Visualising the future of our planet – Can we do better than heat maps? Museo Nacional de Ciencias Naturales (CSIC), Spain

Slides:



Advertisements
Similar presentations
An introduction to climate change vulnerability assessments Stuart Butchart, BirdLife International
Advertisements

A Climate-based Interpretation of Limber Pine Management Scenarios in Rocky Mountain National Park Contributors: Bill Monahan, Tammy Cook, Jeff Connor,
WP3 Biomapping results to date WP3: NRM, CDF, CEFAS, DINARA, WCS Additional input: WP1, AquaMaps workgroup.
Contrasting tissue strategies explain functional beta diversity in Amazonian trees C. Fortunel, C.E.T. Paine, N. Kraft, P.V.A. Fine, C. Baraloto*
Tomer Gueta, Avi Bar-Massada and Yohay Carmel Using GBIF data to test niche vs. neutrality theories at a continental scale, and the value of data cleaning.
Pearson & Dawson Quiz 1. What is a bioclimatic envelope? 2. How might bioclimatic envelope models be useful for invasive species management? 3. List and.
Essential Biodiversity Variables for Global Earth Observation Henrique M. Pereira Centre for Environmental Biology Faculdade de Ciências da Universidade.
What is macroecology? Macroecology deals with ecological patterns and processes at various scales In particular macroecology tries to identify and to explain.
Commonness and rarity in species distribution Sophia Qian Niu Graduate seminar: Lost in space.
Fundamental patterns of macroecology Patterns related to the spatial scale Patterns related to the temporal scale Patterns related to biodiversity.
Carbon Cycle and Ecosystems Important Concerns: Potential greenhouse warming (CO 2, CH 4 ) and ecosystem interactions with climate Carbon management (e.g.,
Evolution of Biodiversity
Biogeography Chapters Conservation Biogeography and a Changing Environment Acalypta Illustration by Nancy Lowe.
Algorithm Development for Vegetation Change Detection and Environmental Monitoring Louis A. Scuderi 1, Amy Ellwein 2, Enrique Montano 3 and Richard P.
Corals and sea anemones on line: a functioning biodiversity database D. G. Fautin R. W. Buddemeier University of Kansas: Department of Ecology and Evolutionary.
Congruence Among Taxonomic Groups Biol2559/22/2003 Brooke Wheeler.
Bird Trip: Me Sunday April 29 or May 6? Need binoculars? Transportation?
A COMPARISON OF APPROACHES FOR VERIFYING SOUTHWEST REGIONAL GAP VERTEBRATE-HABITAT DISTRIBUTION MODELS J. Judson Wynne, Charles A. Drost and Kathryn A.
Vogelwarte.ch BIO 232 Macroecology, niche evolution and climate change by Niklaus. E. Zimmermann Today: Damaris Zurell.
An On-line Atlas of Marine Diversity and a growing inventory of others.
Forest Structure and Distribution across the Geographic Range of the Giant Panda Up-scaling from Plots to the Entire Region Jianguo (Jack) Liu (Michigan.
Biodiversity and Climate Change
Integrating Global Species Distributions, Remote Sensing and Climate Station Data to Assess Biodiversity Response to Climate Change Adam Wilson & Walter.
Assessing conservation priorities: the African Vertebrates Databank (AVD) Istituto di Ecologia Applicata Via L.Spallanzani, Rome ITALY
Butterflies as a model system to understand the interaction of landscape and climate Leslie Ries National Socio-environmental Synthesis Center (SESYNC),
Biogeography Chapter 1.
RAPID ASSESSMENT PROGRAM (RAP) Terrestrial Ecosystems Freshwater Ecosystems Marine Ecosystems.
The 2010 Red List of Finnish species: the assessment work in practice Ilpo Mannerkoski Finnish Environment Institute Syktyvkar
Nick Isaac, Tom August & Gary Powney Trends in British Biodiversity since
Rio de Janeiro Earth Summit Signatories pledged to establish a system of protected areas Reserves should be Comprehensive Representative Adequate Flexible.
SERVIR-AFRICA: an overview André Kooiman International workshop on higher resolution Land cover mapping for the African continent UNEP, 27 June 2013.
Lecture 13 Biodiversity I.What is Biological Diversity? II.Latitudinal and Altitudinal Gradients III.Geographic Controls on Diversity A.Historical Theories.
Adaptation of Biodiversity to Climate Change in southern Africa CSIR, National Botanical Institute, University of Pretoria & Kruger National Park.
