Dr Mindy Syfert

Dr Mindy Syfert

Postdoctoral Researcher Crop Wild Relatives

Department: Life Sciences
Division: LS Algae, Fungi and Plants Division

Specialisms

Biogeography, GIS, biodiversity, conservation, geospatial analysis, plant ecology, remote sensing

Summary

I am a biogeographer with a research focus on spatial predictive modelling of plant distributions. My interests include vegetation ecology and plant species diversity at various spatial and temporal scales as well as GIS and remote sensing. I am currently studying wild relatives to the Solanaceae crops of tomato, potato and eggplant and their associated pests. My PhD thesis explored the predictive ability of species distribution models (SDMs) when working with plant species for which ecological knowledge is limited and distribution data are not readily available. My research areas were: 1) evaluating the effect of sampling bias on the predictions of an SDM and offers a method to account for biases in collections data- the study species were tree ferns endemic to New Zealand 2) evaluating the potential of SDMs to provide accurate information that can be used in conservation assessments, in particular for species threatened under the IUCN Red List Categories and Criteria- the study species were a variety of plant groups occurring in Central America with geographic ranges extending into South America 3) investigating the utility of SDMs to generate biologically meaningful patterns of plant species richness at the regional scale and using these results to infer ecological processes generating and maintaining this diversity- the study species were ferns occurring in Costa Rica and Panama.

Qualifications

Degrees

PhD, Plant Sciences, University of Cambridge, United Kingdom, 2009 - 2013

MA, Geography, University of Denver, United States, 2003 - 2005

Employment history

Academic

Post-Doc, NHM, London, Life Sciences, Plants Division, United Kingdom, 2013 - ongoing

Non-academic

GIS Manager, Stanford University, Earth Sciences Library, 2006 - 2009

Other projects

NHM Initiative Pest and Crop Wild Relatives
Role: Funded by
Funding: Museum development
Dates: 2015 - 2017.

Memberships

Member, Royal Geographical Society, London, UK, 2012 - on going.

Publications

Syfert MM, Joppa L, Smith MJ, Coomes DA, Bachman SP, Brummitt NA (2014) Using species distribution models to inform IUCN Red List assessments. Biological Conservation, 177 : 174 - 184. doi: 10.1016/j.biocon.2014.06.012

Syfert MM, Smith MJ, Coomes DA (2013) The Effects of Sampling Bias and Model Complexity on the Predictive Performance of MaxEnt Species Distribution Models. PLoS ONE, 8 (2) : e55158 - e55158. doi: 10.1371/journal.pone.0055158

Pringle RM, Syfert M, Webb JK, Shine R (2009) Quantifying historical changes in habitat availability for endangered species: use of pixel- and object-based remote sensing. Journal of Applied Ecology, 46 (3) : 544 - 553. doi: 10.1111/j.1365-2664.2009.01637.x

Invited and keynote speaker

Inferring diversity pattern and ecological processes from stacked SDMs, a joint meeting of the Environmental Statistics Section (ESS) of the Royal Statistical Society (RSS), British and Irish region of the Biometric Society and the Computational Ecology Special Interest Group of the British Ecological Society (BES): 9/10/2013.

Applying species distribution models to help inform IUCN Red List assessments, International Biogeography Society: 12/1/2013.

Introduction

Summary

I am a biogeographer with a research focus on spatial predictive modelling of plant distributions. My interests include vegetation ecology and plant species diversity at various spatial and temporal scales as well as GIS and remote sensing. I am currently studying wild relatives to the Solanaceae crops of tomato, potato and eggplant and their associated pests. My PhD thesis explored the predictive ability of species distribution models (SDMs) when working with plant species for which ecological knowledge is limited and distribution data are not readily available. My research areas were: 1) evaluating the effect of sampling bias on the predictions of an SDM and offers a method to account for biases in collections data- the study species were tree ferns endemic to New Zealand 2) evaluating the potential of SDMs to provide accurate information that can be used in conservation assessments, in particular for species threatened under the IUCN Red List Categories and Criteria- the study species were a variety of plant groups occurring in Central America with geographic ranges extending into South America 3) investigating the utility of SDMs to generate biologically meaningful patterns of plant species richness at the regional scale and using these results to infer ecological processes generating and maintaining this diversity- the study species were ferns occurring in Costa Rica and Panama.

Qualifications

Degrees

PhD, Plant Sciences, University of Cambridge, United Kingdom, 2009 - 2013

MA, Geography, University of Denver, United States, 2003 - 2005

Employment history

Academic

Post-Doc, NHM, London, Life Sciences, Plants Division, United Kingdom, 2013 - ongoing

Non-academic

GIS Manager, Stanford University, Earth Sciences Library, 2006 - 2009

Projects

Other projects

NHM Initiative Pest and Crop Wild Relatives
Role: Funded by
Funding: Museum development
Dates: 2015 - 2017.

Professional activities

Memberships

Member, Royal Geographical Society, London, UK, 2012 - on going.

Publications

Publications

Syfert MM, Joppa L, Smith MJ, Coomes DA, Bachman SP, Brummitt NA (2014) Using species distribution models to inform IUCN Red List assessments. Biological Conservation, 177 : 174 - 184. doi: 10.1016/j.biocon.2014.06.012

Syfert MM, Smith MJ, Coomes DA (2013) The Effects of Sampling Bias and Model Complexity on the Predictive Performance of MaxEnt Species Distribution Models. PLoS ONE, 8 (2) : e55158 - e55158. doi: 10.1371/journal.pone.0055158

Pringle RM, Syfert M, Webb JK, Shine R (2009) Quantifying historical changes in habitat availability for endangered species: use of pixel- and object-based remote sensing. Journal of Applied Ecology, 46 (3) : 544 - 553. doi: 10.1111/j.1365-2664.2009.01637.x

Impact and outreach

Invited and keynote speaker

Inferring diversity pattern and ecological processes from stacked SDMs, a joint meeting of the Environmental Statistics Section (ESS) of the Royal Statistical Society (RSS), British and Irish region of the Biometric Society and the Computational Ecology Special Interest Group of the British Ecological Society (BES): 9/10/2013.

Applying species distribution models to help inform IUCN Red List assessments, International Biogeography Society: 12/1/2013.