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Record W4394065591 · doi:10.6084/m9.figshare.12398051

Impact of adjacent land use on the ecological condition of riparian habitats: The relation between condition and vegetation properties

2020· dataset· en· W4394065591 on OpenAlexaboutno aff
Isela Zermeño‐Hernández, Moisés Méndez‐Toribio

Bibliographic record

VenueFigshare · 2020
Typedataset
Languageen
FieldAgricultural and Biological Sciences
TopicSoil erosion and sediment transport
Canadian institutionsnot available
Fundersnot available
KeywordsRiparian zoneHabitatVegetation (pathology)EcologyRelation (database)GeographyEnvironmental scienceLand useBiology

Abstract

fetched live from OpenAlex

METHODSSampling sitesSampling sites were established along the riverbank by considering three different adjacent land-use practices, including the following: forest (FOR, N=3), agricultural (AGR; N=5 sites) and urban areas (URB; N=4). Riparian vegetation in the FOR land-use type was in contact with old-growth remnants of tropical deciduous forest or secondary vegetation present for a length of at least 500 m perpendicular to the riparian vegetation. Crops adjacent to the AGR sampling sites were maize (Zea mays), sorghum (Sorghum bicolor), grass (Cenchrus ciliaris) and/or strawberry (Fragaria ananassa). Sites in the URB were located within semi-urbanized rural as well as urban areas. The different vegetation sampling sites were established far from rapidly flowing sections and/or meanders in the river to avoid variation in the vegetation structure caused by these ecological factors. Riparian condition indexThe ecological condition of each sampling site along the riverbank was determined by using a previously tested riparian condition index (RCI), with modifications according to our study site conditions (Jansen & Robertson, 2001). Each sampling unit consisted of a 500-m transect established along the riverbank and randomly located on one side of the river. The RCI considered six different biophysical, vegetative and landscape traits (subindex): (1) habitat continuity and width (HABITAT), (2) vegetation cover and structural complexity (COVER), (3) bank stability (BANKS), (4) standing and fallen debris (DEBRIS), (5) dominance of native vegetation versus exotic (NATIVES), and (6) the natural regeneration of woody seedlings (REGENERATION). As shown in Table 1, 17 indicators of the riparian condition were measured in the field to quantify the contribution of each subindex score to the overall condition index value. Each indicator was weighted according to its ecological importance in each subindex, as given in Table 1. A lower value was assigned to the NATIVE and REGENERATION subindices. In the case of NATIVES, there is little information regarding how exotic plant species may perform ecological functions compared with the native plant species they have replaced (Jansen & Robertson, 2001). Therefore, the relative contribution of NATIVES in our index of ecosystem condition was lower than other subindices (Table 1 in the source publication). A low value was also given to the REGENERATION subindex since the presence of common species is highly variable across space and time and depends on a complex interaction of hydrologic and geomorphic processes that shape seedling establishment (González et al., 2018). Hence, this subindex may not be as relevant for the ecological condition assessment of riparian forests as other ecological indicators. For example, the incidence of rocks is considered an important component for the maintenance and stability of the riverbanks and for erosion reduction, so although Jansen and Robertson (2001) did not use this indicator in their index, the presence of rocks was included here as a useful indicator for assessing bank stability (e.g., Heartsill-Scalley & Aide, 2003). The evaluations for each indicator were averaged for each site, scored, and weighted, then added to obtain a final score per site. Potential scores were from 0 (worst condition) to 50 (best condition). To summarize the results, the scores of the RCI were grouped into five categories: very poor condition (<25); poor condition (>25-<30); regular condition (> 30 - < 35); good condition (> 35 - < 40); and excellent condition (> 40). Evaluations methodsThe same observer performed all the evaluations regarding the ecological conditions to reduce any bias. Observations took place before the rainy season. Thirteen of the 17 riparian indicators were measured systematically in four 20 × 5-m perpendicular riverbank transects. These transects were located 125 m apart along the sampling unit to measure: (i) the canopy cover (%), which consisted of all trees > 5 m height; (ii) the understory cover, which included herbs, grasses, shrubs and juvenile trees from 1 to 5 m in height; (iii) the ground cover or low understory stratum, including herb and grasses < 1 m tall; (iv) the number of layers of vegetation; (v) the stability of the riverbank; (vi) the presence of boulders and stones; (vii) the incidence of standing dead trees; (viii) the density of terrestrial woody debris (> 10 cm diameter); (ix) the percentage of native species in the canopy, (x) the percentage of native species in the understory; (xi) the percentage of native species in the ground stratum; (xii) canopy regeneration measured as the density of tree seedlings ≥30 cm and ≤ 100 cm in height in the understory; and (xiii) understory regeneration referred to the density of shrubs seedlings ≥30 cm and ≤ 100 cm in height. Furthermore, (xiv) the width of the riparian vegetation (on the side of the river being assessed) was measured at 10 evenly spaced points within each sampling unit; (xv) the leaf litter cover on the ground was estimated at 10 perpendicular transects (20 × 5 m) to the riverbank. Finally, (xvi) the longitudinal continuity of the riparian vegetation, and (xvii) the aquatic woody debris were assessed once through the entire 500-m sampling unit. For longitudinal continuity, a diagram was drafted where discontinuities in vegetation cover were identified along the 500-m sampling unit. For aquatic woody debris, four categories of tree and branch density along the 500-m bank section were made (Table 1 in the source publication). Assessment of vegetation propertiesAt each of the 12, 0.1-ha sites, we established 10, 20 × 5 m (100 m2) transects on one side of the river, which were randomly positioned. At each sampling site, we recorded the density of stems and individuals of trees and shrubs with trunks ≥ 2.5 cm in diameter at breast height (DBH at 1.30 m); basal area (m2); mean height, from the maximum height recorded for all the individuals and the number of multi-stemmed individuals. Species were identified to the lowest taxonomic level in the field, and botanical samples were taken to an herbarium for those unidentified species. We recorded the total number of species (S) and the number of species that occur in a only one sample (unique number of species; U) (Magurran & McGill, 2011). One nonparametric richness index (the incidence-based cover estimator, ICE) and alpha diversity indices were also obtained. The Shannon index (H´), calculates the diversity of a site considering the proportional abundance of its species. The Simpson index (D), is an index of dominance where the diversity of the sample declines as the value of D increases. All indices were calculated with the EstimateS 9.1.0 program (Colwell & Elsensohn, 2014) by using in each case 200 permutations without replacement. The level of patchiness was set to 0 in order to prevent bias in estimating species richness because of clustering of the species themselves (Colwell & Coddington, 1994).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Dataset · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.107
GPT teacher head0.266
Teacher spread0.159 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreDataset

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2020
Admission routes1
Has abstractyes

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