Getting Ready for the Future Woody Turner Earth Science Division NASA Headquarters May 7, 2014 Biodiversity and Ecological Forecasting Team Meeting Sheraton.
Using historic data sources to calibrate and validate models of species’ range dynamics Giovanni Rapacciuolo University of California Berkeley
Survey Priorities Discussion Group Participants: Wang Hao, Cristiano, Megan, Wiggy, Curtis, Simon, Henni, Kristen, Naamal, Matt, Lisa, Leeanne, Tom L.
Climate change, ecological impacts and managing biodiversity Mark W. Schwartz
Muthama Muasya University of Cape Town Application of DNA barcoding in plant taxonomy, Eastern Africa Experience.
Jake F. Weltzin, Kathryn Thomas, Brian Haggerty, Theresa Crimmins, Ellen Denny, Abe Miller-Rushing, Alyssa Rosemartin The USA National Phenology.
How many species are there, globally? Range of estimates: 2 – 100 million Best estimate: 10 million 1.4 – 2 million species have a name. An estimated 97%
Trees, taxonomy & location: mapping phylogeography using Biodiverse Dan Rosauer & Shawn Laffan University of New South Wales & Centre for Plant Biodiversity.
Candidate KBA Identification: Modeling Techniques for Field Survey Prioritization Species Distribution Modeling: approximation of species ecological niche.
Components of plant species diversity in the New Zealand forest Jake Overton Landcare Research Hamilton.
Jake F. Weltzin Mark D. Schwartz In-situ validation of land- surface phenology A framework for involvement with USA National Phenology Network.
Definition of an Observation In general, an observation represents the measurement of some attribute, of some thing, at a particular time and place. Observations.
Conservation management for an uncertain future Mike Morecroft.
Niches, Interactions and Movements. Calculating a Species Distribution Range Jorge Soberon M. A. Townsend Peterson.
Integrating remotely sensed data and ecological models to assess species extinction risks under climate change Richard Pearson (AMNH) Resit Akçakaya (Stony.
Macroecology & Conservation Unit
Ocean Biogeographic Information System Edward Vanden Berghe.
Evolution of Biodiversity
Remote-sensing and biodiversity in a changing climate Catherine Graham SUNY-Stony Brook Robert Hijmans, UC-Berkeley Lianrong Zhai, SUNY-Stony Brook Sassan.
Ecological Niche Modeling Conceptual Workflows Deana Pennington University of New Mexico December 16, 2004.
Environmental Information Infrastructure John R. Busby ERIN, Environment Australia.
Figures from Chapter 4. Figure 4.1 Cumulative curves of species description and fitted models of four of the five size...as zero. From Medellín & Soberón.
AUSTRALIA’S VIRTUAL HERBARIUM A national collaborative model for integrated access to distributed biological information Australian National Herbarium.
Remote Sensing and Avian Biodiversity Patterns in the United States Volker C. Radeloff 1, Anna M. Pidgeon 1, Curtis H. Flather 2, Patrick Culbert 1, Veronique.
Extracting time series from occurrence records Nick Isaac Cross-taxa analysis of community dynamics: 4/11/15.
Conserving Europe’s plant genetic resources for use now and in the future PGR Forum European crop wild relative diversity assessment and conservation forum.
1 Occupancy models extension: Species Co-occurrence.
Inspiring and Engaging the Public Towards a Shared Understanding and Sense of Ownership of Freshwater Ecosystems A. Mauroner a, I.J. Harrison ab, & M.
Multiple Season Study Design. 2 Recap All of the issues discussed with respect to single season designs are still pertinent.  why, what and how  how.
Module 4 – Biodiversity By Ms Cullen. Terminology Try and define the following terms used when studying the environment.
Single Season Study Design. 2 Points for consideration Don’t forget; why, what and how. A well designed study will:  highlight gaps in current knowledge.
RCN Development of an Online Database to Enhance the Conservation of SGCN Invertebrates in the Northeastern Region James W. Fetzner Jr. & John.
Organising data to represent biodiversity
EC FP7 - Cooperation Theme 6: Environment (incl. climate change)
Improving the forecast for biodiversity under climate change
Walter Jetz, Jana M. McPherson, Robert P. Guralnick 
Improving the forecast for biodiversity under climate change
Presentation transcript:

Visualising the future of our planet – Can we do better than heat maps? Museo Nacional de Ciencias Naturales (CSIC), Spain / Joaquín Hortal Microsoft Research Ltd. Cambridge, 16-17/2012

Biodiversity information Quality (and quantity) of data: Wallacean Shortfall Mapping unknown species distributions Mapping ignorance

Imagine a magnificent and omniscient GIS for all the Earth’s living species, with the capacity to display any level of the Linnaean hierarchy on any spatial scale, for any season of the year. biodiversity and biogeography Colwell & Coddington Phil Trans Roy Soc B 1994

digitize available distributional information:  Natural History collections ▪ Institutional (Museums, Herbaria) ▪ Private collections gathering biodiversity information

digitize available distributional information:  Natural History collections ▪ Institutional (Museum, Herbaria) ▪ Private collections  Literature gathering biodiversity information

digitize available distributional information:  Natural History collections ▪ Institutional (Museums, Herbaria) ▪ Private collections  Literature  ad hoc surveys gathering biodiversity information

integrate all information on the distribution of biodiversity biodiversity databases Map of Life ; ;

Adequate distribution data is lacking for many of the known species and higher taxa (Lomolino 2004) Whittaker et al. Div Distr 2005; Hortal et al. Conserv Biol 2007 Wallacean shortfall

Adequate distribution data is lacking for many of the known species and higher taxa (Lomolino 2004) 1,131 species 1,084,971 records 960 records/species 128 records/grid cell Tenerife seed plants Whittaker et al. Div Distr 2005; Hortal et al. Conserv Biol 2007 Wallacean shortfall

Tenerife seed plants Records Observed Richness Whittaker et al. Div Distr 2005; Hortal et al. Conserv Biol 2007 Wallacean shortfall Adequate distribution data is lacking for many of the known species and higher taxa (Lomolino 2004)

taxonomic error Lozier et al J Biogeogr 2009

taxonomic error Lozier et al J Biogeogr 2009

taxonomic bias Baselga et al Biodiv Conserv 2007

recorder’s home range hotspots spatial bias Dennis & Thomas J Insect Conserv 2000

accessibility: ‘roadside bias’ spatial bias Kadmon et al Ecol Appl 2003; Hurlbert & Jetz PNAS 2007

butterflies scarab dung beetles bias differs between groups Hortal et al Biod Conserv 2001; Hortal et al Ecography 2004 spatial bias

Onthophagus fracticornis Lobo et al. Div Distr 2007 temporal bias

Historical survey process has been incomplete and biased:  Taxonomic bias  Temporal bias  Spatial bias quality of distributional data Pineda & Lobo J Anim Ecol 2009

Historical survey process has been incomplete and biased:  Taxonomic bias  Temporal bias  Spatial bias quality of distributional data Pineda & Lobo J Anim Ecol 2009 Current biodiversity picture depends on the survey process

