Ecological Networks: Case Studies, Challenges and Lessons Learned
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Poland, May 2011
Woodpeckers, Waders and Warblers: North-eastern Poland, 12-17th May Participants: Ian Reid and Tom Bedford North-eastern Poland had been on my list of places to go ever since Bill Oddie’s second series went there and he was guided around by Marek Borkowski to see goodies such as Aquatic Warbler and Great Snipe. Finally in ’11 I decided it was high time I went and I was delighted when Tom showed enthusiasm for joining me. In fact having set the ball rolling, choosing the dates, sorting out guides, and pricing flights, I then ended up in a very busy period at work and it was Tom took care of most of the remaining logistics. It is possible to do such a trip rather cheaper than we did, but we opted for the convenience of Heathrow and took BA to Warsaw at a cost of approx. £150 each. Car hire, booked through carhire3000.com was a Toyota Yaris with Thrifty, and once we had added a second driver came to about £200 for 6 days. We booked guides through Lukasz Masurek’s site wildpoland.com for the evening of 12th (for Pygmy Owl), 8 hours on 13th (Woodpeckers and the Strict Reserve), and the evening of 13th (Great Snipe). Arek Szmyura, mentioned in many trip reports was our guide for the first two, and his son Mateusz took us to the Great Snipe lek. Prices have gone up a lot since the reports I read from ’09. The Pygmy Owl trip cost 90euros, Arek’s services for 8 hours on 13th was 150euros, and the Great Snipe trip was also 90euros. -
Identifying a Common Backbone of Interactions Underlying Food Webs from Different Ecosystems
ARTICLE DOI: 10.1038/s41467-018-05056-0 OPEN Identifying a common backbone of interactions underlying food webs from different ecosystems Bernat Bramon Mora1, Dominique Gravel2,3, Luis J. Gilarranz4, Timothée Poisot 2,5 & Daniel B. Stouffer 1 Although the structure of empirical food webs can differ between ecosystems, there is growing evidence of multiple ways in which they also exhibit common topological properties. To reconcile these contrasting observations, we postulate the existence of a backbone of — 1234567890():,; interactions underlying all ecological networks a common substructure within every net- work comprised of species playing similar ecological roles—and a periphery of species whose idiosyncrasies help explain the differences between networks. To test this conjecture, we introduce a new approach to investigate the structural similarity of 411 food webs from multiple environments and biomes. We first find significant differences in the way species in different ecosystems interact with each other. Despite these differences, we then show that there is compelling evidence of a common backbone of interactions underpinning all food webs. We expect that identifying a backbone of interactions will shed light on the rules driving assembly of different ecological communities. 1 Centre for Integrative Ecology, School of Biological Sciences, University of Canterbury, Christchurch 8041, New Zealand. 2 Québec Centre for Biodiversity Sciences, McGill University, Montréal H3A 0G4, Canada. 3 Canada Research Chair on Integrative Ecology, Départment de Biologie, Université de Sherbrooke, Sherbrooke J1K 2R1, Canada. 4 Department of Evolutionary Biology and Environmental Studies, University of Zurich, 8006 Zurich, Switzerland. 5 Département de Sciences Biologiques, Université de Montréal, Montréal H3T 1J4, Canada. -
Phylogenetic Reanalysis of Strauch's Osteological Data Set for The
TheCondor97:174-196 0 The Cooper Ornithological Society 1995 PHYLOGENETIC REANALYSIS OF STRAUCH’S OSTEOLOGICAL DATA SET FOR THE CHARADRIIFORMES PHILIP c. CHU Department of Biology and Museum of Zoology The University of Michigan, Ann Arbor, MI 48109 Abstract. Strauch’s (1978) compatibility analysisof relationshipsamong the shorebirds (Charadriifonnes) was the first study to examine the full range of charadriifonn taxa in a reproducibleway. SubsequentlyMickevich and Parenti (1980) leveled seriouscharges against Strauch’s characters,method of phylogenetic inference, and results. To account for these charges,Strauch ’s characterswere re-examined and recoded, and parsimony analyseswere performed on the revised matrix. A parsimony analysison 74 taxa from the revised matrix yielded 855 shortesttrees, each length = 286 and consistencyindex = 0.385. In each shortest tree there were two major lineages,a lineageof sandpiper-likebirds and a lineageof plover- like birds; the two formed a monophyletic group, with the auks (Alcidae) being that group’s sister taxon. The shortest trees were then compared with other estimates of shorebird re- lationships, comparison suggestingthat the chargesagainst Strauch’s results may have re- sulted from the Mickevich and Parenti decisions to exclude much of Strauch’s character evidence. Key words: Charadrilformes; phylogeny; compatibility analysis: parsimony analysis; tax- onomic congruence. INTRODUCTION Strauch scored 227 charadriiform taxa for 70 The investigation of evolutionary relationships characters. Sixty-three of the characters were among shorebirds (Aves: Charadriiformes) has a taken from either the skull or postcranial skel- long history (reviewed in Sibley and Ahlquist eton; the remaining seven involved the respec- 1990). Almost all studies used morphology to tive origins of three neck muscles, as published make inferences about shared ancestry; infer- in Burton (1971, 1972, 1974) and Zusi (1962). -
Proposal for the Inclusion of Species on the Appendices of the Convention on the Conservation of Migratory Species of Wild Animals
1 / 2 Proposal II/7 PROPOSAL FOR THE INCLUSION OF SPECIES ON THE APPENDICES OF THE CONVENTION ON THE CONSERVATION OF MIGRATORY SPECIES OF WILD ANIMALS A. PROPOSAL: Listing the entire population of Glareola nuchalis on Appendix II. B. PROPONENT: Government of Kenya. C. SUPPORTING STATEMENT: 1. Taxon: 1.1 Class: Aves 1.2 Order: Charadriiformes 1.3 Family: Glareolidae 1.4 Genus/species/subspecies: Glareola nuchalis 1.5 Common names: English: Rock Pratincole, White-collared Pratincole French: Glarède aureole 2. Biological data 2.1 Distribution Distributed in West and central Africa. Scarce in eastern Africa. 2.2 Population No detailed census data available, but the best guess information available puts the number at >25,000 within its distribution range. 2.3 Habitat Exposed rocks in large rivers and streams used for breeding. May rest on sandbars, when rivers flood. Also found in coastal localities and other inland waters. 2.4 Migrations Locally common resident and regular intra-African migrant. Migrates within its distribution range. Most post breeding dispersal occurs during the wet season. 3. Threat data 3.1 Direct threats Unpredictable fluctuations of water levels of local rivers during breeding seasons affect the breeding performance. Sand mining along rivers has severe impacts on the availability of suitable habitats in the riparian areas for nesting. 3.2 Habitat destruction Riparian land use activities within the range states limits the availability of suitable roosting and nesting areas along rivers. 3.3 Indirect threats The loss and degradation of catchments for all wetlands within its range. 3.4 Threats connected especially with migrations None known. -
Limits on Ecosystem Trophic Complexity: Insights from Ecological Network Analysis
Ecology Letters, (2014) 17: 127–136 doi: 10.1111/ele.12216 IDEA AND PERSPECTIVE Limits on ecosystem trophic complexity: insights from ecological network analysis Abstract Robert E. Ulanowicz,1,2* Robert D. Articulating what limits the length of trophic food chains has remained one of the most enduring Holt1 and Michael Barfield1 challenges in ecology. Mere counts of ecosystem species and transfers have not much illumined the issue, in part because magnitudes of trophic transfers vary by orders of magnitude in power- law fashion. We address this issue by creating a suite of measures that extend the basic indexes usually obtained by counting taxa and transfers so as to apply to networks wherein magnitudes vary by orders of magnitude. Application of the extended measures to data on ecosystem trophic networks reveals that the actual complexity of ecosystem webs is far less than usually imagined, because most ecosystem networks consist of a multitude of weak connections dominated by a rel- atively few strong flows. Although quantitative ecosystem networks may consist of hundreds of nodes and thousands of transfers, they nevertheless behave similarly to simpler representations of systems with fewer than 14 nodes or 40 flows. Both theory and empirical data point to an upper bound on the number of effective trophic levels at about 3–4 links. We suggest that several whole-system processes may be at play in generating these ecosystem limits and regularities. Keywords Connectivity, ecosystem roles, food chain length, network complexity, system flexibility, system roles, trophic breadth, trophic depth, window of vitality. Ecology Letters (2014) 17: 127–136 2009); some herbivores (megaherbivores) evolve to sizes AN ENDURING QUESTION IN ECOLOGY: WHAT whereby they largely escape predation, thus truncating the LIMITS FOOD CHAIN LENGTH? food chain. -
