Results

I split my findings into two parts. First, real-world evidence from public data that I can stand behind right now. Second, the analysis framework I built and validated for the field data I still have to collect.

Part 1 · Real-world evidence real data

Where the species actually occur

I pulled 1,402 georeferenced records of my four species from GBIF across northwest and central Connecticut, from the Litchfield hills east to the Farmington Valley. The records cluster around towns, roads, and trails.

real data Interactive map of 1,402 GBIF records by species (points cluster when zoomed out). Click any point for species, year, and source; toggle the density heatmap or switch to satellite. Open the full map (all layers) →

Real clusters (Getis-Ord Gi*)

When I grid the records into 1 km cells and run Getis-Ord Gi*, each species shows statistically significant clusters. Japanese knotweed forms a clear hotspot along the developed Torrington corridor. I also include the all-species map as an honest control for where people tend to look.

real data Interactive Getis-Ord Gi* hotspots (red cells) with a monitoring-priority layer. Turn the species points on or switch the basemap. Hover a cell for its record count. Full map →

Local patterns

Visible reporting clusters appear in several developed-edge landscapes, including parts of the Farmington Valley, Greater Hartford, and northwest Connecticut. These clusters should be interpreted carefully because public records can reflect where people observe and report plants.

Still, the patterns help identify useful places for local monitoring, education, and field validation.

Most invaders sit on disturbed land more than chance

This is my central real-data result. I sampled CT ECO 2015 land cover at each occurrence and at random background points. The land itself is only 29.8% developed/disturbed, and three of the four species turn up there far more often. Japanese barberry is the exception, for a reason I return to below:

Species	Records	On disturbed land	Selection ratio	p-value
Japanese knotweed	196	58.7%	1.97×	2 × 10⁻¹¹
Oriental bittersweet	209	56.9%	1.91×	1 × 10⁻¹⁰
Garlic mustard	523	51.6%	1.74×	4 × 10⁻¹¹
Japanese barberry	269	30.5%	1.02×	0.91 (n.s.)

Land cover disturbance test — real data Left: percent of occurrences on developed/disturbed land vs. the background availability (dashed line); *** = p < 0.001. Right: full land-cover composition by species and background. Knotweed, bittersweet, and garlic mustard far exceed the background; barberry sits right at it.

What I conclude from the real data. Independent of my own field survey, three of the four invaders are strongly over-represented on human-disturbed land. Observer bias remains a limitation of public occurrence data, but the consistency of the land-cover and distance-to-development patterns suggests the disturbance association is unlikely to be only a reporting artifact. The fourth, barberry, needs a different test, which comes next.

How close to development? real data

Barberry looked harmless in that first test only because it is a forest-understory shrub: at any given point it usually sits under tree canopy, not on pavement, so a land-cover-at-the-point test cannot see its link to disturbance. A cross-check with 10 m Google Dynamic World (2025 to 2026) said the same, with knotweed, bittersweet, and garlic mustard well above the landscape average (2.5×, 1.9×, 1.9×) and barberry not.

So I asked a sharper question: not whether a plant is on developed land, but how close it is. I measured each occurrence's distance to the nearest built pixel and compared it to random background points. Now all four species, barberry included, are significantly closer to development than random:

Species	Median distance to built	Within 100 m	p (closer than random)
Japanese knotweed	54 m	62%	4 × 10⁻²⁶
Garlic mustard	80 m	55%	9 × 10⁻³⁸
Oriental bittersweet	125 m	46%	2 × 10⁻¹⁵
Japanese barberry	198 m	32%	6 × 10⁻⁸
Background (random)	385 m	21%	n/a

Distance to built land by species — real data Distance to the nearest built land (Dynamic World 2025-26). Left: every species sits far closer to development than random (*** = p < 0.001). Right: cumulative share within a given distance, where all four species curves rise above the background.

The conclusion. All four invaders are edge- and disturbance-associated. What differs is how far each reaches into intact habitat: knotweed hugs roadsides and fill (median 54 m from development), garlic mustard follows trails and edges (80 m), Oriental bittersweet reaches deeper as a canopy vine (125 m), and Japanese barberry penetrates furthest of all into the forest understory (198 m). The lesson: for understory and canopy invaders, distance to disturbance is a more sensitive test than land cover at a single point.

Part 2 · Analysis framework illustrative, simulated

These figures use simulated data. My field survey is not yet collected, so I built the full analysis on realistic simulated data to prove it runs end to end and recovers known patterns. When I enter my real field data, the same scripts produce the real figures with no other changes.

The edge gradient is nonlinear and species-specific

GAM nonlinear decline — illustrative My generalized additive model recovers a nonlinear decline in cover with distance from the edge, with the shape differing by species.

Different species cluster in different places

Per-species hotspots — illustrative Per-species Getis-Ord Gi* across four sites. In my framework the species' hotspots rarely coincide (garlic mustard vs. knotweed Jaccard ≈ 0.04), implying species-specific management.

From hotspots to a priority map

Priority zones — illustrative Crossing invasive hotspots with conservation value yields four management zones: Remove/Treat, Restore native, Monitor, and Low priority.

Conservation priorities

Putting both parts together, my recommendations for the study area are:

Prioritize disturbed edges. Roads, trails, and field margins are where all four species concentrate, so monitoring effort pays off most there.
Target control species by species. Because the invaders occupy different parts of the landscape, a single "invaded zone" approach would miss most of the problem.
Protect the interior. Great Mountain Forest and other intact areas show lower invasion, so keeping edges in check protects that interior.

See my data sources & tools →