Skip to main content

Observations & Species Sampling

Observations Example

Overview​

The Observations and Observation Species Sampling modules in TytonIQ provide detailed ecological records collected from established sampling sites. Together, they allow users to document field conditions, capture on-ground metrics (such as species richness), and record species-level measurements. These modules are critical for validating rehabilitation performance, supporting ecological research, and providing evidence for compliance reporting.


Key Features​

1. Interactive Filtering and Navigation​

Both modules provide filters in the bottom panel to refine displayed data:

Observations Example

Observations Tab​

  • Observation Name: Unique identifier (e.g. APS-01-20170528).
  • Sampling Site Name: Associated sampling site ID (e.g. APS-01).
  • On-Ground Campaign: Survey campaign grouping (e.g. 2017 Fitzroy Hill On-ground Survey).
  • Observer Name: Person conducting the observation.
  • Native Species Count: Number of species identified.
  • Vegatation Condition: Diversity index with thresholds for success.
  • Comments: Field notes describing site conditions.
  • Photo count: Linked imagery from the inspection.

Observations Example

Observation Species Sampling Tab​

  • Observation Name: Links species data to a specific observation ID.
  • Family: Plant family (e.g. Fabaceae).
  • Genus–Species: Scientific name (e.g. Acacia ancistrocarpa).
  • Cover (%): Vegetation cover for the species.
  • Height (m): Average plant height.
  • Density (plants/ha): Plant density per hectare.
  • Comments: Species-level notes (if recorded).

Observations Example

2. Observation Data Panel (Right Sidebar)​

When selecting an observation, the right-hand panel displays key details:

  • Date Established: Linked sampling site establishment date.
  • Type: Rehabilitation or analogue classification.
  • Sampling Method: Technique used (e.g. Quadrat).
  • Last Observed: Date of most recent survey.
  • Observation Date: Date of current observation.
  • Observer: Field staff name (if recorded).
  • Native Species Count: Total species observed (e.g. 24).
  • Perennial Species Richness: Diversity index with passing threshold (e.g. 18 >15).
  • Comments: Qualitative notes (e.g. very shallow surface soil (5–10 cm) over hard subsoil, suggesting deep ripping required).
  • Photos: Linked inspection images.

From the panel, users can navigate directly to species sampling records for the selected observation.


Observations Example

3. Observations Table (Bottom Panel)​

The Observations table provides a searchable list of recorded surveys. Key fields include:

FieldDescription
Observation NameUnique identifier linking to sampling site (e.g. APS-01-20170528).
Sampling SiteAssociated sampling site (e.g. APS-01).
Observation DateDate of field survey.
On-Ground CampaignCampaign grouping (e.g. 2017 Fitzroy Hill Survey).
ObserverPerson who carried out the observation.
Native Species CountNumber of native species recorded.
Perennial RichnessSpecies richness value and threshold (e.g. 18 >15).
CommentsField notes about site conditions.
PhotosLinked inspection images.

Observations Example

4. Species Sampling Table (Bottom Panel)​

The Observation Species Sampling table provides species-level data tied to observations. Core fields include:

FieldDescription
Observation NameUnique ID linking species records to an observation.
FamilyTaxonomic family (e.g. Fabaceae, Myrtaceae).
Genus–SpeciesScientific species name.
Cover (%)Vegetation cover as a percentage.
Height (m)Average plant height in metres.
Density (plants/ha)Estimated plant density per hectare.
CommentsSpecies-level notes, if recorded.

Usage Scenarios​

  • Ecological Monitoring: Track species composition and diversity changes over time.
  • Ground-Truthing: Validate satellite-based vegetation data with field species surveys.
  • Rehabilitation Review: Assess establishment success and identify soil or site limitations.
  • Compliance & Reporting: Provide detailed, species-level evidence for regulatory submissions.
  • Long-Term Baselines: Build multi-year ecological datasets to support restoration science.