Florida's Ecological Network
Green Infrastructure — Linking Lands for Nature and People Case Study Series Florida’s Ecological Network Photo by Jane M. Rohling / USFWS Vision Overview "In the 21st century, Florida has a protected system of The Florida Greenways Commission defined a greenways that is planned and managed to conserve “greenways system” as a “system of native landscapes native landscapes, ecosystems and their species; and and ecosystems that supports native plant and animal to connect people to the land . Florida's diverse species, sustains clean air, water, fisheries, and other wildlife species are able to move . within their natural resources, and maintains the scenic natural ranges with less danger of being killed on roadways or beauty that draws people to visit and settle in Florida.” becoming lost in towns or Greenways follow natural land or water features such cities. Native landscapes as ridges or rivers or human landscape features such and ecosystems are as abandoned railroad corridors or canals. A healthy, protected, managed, and well functioning system of greenways can support restored through strong wildlife communities and provide innumerable benefits public and private to Florida’s people, as well. The Commission saw a partnerships. Sensitive healthy and diverse green infrastructure as the riverine and coastal underlying basis of Florida’s sustainable future. They waterways are effectively identified two components to the statewide greenways protected by buffers of green, open space and working system: the Ecological Network and the Recreational/ landscapes. Florida's rich system of greenways Cultural Network (Figure 1). We focus here on the helps sustain Florida's future by conserving its green Ecological Network. -
Predicting Ecosystem Functions from Biodiversity and Mutualistic Networks: an Extension of Trait-Based Concepts to Plant– Animal Interactions
Ecography 38: 380–392, 2015 doi: 10.1111/ecog.00983 © 2014 Th e Authors. Ecography © 2014 Nordic Society Oikos Subject Editor: Jens-Christian Svenning. Editor-in-Chief: Jens-Christian Svenning. Accepted 24 October 2014 Predicting ecosystem functions from biodiversity and mutualistic networks: an extension of trait-based concepts to plant – animal interactions Matthias Schleuning , Jochen Fr ü nd and Daniel Garc í a Intecol special issue M. Schleuning ([email protected]), Biodiversity and Climate Research Centre (BiK-F) and Senckenberg Gesellschaft f ü r Naturforschung, Senckenberganlage 25, DE-60325 Frankfurt (Main), Germany. – J. Fr ü nd, Dept of Integrative Biology, Univ. of Guelph, ON N1G2W1, Canada. – D. Garc í a, Univ. of Oviedo, Depto Biolog í a de Organismos y Sistemas and Unidad Mixta de Investigaci ó n en Biodiversidad (CSIC-UO-PA), Valent í n Andr é s Á lvarez s/n, ES-33071 Oviedo (Asturias), Spain. Research linking biodiversity and ecosystem functioning (BEF) has been mostly centred on the infl uence of species rich- ness on ecosystem functions in small-scale experiments with single trophic levels. In natural ecosystems, many ecosystem functions are mediated by interactions between plants and animals, such as pollination and seed dispersal by animals, for which BEF relationships are little understood. Largely disconnected from BEF research, network ecology has examined the structural diversity of complex ecological networks of interacting species. Here, we provide an overview of the most impor- tant concepts in BEF and ecological network research and exemplify their applicability to natural ecosystems with examples from pollination and seed-dispersal studies. In a synthesis, we connect the structural approaches of network analysis with the trait-based approaches of BEF research and propose a conceptual trait-based model for understanding BEF relation- ships of plant – animal interactions in natural ecosystems. -
Overview of Network Environ Analysis: a Systems Analysis Technique for Understanding Complex Ecological Systems
Ecodinamica Overview of Network Environ Analysis: A systems analysis technique for understanding complex ecological systems Brian D. Fath 1,2,3 1Biology Department Towson University, Towson, Maryland, USA 2Fulbright Distinguished Chair in Energy and Environment, Parthenope University of Naples, Italy 3Advanced Systems Analysis Program, International Institute for Applied Systems Analysis, Austria Synopsis Network Environ Analysis, based on network theory, Analysis requires data including the intercompart - reveals the quantitative and qualitative relations be - mental flows, compartmental storages, and boundary tween ecological objects interacting with each other input and output flows. Software is available to per - in a system. The primary result from the method pro - form this analysis. This article reviews the theoretical vides input and output “environs”, which are inter - underpinning of the analysis and briefly introduces nal partitions of the objects within system flows. In some the main properties such as indirect effects ra - addition, application of Network Environ Analysis on tio, network homogenization, and network mutual - empirical datasets and ecosystem models has re - ism. References for further reading are provided. vealed several important and unexpected results that have been identified and summarized in the litera - Keywords: Systems ecology, connectivity, energy flows, ture as network environ properties. Network Environ network analysis, indirect effects, mutualism Introduction which is mostly concerned with interrelations of ma - terial, energy and information among system com - Environ Analysis is in a more general class of meth - ponents (Table 1). ods called Ecological Network Analysis (ENA) ENA starts with the assumption that a system can be which uses network theory to study the interactions represented as a network of nodes (compartments, between organisms or populations within their envi - objects, etc.) and the connections between them ronment. -
Ecological Network Metrics: Opportunities for Synthesis
University of Vermont ScholarWorks @ UVM College of Arts and Sciences Faculty Publications College of Arts and Sciences 8-1-2017 Ecological network metrics: Opportunities for synthesis Matthew K. Lau Harvard Forest Stuart R. Borrett University of North Carolina Wilmington Benjamin Baiser University of Florida Nicholas J. Gotelli University of Vermont Aaron M. Ellison Harvard Forest Follow this and additional works at: https://scholarworks.uvm.edu/casfac Part of the Climate Commons, Community Health Commons, Human Ecology Commons, Nature and Society Relations Commons, Place and Environment Commons, and the Sustainability Commons Recommended Citation Lau MK, Borrett SR, Baiser B, Gotelli NJ, Ellison AM. Ecological network metrics: opportunities for synthesis. Ecosphere. 2017 Aug;8(8):e01900. This Article is brought to you for free and open access by the College of Arts and Sciences at ScholarWorks @ UVM. It has been accepted for inclusion in College of Arts and Sciences Faculty Publications by an authorized administrator of ScholarWorks @ UVM. For more information, please contact [email protected]. INNOVATIVE VIEWPOINTS Ecological network metrics: opportunities for synthesis 1, 2,3 4 5 MATTHEW K. LAU, STUART R. BORRETT, BENJAMIN BAISER, NICHOLAS J. GOTELLI, 1 AND AARON M. ELLISON 1Harvard Forest, Harvard University, Petersham, Massachusetts 02138 USA 2Department of Biology and Marine Biology, University of North Carolina, Wilmington, North Carolina 28403 USA 3Duke Network Analysis Center, Social Science Research Institute, Duke University, Durham, North Carolina 27708 USA 4Department of Wildlife Ecology and Conservation, University of Florida, Gainesville, Florida 32611 USA 5Department of Biology, University of Vermont, Burlington, Vermont 05405 USA Citation: Lau, M. K., S. -
Iucn Red Data List Information on Species Listed On, and Covered by Cms Appendices
UNEP/CMS/ScC-SC4/Doc.8/Rev.1/Annex 1 ANNEX 1 IUCN RED DATA LIST INFORMATION ON SPECIES LISTED ON, AND COVERED BY CMS APPENDICES Content General Information ................................................................................................................................................................................................................................ 2 Species in Appendix I ............................................................................................................................................................................................................................... 3 Mammalia ............................................................................................................................................................................................................................................ 