Jan Leps, Dept of Botany, University of South Bohemia, České Budejovice, Czech Republic Biodiversity of seminatural meadows under various management regimes:

Slides:



Advertisements
Similar presentations
Computational Statistics. Basic ideas  Predict values that are hard to measure irl, by using co-variables (other properties from the same measurement.
Advertisements

An Introduction to Multivariate Analysis
LTER Planning Process Science Task Force (STF) Report to NSF September 2005.
Experiments evaluated using multivariate methods.
Effect of mowing, fertilization and dominant removal on ecosystem characteristics and species trait composition. Jan Leps, Jiri Dolezal, and David Zeleny.
1. Review- What is Science Explain- What kinds of understandings does science contribute about the natural world Form an Opinion- Do you think that scientists.
Written Reports Suggestions for Good Scientific Writing John E. Silvius Professor of Biology Cedarville University.
Properties of Community Data in Ecology Adapted from Ecological Statistical Workshop, FLC, Daniel Laughlin.
Unit 1 Biology Notes Characteristics of Life
The Analysis of Variance
Today Concepts underlying inferential statistics
Quantitative Genetics
Introduction Subalpine meadows play a crucial role in species diversity, supporting many endangered species of plant and wildlife. Subalpine meadows play.
BIODIVERSITY + EVOLUTION Chapter 4. BRIDGING THE GAP  Biodiversity is all of the differences amongst the living world.  So how do topics already covered.
Choosing and using statistics to test ecological hypotheses
Inference for Linear Regression Conditions for Regression Inference: Suppose we have n observations on an explanatory variable x and a response variable.
Tuesday 11:00 – 1:50 Thursday 11:00 – 1:50 Instructor: Nancy Wheat Ecology Bio 47 Spring 2015.
Species Abundance and Diversity
MANAGEMENT AND ANALYSIS OF WILDLIFE BIOLOGY DATA Bret A. Collier 1 and T. Wayne Schwertner 2 1 Institute of Renewable Natural Resources, Texas A&M University,
Effects of Intraspecific Competition on Varying Groups of Marigolds Tiffany Landis Microbiology Major Tennessee Technological University Cookeville, TN.
Introduction Stomatal conductance regulates the rates of several necessary processes in plants including transpiration, carbon dioxide assimilation, and.
Mechanism of species coexistence Why there are so many species in communities? How are they able to escape the competitive exclusion? (i.e. species already.
DIRECT ORDINATION What kind of biological questions can we answer? How can we do it in CANOCO 4.5?
The Scientific Method Honors Biology Laboratory Skills.
How do forest ecosystems respond to environmental change?
From: McCune, B. & J. B. Grace Analysis of Ecological Communities. MjM Software Design, Gleneden Beach, Oregon
1 Species Abundance and Diversity. 2 Introduction Community: Association of interacting species inhabiting some defined area.  Community Structure includes.
Educational Research: Competencies for Analysis and Application, 9 th edition. Gay, Mills, & Airasian © 2009 Pearson Education, Inc. All rights reserved.
Ecology: Community Structure & Ecosystem Services David Mellor, PhD Citizen Science Coordinator Virginia Master Naturalists.
Experimental design. Experiments vs. observational studies Manipulative experiments: The only way to prove the causal relationships BUT Spatial and temporal.
PROJECT SUMMARY Low-input high-diversity (LIHD) grasslands are a promising system for biofuel production as they provide additional environmental benefits.
All living things have cells/contain DNA
Introduction: Globally, atmospheric concentrations of CO 2 are rising, and are expected to increase forest productivity and carbon storage. However, forest.
Constrained ordinations Dependence of multivariate response on one or many predictors.
How Plants Grow & Respond to Disturbance. Succession & Disturbance  Community change is driven by successional forces: Immigration and establishment.
Site Description This research is being conducted as a part of the Detritus Input and Removal Treatments Project (DIRT), a cross-continental experiment.
Page 1 We will cover: Data Tables Line Graphs Bar Graphs Circle Graphs We will cover: Data Tables Line Graphs Bar Graphs Circle Graphs.
ITEC6310 Research Methods in Information Technology Instructor: Prof. Z. Yang Course Website: c6310.htm Office:
Response of Luzula arctica and Luzula confusa to warming in Barrow and Atqasuk, Alaska Kelseyann Kremers and Dr. Robert D. Hollister Grand Valley State.
Experimental design.
Copyright © 2013, 2009, and 2007, Pearson Education, Inc. Chapter 14 Comparing Groups: Analysis of Variance Methods Section 14.3 Two-Way ANOVA.
Figure 7.1. Variation in weed species composition in relation to crop grown in the four annual cropping systems of the Main Cropping System Experiment.
Repeated undersowing of clover in organic cereal production. Nutrient dynamics and sustainability. Anne-Kristin Løes, Bioforsk Organic Food and Farming.
Species Abundance and Diversity
Prediction models perform better when including transition zones Sophie Vermeersch Plant Science and Nature Plant Science and Nature Management, Department.
mQ OBJECTIVES The student should be able to: 1.list and describe the steps of the scientific method 2.define.
Diversity Productivity Relationships Species Richness Seminar October 21, 2003.
Page 1 We will cover: Data Tables Line Graphs Bar Graphs Circle Graphs We will cover: Data Tables Line Graphs Bar Graphs Circle Graphs.
Results: Tables and Figures. Tables and Figures When to use what? Text: for simple results E.g. Seed production was higher for plants in the full-sun.
Above and Below ground decomposition of leaf litter Sukhpreet Sandhu.
Determining Factors that Reflect Aphid Presence Eco-Informatics Summer Institute 2007 Genevieve Layman Sean Moore Elizabeth Borer.
Plant functional trait expression in the Rengen Grassland Experiment (RGE) Jürgen Schellberg 1, Katharina Brüne 1 and Michal Hejcman 2 1 University of.
Supplementary Material for Chapter 14 Ants, Elephants, and Experimental Design: Understanding Science and Examining Connections between Species Interactions.
Jan Leps, Dept of Botany, University of South Bohemia, České Budejovice, Czech Republic Biotic and abiotic effects on species and functional trait composition:
Rasheed Gibson, Jesse Raike, Ethel Carrillo, Courtney Helmig
Let’s Organize the Data!
Statistics review Basic concepts: Variability measures Distributions
Department of Bioscience
Graphing in Science Graphs are pictures of you data and can reveal patterns and trends in data.
The Scientific Method.
The Scientific Method.
Empirical studies testing the determinants of community structure
Null models in community ecology
Experimental design.
Presentation transcript:

Jan Leps, Dept of Botany, University of South Bohemia, České Budejovice, Czech Republic Biodiversity of seminatural meadows under various management regimes: a 16 years experimental study

OHRAZENI – a seminatural meadow Regular mowing ceased in late eighties

Molinia caerulea Nardus stricta

Species diversity and “interesting plants” (e.g. red list species) concentrated in “traditional”, i.e. mown, unfertilized Dactylorhiza majalis Senecio rivularis

Carex pulicaris C. hartmanii 14 Carex species

Scorzonera humilis

Myosotis nemorosa

Factorial experiment, 3 replications Mowing (once a year, in June) Ferilization (65 [50] g of commercial NPK/m % N (nitrate and ammonium), 19% P (as P 2 O 5 ) and 19% K (as K 2 O)) Dominant (i.e. Molinia caerulea) removal (in spring 1995, but re-weeding necessary time from time) Yielding 24 plots, 2m × 2m each Central 1m x 1m sampled, followed by detailed analysis of 50cm × 50cm grid of 10cm × 10 cm – including seedling counts Experiment started 1994, baseline data available

Ohrazení (

Detailed recording of vegetation in all the 16 years Sprouts of woody plants removed

Response of plant community to the treatments in terms of Species richness and composition Species traits –Methodological notes: how to analyse the species traits?

Mown - unfertilized (=traditional)

Mown - unfertilized & Molinia removed

Mown - fertilized (=intensive)

Mown - fertilized & Molinia removed

Unmown - unfertilized (=abandoned)

Unmown - unfertilized & Molinia removed

Unmown - fertilized (=abandoned eutrofized meadow)

Unmown - fertilized & Molinia removed

Problem for interpretation, in late years, Molinia removal would have little effect in fertilized mown (as it have very low cover also in the control plots) Error bars =95% confidence intervals

Species richness

In mown plots, increase of the number of species during first six years, regardless of removal of Molinia, in unmown plots, removal has positive effect on species richness NSP of vascular plants per m 2

In unmown plot, continuous decrease. Initial positive effect of removal ceased after 10 yrs. In mown plots, initial increase (5 years) followed by decrease, no effect of removal.

Take home message Increase in soil nutrients can lead to competitive exclusion - nevertheless, in community of established perennial plants, the exclusion can take rather long (six years in our case).

