Dataset Information

Seasonality in spatial distribution: Climate and land use have contrasting effects on the species richness of breeding and wintering birds.

ABSTRACT: Aim:Many studies have examined large-scale distributions of various taxa and their drivers, emphasizing the importance of climate, topography, and land use. Most studies have dealt with distributions over a single season or annually without considering seasonality. However, animal distributions and their drivers can differ among seasons because many animals migrate to suitable climates and areas with abundant prey resources. We aim to clarify seasonality in bird distributions and their drivers. Location:Japan. Methods:We examined the effects of climate (annual mean temperature, snow depth), topography (elevation), and land use (extent of surrounding habitat) on bird species richness, in the breeding and wintering seasons separately, using nationwide data (254 forest and 43 grassland sites, respectively). We separately analyzed the species richness of all species, residents, short-, and long-distance migrants in forests and grasslands. Results:In the breeding season, the annual mean temperature negatively affected all groups (except for forest and grassland residents), and the extent of surrounding habitat positively affected many groups. By contrast, in the wintering season, temperature positively affected all groups (except for forest residents), and the extent of surrounding habitat positively affected only grassland long-distance migrants. In both seasons, the species richness of forest and grassland residents was high in regions of moderate and high temperature, respectively. Moreover, snow depth negatively affected all forest groups in the wintering season. Mapping expected species richness suggested that regions with different climates served as habitats for different groups during different seasons. Main conclusions:All regions were important bird habitats depending on the season, reflecting the contrasting effects of temperature across seasons. In the breeding season, surrounding land use was also an important driver. To understand the seasonal role that each region and environment plays in maintaining species/communities, a large-scale study considering both environmental seasonality and species distribution is needed.

SUBMITTER: Kawamura K

PROVIDER: S-EPMC6636198 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Seasonality in spatial distribution: Climate and land use have contrasting effects on the species richness of breeding and wintering birds.

Kawamura Kazuhiro K Yamaura Yuichi Y Senzaki Masayuki M Ueta Mutsuyuki M Nakamura Futoshi F

Ecology and evolution 20190620 13

<h4>Aim</h4>Many studies have examined large-scale distributions of various taxa and their drivers, emphasizing the importance of climate, topography, and land use. Most studies have dealt with distributions over a single season or annually without considering seasonality. However, animal distributions and their drivers can differ among seasons because many animals migrate to suitable climates and areas with abundant prey resources. We aim to clarify seasonality in bird distributions and their d ...[more]

PMID: 31346421

Similar Datasets

Project description:IntroductionKnowledge of species richness patterns and their relation with climate is required to develop various forest management actions including habitat management, biodiversity and risk assessment, restoration and ecosystem modelling. In practice, the pattern of the data might not be spatially constant and cannot be well addressed by ordinary least square (OLS) regression. This study uses GWR to deal with spatial non-stationarity and to identify the spatial correlation between the plant richness distribution and the climate variables (i.e., the temperature and precipitation) in a 1° grid in different biogeographic zones of India.MethodologyWe utilized the species richness data collected using 0.04 ha nested quadrats in an Indian study. The data from this national study, titled 'Biodiversity Characterization at Landscape Level', were aggregated at the 1° grid level and adjudged for sampling sufficiency. The performances of OLS and GWR models were compared in terms of the coefficient of determination (R2) and the corrected Akaike Information Criterion (AICc).Results and discussionA comparative study of the R2 and AICc values of the models showed that all the GWR models performed better compared with the analogous OLS models. The climate variables were found to significantly influence the distribution of plant richness in India. The minimum precipitation (Pmin) consistently dominated individually (R2 = 0.69; AICc = 2608) and in combinations. Among the shared models, the one with a combination of Pmin and Tmin had the best model fits (R2 = 0.72 and AICc = 2619), and variation partitioning revealed that the influence of these parameters on the species richness distribution was dominant in the arid and the semi-arid zones and in the Deccan peninsula zone.ConclusionThe shift in climate variables and their power to explain the species richness of biogeographic zones suggests that the climate-diversity relationships of plants species vary spatially. In particular, the dominant influence of Tmin and Pmin could be closely linked to the climate tolerance hypothesis (CTH). We found that the climate variables had a significant influence in defining species richness patterns in India; however, various other environmental and non-environmental (edaphic, topographic and anthropogenic) variables need to be integrated in the models to understand climate-species richness relationships better at a finer scale.

Project description:Studying the pattern of species richness is crucial in understanding the diversity and distribution of organisms in the earth. Climate and human influences are the major driving factors that directly influence the large-scale distributions of plant species, including gymnosperms. Understanding how gymnosperms respond to climate, topography, and human-induced changes is useful in predicting the impacts of global change. Here, we attempt to evaluate how climatic and human-induced processes could affect the spatial richness patterns of gymnosperms in China. Initially, we divided a map of the country into grid cells of 50 × 50 km2 spatial resolution and plotted the geographical coordinate distribution occurrence of 236 native gymnosperm taxa. The gymnosperm taxa were separated into three response variables: (a) all species, (b) endemic species, and (c) nonendemic species, based on their distribution. The species richness patterns of these response variables to four predictor sets were also evaluated: (a) energy-water, (b) climatic seasonality, (c) habitat heterogeneity, and (d) human influences. We performed generalized linear models (GLMs) and variation partitioning analyses to determine the effect of predictors on spatial richness patterns. The results showed that the distribution pattern of species richness was highest in the southwestern mountainous area and Taiwan in China. We found a significant relationship between the predictor variable set and species richness pattern. Further, our findings provide evidence that climatic seasonality is the most important factor in explaining distinct fractions of variations in the species richness patterns of all studied response variables. Moreover, it was found that energy-water was the best predictor set to determine the richness pattern of all species and endemic species, while habitat heterogeneity has a better influence on nonendemic species. Therefore, we conclude that with the current climate fluctuations as a result of climate change and increasing human activities, gymnosperms might face a high risk of extinction.

Dataset Information

Seasonality in spatial distribution: Climate and land use have contrasting effects on the species richness of breeding and wintering birds.

Publications

Seasonality in spatial distribution: Climate and land use have contrasting effects on the species richness of breeding and wintering birds.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets