options by the connectivity they add.
-
10 · Landscape change
Spatial processes of transformation, map error versus real change, transition matrices and connectivity through time.
-
11 · Continuous surfaces
Gradients instead of patches: surface metrics, the bearing area curve, autocorrelation and texture, terrain indices of elevation models.
-
Systematic conservation planning in ten chapters: complementarity, targets and costs, Zonation rankings, optimized plans and solvers, spatial design, trade-offs, evaluation, management actions and the planning process.
-
Niches and distributions, cleaning and thinning records, background and the accessible area, the main algorithms, spatial cross-validation, thresholds and extrapolation.
-
Land change models: demand, transition potential and allocation, the main families, validation against persistence at several resolutions, calibration by patterns, scenarios and replicates.
-
A decision guide from ecological questions to analyses, a worked study design and a checklist of what to report.
-
Appendices
A glossary of the terms of the course and an annotated list of further reading.
What the course assumes¶
Nothing beyond secondary-school mathematics and a first contact with Python:
variables, functions, a for loop. Where formulas appear they are explained
term by term, and the code shows the same computation on real data. Readers
who want the software side first can start with the
quickstart and
core concepts, then come back.
How the course relates to the rest of the site¶
| You want to... | Go to |
|---|---|
| understand what an analysis means and when to use it | this course |
| run an analysis step by step, with all its options | the tutorials |
| look up a function's parameters and outputs | the API reference |
| know how the results compare with other software | benchmarks & accuracy |