Species richness dynamics depends on spatial scale Repeated measures ANOVA 3 main plot factors (Mowing, Fertilization, Removal) 2 Rep Mes faktors – year and plot size (from 10x10cm 2 to 50x50cm 2 ) Number of species log transformed – i.e. relative change of the richness Very long ANOVA table

Only selected terms will be interpretted Many interactions significant, effects are not additive

During the first eight years, number of species increases on small plots, but is constant on larger plots

Positive effect of mowing is most pronounced on the small spatial scale Data from 2008, averaged over fertilization and removal treatments 10x10 50x50

In small plots, the effect of removal is more pronounced in non-fertilized plots, in large plots, in fertilized plots

Seedling number - Average over

Species composition

DCA - Molinia is passive species - log transformed cover. Starting points in 1994 show the random variability - the divergence of trajectories show the differentiation according to treatments between 1994 and Centroids of Year x Treatment

Principal response curves triangles - mown circles unmown full symbol - fertil. open symbol - unfert. solid line - control broken l. - removal

Principal response curves Multivariate counterpart of Repeated measurement ANOVA - the first axis, which is plotted against the time, captures the main differentiation among categories of YEAR * TREATMENT interaction The common temporal trend is subtracted from the data - YEAR as covariable(s) The horizontal axis corresponds to the control (in our case, unmown, unfertilized, no removal)

Principal response curves triangles - mown circles unmown full symbol - fertil. open symbol - unfert. solid line - control broken l. - removal

The species lost from the community under various management types Are not a random subsample, but species with specific ecological characteristics (contrary to Hubbell’s theory) IMPORTANCE OF SPECIES TRAITS (mostly based on 2004 biomass data)

Two approaches to analyse species trait response Species based: can we predict the species response on the basis of its traits? –Traits are predictors of species response (trait value is a fixed characteristics of individual species) Community (plot) based: how do the community (weighted) average [or variability] respond to environmental characteristics? –Traits (averages, variability) are response. Trait plasticity can be evaluated

Species based approach 1. Calculate the environmental response for each individual species (we have used constrained ordination framework / RDA) 2. Predict the species response on the basis of species traits (various regressions, regression trees)

Species response to fertilization (RDA score, positive values mean that the species gains from fertilization)

Species response to mowing (RDA score, positive values mean that the species is supported by mowing)

Regression tree prediction of response to mowing Unassisted seed disperals Tall plants rosettes

Potential height is good predictor of response to fertilization and mowing With increasing nutrients, the plants are released from competition for nutrients, but simultaneously, the importance of competition for light increases - the taller plants are in advantage. The taller plants are harmed more by mowing Higher asymmetry of competition for light (in comparison with competition for nutrients) explains decline in species richness.

Methodological note 1 Regression tree – highly non-parametric regression approach –Well designed to account for the non-additivity The tall plants are always harmed by mowing In a group of “not so tall” plants, also other factors play a role: e.g. those with a rosette respond more positively

Methodological note 2 Species based approach – species are considered independent observations – problem of phylogenetic relatedness –Do we need phylogenetic correction? –To which extend could be the similarity of species responses explained by the similarity of their traits, and to which extend by the phylogenetic relatedness?

The role of the dominant (Molinia caerulea) consequences of its removal

Litter - Molinia produces large amount of slowly decaying litter. Its removal causes decrease of litter amount (with exception of mown & fertilized conditions) As a consequence, seedling recruitment is supported by Molinia removal. Molinia competes also after its death.

In the presence of Molinia, the peak of biomass is shifted from June to August (and is slightly higher) - only weak statistical support Unfertilized plots only shown The presence of a single species can considerably shift the seasonal biomass dynamics

In mown plots, the removed Molinia is replaced by other grasses, however, there is no other grass which would be able to replace Molinia in unmown plots. Take home message: The dominant species (when removed) is not always replaced with the species from the same functional group.

Molinia is rather extreme in some traits, e.g. Very late phenology Very slow rate of litter decomposition Very deep and strong roots Extremely constant biomass over the years It is very likely that the presence/absence of Molinia has crucial role in various ecosystem processes (e.g. nutrient cycling)

Iva Spackova, Alena Vitova, Petr Macek, Francesco de Bello, Jiri Dolezal, Vojtech Lanta, Jonathan Titus, Eva Chaloupecka, Katerina Palkova, David Zeleny Thanks for the help Thanks for money Czech Science Foundation (GACR) Framework V – VISTA project

Thank you for your attention