A Multi-scale Approach to Investigating the Wintering Habitat Selection of Red-crowned Cranes in the Yancheng Nature Reserve, China

A B S T R A C T The red-crowned crane (Grus japonensis) is a rare and endangered species that lives in wetland habitats. In this study, we first compared crane habitat selection in December, 2013 and January, 2014 using the Neu method in the Yancheng National Reserve (YNR). We then explored the relative importance of habitats (plot, landscape) and spatial factors on red-crowned crane abundance at multiple scales using regression models and variation partitioning approaches. Our results indicated that seepweed (Suaeda salsa) tidal flats and reed ponds were the favored habitats by cranes in December and January, respectively. The variation partitioning results indicated that plot and landscape factors were the determining factors of crane abundance in December, but plot features were more important in January. Furthermore, the pure and total effects of plot factors, and the combined effects of plot, landscape and spatial factors, increased significantly from December to January. At plot scale, vegetation coverage and road distance were the crucial variables that determine crane abundance in both months. At landscape scale, percentage of reed ponds and percentage of seepweed tidal flats showed a positive independent effect on crane abundance in both months. Percentage of paddy fields was also a significant variable in December, whereas percentage of fishponds was in January. Our study indicated that crane habitat selection and the determining factors changed over time due to food availability and human disturbance (e.g., reed pond and fishpond harvests). Our results encourage the application of partitioning methods in avian ecology because they provide a more in-depth understanding of the importance of different explanatory variables over traditional regression methods. Efforts should be made to strengthen wetland restoration and improve the mitigation of human disturbance in the YNR

Modelling impacts of drivers on biodiversity and ecosystems

Purpose of this chapter: Explores key issues in modelling impacts of changes in direct drivers on biodiversity and ecosystems; and critically reviews major types of models for generating outputs that are either directly relevant to assessment and decision-support activities, or are required as inputs to subsequent modelling of nature’s benefits to people. Key findings: 1-Models of biodiversity and ecosystem function are critical to our capability to predict and understand responses to environmental change; 2- There is a need to match biodiversity and ecosystem function model development to stakeholder and policy needs; 3- Biodiversity and ecosystem modelling depends heavily on our understanding of ecosystem structure, function and process and on their adequate representation in models; 4- Uncertainty in ecosystem dynamics is inherent in ecosystem modelling.EEA Santa CruzFil: Brotons, Lluís. InForest jru. Creaf-Ctfc; EspañaFil: Christensen, Villy. The University of British Columbia; Canadá.Fil: Ravindranath, N. H. India Center for Sustainable Technologies. Indian Institute of Science; India.Fil: Cao, Mingchang. Keqiang Zhao; China.Fil: Chun, Jung Hwa. National Institute of Forest Science, Division of Forest Ecology; Corea del SurFil: Maury, Olivier. Institut de Recherche pour le Développement (IRD); Francia.Fil: Peri, Pablo Luis. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Santa Cruz; Argentina.Fil: Peri, Pablo Luis. Universidad Nacional de la Patagonia Austral; Argentina.Fil: Peri, Pablo Luis. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.Fil: Proença, Vânia. Instituto Superior Tecnico - UNIU Lisboa; Portugal.Fil: Salihoglu, Baris. Middle East Technical University. Institute of Marine Sciences; Turquí

Relationships between Plant Species Richness and Terrain in Middle Sub-Tropical Eastern China

The objective of this research was to study the relation between species richness and topography in the middle sub-tropical area of Eastern China. A species richness survey was conducted along altitude in Kaihua County, Zhejiang Province, Eastern China. Topographic variables, such as altitude, slope, aspect, terrain roughness, relief degree and the topographical wetness index, were extracted from the digital elevation model. The Generalized Additive Model (GAM), the linear model and the quadratic model were used to fit response curves of species richness to topographic variables. The results indicated that altitude and the topographical wetness index have a significant relation to species richness. Species richness has a unimodal response to altitude and a linear response to the topographical wetness index. However, no significant correlations were observed between slope, aspect and species richness. The predicted species richness by GAM is significantly correlated with the observed species richness, whereas the prediction error tends to increase with the increment of species richness. This study furthered insights into the relationship between topography and plants’ diversity in the middle sub-tropical area of Eastern China

Relationships between Plant Species Richness and Terrain in Middle Sub-Tropical Eastern China

The objective of this research was to study the relation between species richness and topography in the middle sub-tropical area of Eastern China. A species richness survey was conducted along altitude in Kaihua County, Zhejiang Province, Eastern China. Topographic variables, such as altitude, slope, aspect, terrain roughness, relief degree and the topographical wetness index, were extracted from the digital elevation model. The Generalized Additive Model ( GAM), the linear model and the quadratic model were used to fit response curves of species richness to topographic variables. The results indicated that altitude and the topographical wetness index have a significant relation to species richness. Species richness has a unimodal response to altitude and a linear response to the topographical wetness index. However, no significant correlations were observed between slope, aspect and species richness. The predicted species richness by GAM is significantly correlated with the observed species richness, whereas the prediction error tends to increase with the increment of species richness. This study furthered insights into the relationship between topography and plants' diversity in the middle sub-tropical area of Eastern China

Percentages of the total variance in red-crowned crane occurrence data explained by plot, patch, and landscape variables at the second hierarchical level; a, b, c are unique effects of plot, patch and landscape variables, respectively; d, e, f, g are fractions indicating their combined effects.

<p>Percentages of the total variance in red-crowned crane occurrence data explained by plot, patch, and landscape variables at the second hierarchical level; a, b, c are unique effects of plot, patch and landscape variables, respectively; d, e, f, g are fractions indicating their combined effects.</p

The landscape index of red-crowned crane suitable habitat in the YRDNR.

<p>North: northern part of the YRDNR; South: southern part of the YRDNR; Total: the entire reserve; CA: total suitable habitat area; PLAND: proportion of suitable habitat area; AREA_MN: average patch area of suitable habitat; PG: protection gap area in the YRDNR; PPG: percentage of protection gap area with total suitable habitat in the YRDNR.</p><p>The landscape index of red-crowned crane suitable habitat in the YRDNR.</p

Percentages of the total variance in red-crowned crane occurrence data explained by habitat and spatial variables at the first hierarchical level.

<p>(a) The variance explained by habitat variables; (b) the variance explained by spatial variables; and (c) the variance explained by combination of habitat and spatial variables.</p

Habitat suitability map and protection gap for red-crowned cranes used by the final autologistic model.

<p>Red: suitable habitat for the red-crowned crane in the core zone of the YRDNR. Green: suitable habitat for the red-crowned crane outside the core zone of the YRDNR. Blue: Yellow River in the YRDNR. Light gray: experimental zones of the YRDNR. Gray: buffer zones of the YRDNR. Dark gray: core zones of the YRDNR.</p

Location of the Yellow River Delta Nature Reserve in China.

<p>'Absence' and 'presence' indicate the absence and presence sampling points of red-crowned cranes. Blue: Yellow River in the YRDNR. Light gray: experimental zones of the YRDNR. Light green: buffer zones of the YRDNR. Dark green: core zones of the YRDNR.</p

Variable parameters for the final autologistic model with a neighborhood size of 8 km.

<p>**: p-value<0.01;</p><p>***: p-value <0.001</p><p>Variable parameters for the final autologistic model with a neighborhood size of 8 km.</p