4 Aves ...................................................................................................................................................................................................................................................... 7 Reptilia ............................................................................................................................................................................................................................................... 12 Pisces ................................................................................................................................................................................................................................................. -
Fastest Migration Highest
GO!” Everyone knows birds can fly. ET’S But not everyone knows that “L certain birds are really, really good at it. Meet a few of these champions of the skies. Flying Acby Ellen eLambeth; art sby Dave Clegg! Highest You don’t have to be a lightweight to fly high. Just look at a Ruppell’s griffon vulture (left). One was recorded flying at an altitude of 36,000 feet. That’s as high as passenger planes fly! In fact, it’s so high that you would pass out from lack of oxygen if you weren’t inside a plane. How does the vulture manage? It has Fastest (on the level) Swifts are birds that have that name for good special blood cells that make a small amount reason: They’re speedy! The swiftest bird using its own of oxygen go a long way. flapping-wing power is the common swift of Europe, Asia, and Africa (below). It’s been clocked at nearly 70 miles per hour. That’s the speed limit for cars on some highways. Vroom-vroom! Fastest (in a dive) Fastest Migration With gravity helping out, a bird can pick up extra speed. Imagine taking a trip of about 4,200 And no bird can go faster than a peregrine falcon in a dive miles. Sure, you could easily do it in an airplane. after prey (right). In fact, no other animal on Earth can go as But a great snipe (right) did it on the wing in just fast as a peregrine: more than 200 miles per hour! three and a half days! That means it averaged about 60 miles The prey, by the way, is usually another bird, per hour during its migration between northern which the peregrine strikes in mid-air with its balled-up Europe and central Africa. -
Social-Ecological Connectivity to Understand Ecosystem Service Provision Across Networks in Urban Landscapes
land Communication Social-Ecological Connectivity to Understand Ecosystem Service Provision across Networks in Urban Landscapes 1,2, , 3, Monika Egerer * y and Elsa Anderson y 1 TUM School of Life Sciences, Technical University of Munich, Hans Carl-von-Carlowitz-Platz 2, 85354 Freising, Germany 2 Department of Ecology, Ecosystem Science/Plant Ecology, Technical University of Berlin, 12165 Berlin, Germany 3 Cary Institute of Ecosystem Studies, Millbrook, NY 12545, USA; [email protected] * Correspondence: [email protected] Shared first authorship. y Received: 31 October 2020; Accepted: 17 December 2020; Published: 18 December 2020 Abstract: Landscape connectivity is a critical component of dynamic processes that link the structure and function of networks at the landscape scale. In the Anthropocene, connectivity across a landscape-scale network is influenced not only by biophysical land use features, but also by characteristics and patterns of the social landscape. This is particularly apparent in urban landscapes, which are highly dynamic in land use and often in social composition. Thus, landscape connectivity, especially in cities, must be thought of in a social-ecological framework. This is relevant when considering ecosystem services—the benefits that people derive from ecological processes and properties. As relevant actors move through a connected landscape-scale network, particular services may “flow” better across space and time. For this special issue on dynamic landscape connectivity, we discuss the concept of social-ecological networks using urban landscapes as a focal system to highlight the importance of social-ecological connectivity to understand dynamic urban landscapes, particularly in regards to the provision of urban ecosystem services. Keywords: social-ecological systems; landscape connectivity; social-ecological networks; urban; coupled human-natural systems 1.