Historical survey process has been incomplete and biased:  Taxonomic bias  Temporal bias  Spatial bias quality of distributional data Pineda & Lobo J Anim Ecol 2009 Current biodiversity picture depends on the survey process Current knowledge on species distribution patterns may depend on survey unevenness rather than on their actual distributions

fill in the gaps expert opinion predictive models mapping species distributions Carabus granulatus Copris hispanus Hortal J Biogeogr 2008; Penev et al The genus Carabus in Europe 2007; Chefaoui et al Biol Conserv 2005

neither the species are present everywhere within their range maps, nor all their known occurrences are within these range maps inconsistencies with atlas data Hurlbert & White Ecol Lett 2005

these mismatches are scale dependent inconsistencies with atlas data Hurlbert & Jetz PNAS 2007

probability of presence environmental gradient land classes limited knowledge on the predictors the actual responses of the species to the environment are unknown

data incompleteness Total All species 23 First recorded species the descriptions of the environmental responses of most species are incomplete and biased Hortal et al Oikos 2008

Chefaoui et al Anim Biodiv Conserv 2011 expert-drawn observed plots predictive models hybrid approach fine coarse uncertainty in predictions different techniques predict different distribution patterns whorl snail Vertigo mouninsiana southern damselfly Coenagrion mercuriale GLMGAMNNET

Araújo & Rahbek Science 2006; Lawler et al Global Change Biol 2006 uncertainty in future projections

other determinants of the distribution historical effects e.g., Lobo et al. Div Distr 2006 Chefaoui & Lobo J Wildl Man 20º7 Spanish moon moth Graellsia isabelae

ensemble forecasting Araújo & New Tree 2006 dealing with uncertainty?

maps of ignorance Boggs Proc Am Phil Soc 1949 a region is an “area of ignorance” if the total library resources of the outside world do not cover it (Boggs 1949)

maps of ignorance Boggs Proc Am Phil Soc 1949 a region is an “area of ignorance” if the total library resources of the outside world do not cover it (Boggs 1949)

accuracy of knowledge Hortal, Ladle et al in prep. K p = f ( [K 0 ·C], L t, L s ) K p = accuracy of the knowledge about a given taxon or community at area p K 0 = knowledge about such taxon or community at each area in the moment of the survey C = degree of completeness of the survey L t = loss of knowledge across time L s = loss of knowledge across space

– Taxonomic accuracy – Detectability (crypsis, phenology) – Adequacy of sampling method and dates – Interactions – Size of focal unit – Habitat heterogeneity – Sampling effort and success quality of initial knowledge Hortal, Ladle et al in prep.

Temporal decay of similarity: - Changes in taxonomy - Turnover of species (mobility, phenotypic traits) - Area of unit (small  higher turnover) - Range shifts (climate change) - Local extinctions (land use changes, biological invasions) temporal loss of knowledge Hortal, Ladle et al in prep. Magersfontein battlefield, South Africa (from Moustakas et al Front Biogeogr 2010)

Distance decay of similarity: - Taxon specific - Biogeographical changes - Environmental gradients - Metacommunity structure - Habitat specificity (niche width) spatial loss of knowledge Hortal, Ladle et al in prep. Magersfontein battlefield, South Africa (from Green et al Nature 2004) Species: metacommunity structure / habitat specificity (niche width) / changes in climatic scenopoetic conditions

1. develop tools to map ignorance - how to measure taxonomic uncertainty - how to assess uncertainty in observations - how to map the degree of reliability of species distribution models in each point of space - how to determine when distribution is being extrapolated - how… 2. attach maps of ignorance as metadata for any distributional map suggestions are welcome! looking forward

1. develop tools to map ignorance - how to measure taxonomic uncertainty - how to assess uncertainty in observations - how to map the degree of reliability of species distribution models in each point of space - how to determine when distribution is being extrapolated - how… 2. attach maps of ignorance as metadata for any distributional map suggestions are welcome! looking forward

Museo Nacional de Ciencias Naturales (CSIC), Spain ; Joaquín Hortal Richard J. Ladle Geiziane Tessarolo Jorge M. Lobo Duccio Rocchini and many others...