Data from: How to best threshold and validate stacked species assemblages? Community optimisation might hold the answer

• Main Authors: Scherrer, Daniel, D'Amen, Manuela, Fernandes, Rui F., Mateo, Rubén G., Guisan, Antoine
• Format: Dataset
• Language: English
• Published: Dryad 2018
• Subjects: Primula acaulis; Zygaena filipendulae; Anthriscus sylvestris; Bromus benekenii; Stachys sylvatica; Neottia nidus avis; Aphantopus hyperantus; grasshoppers; Ligusticum mutellina; Phyteuma spicatum; Ranunculus aconitifolius; Geranium sylvaticum; Colias crocea; Glechoma hederacea; Polyommatus Lysandra coridon; Cephalanthera rubra; Polygala chamaebuxus; Calamagrostis varia; Ajuga reptans; Viola hirta; Erynnis tages; Carduus personata; Holocene; Filipendula ulmaria; Lamium purpureum; Euthystira brachyptera; Eupatorium cannabinum; Asplenium trichomanes; Melitaea Mellicta athalia; Dryopteris oreades; Homogyne alpina; Viburnum opulus; Omocestus viridulus; Tettigonia cantans; Cystopteris fragilis; Galium odoratum; Viola reichenbachiana; Sorbus aucuparia; Aconitum napellus aggr; Galeopsis tetrahit; Rhododendron ferrugineum; Sanicula europaea; Carlina simplex; Prunus avium; Ribes alpinum; Thymelicus sylvestris; Vincetoxicum hirundinaria; Pulmonaria officinalis; Sambucus nigra; Cotoneaster tomentosus; Pyrgus serratulae; Populus tremula; Crepis pyrenaica; Tofieldia calyculata; Pieris rapae; Salix caprea; Sambucus racemosa; Caltha palustris; Pteridium aquilinum; Festuca heterophylla; Campanula rotundifolia; Ribes petraeum; Valeriana montana; Acer platanoides; Amelanchier embergeri; Melica nutans; Salvia glutinosa; Lasiommata petropolitana; Polystichum lonchitis; Papilio machaon; Phyllitis scolopendrium; Carex ornithopoda; Chorthippus dorsatus; Lathyrus vernus; Carex pallescens; Lonicera alpigena; Geum rivale; Festuca altissima; Soldanella alpina; Carex flacca; Argynnis aglaja; Geum urbanum; Callophrys rubi; Polyommatus icarus; Arum maculatum; Cotoneaster integerrimus; Convallaria majalis; Petasites albus; Sorbus mougeotii; Ranunculus lanuginosus; Colias phicomone; Clematis vitalba; Coronilla vaginalis; Pulsatilla alpina; Juniperus nana; Aricia artaxerxes; Carduus defloratus; Centaurea montana; Prunella grandiflora; Colias alfacariensis hyale; Erebia ligea; Saxifraga cuneifolia; Viola biflora; Erebia epiphron; Leucanthemum vulgare aggr; Erebia gorge; Viburnum lantana; Fagus sylvatica; Urtica dioica aggr; Vaccinium myrtillus; Aruncus dioicus; Lonicera nigra; Arnica montana; Pholidoptera griseoaptera; Erebia aethiops; Erebia euryale; Metrioptera roeselii; Polyommatus lysandra bellargus; Vanessa cynthia cardui; Alnus incana; Aconitum paniculatum; Brachypodium pinnatum; Veratrum lobelianum; Lotus corniculatus; Astrantia minor; Phegopteris connectilis; Oreopteris limbosperma; Gomphocerus sibiricus; Galium rotundifolium; Melittis melissophyllum; Argynnis niobe; Aposeris foetida; Adenostyles glabra; Carpinus betulus; Potentilla erecta; Senecio ovatus; Cephalanthera longifolia; Fragaria vesca; Lamium montanum; Stachys officinalis; Gymnocarpium robertianum; Rhamnus cathartica; Anemone nemorosa; Cicerbita alpina; Berberis vulgaris; Podisma pedestris; Gymnocarpium dryopteris; Coenonympha gardetta; Acer pseudoplatanus; Polyommatus Cyaniris semiargus; Platanthera bifolia; Boloria pales; Stenobothrus lineatus; Carex montana; Veronica chamaedrys; Prunus spinosa; Epilobium angustifolium; Agrostis capillaris; Chorthippus brunneus; Ranunculus auricomus aggr; Mercurialis perennis; Gymnadenia conopsea aggr; Phyteuma orbiculare; Chorthippus biguttulus; Euphorbia dulcis; Peucedanum ostruthium; Polypodium vulgare; Rumex alpestris; Globularia nudicaulis; Coenonympha pamphilus; Dactylorhiza maculata; Zygaena transalpina; Lycaena hippothoe; Erica carnea; Luzula sylvatica; Betula pendula; Erebia pronoe; Melampyrum sylvaticum; Abies alba; Cupido minimus; Zygaena exulans; Arabis hirsuta; Rubus caesius; Sesleria caerulea; Tilia platyphyllos; Melitaea diamina; Ulmus glabra; Hieracium prenanthoides; Saxifraga rotundifolia; Asplenium fontanum; Maniola jurtina; Arabis turrita; Equisetum arvense; Mycelis muralis; Stellaria nemorum; Boloria clossiana euphrosyne; Polyommatus agrodiaetus damon; Digitalis lutea; Galium pumilum; Anthropocene; Boloria clossiana titania; Decticus verrucivorus; Erebia melampus; Laserpitium latifolium; Erebia oeme; Polysarcus denticauda; Carex digitata; Allium ursinum; Gryllus campestris; Castanea sativa; Sorbus aria; Hesperia comma; Saxifraga paniculata; Circaea lutetiana; Hypericum maculatum; Ranunculus platanifolius; Myosotis sylvatica; Moehringia muscosa; Acer campestre; Aconitum lycoctonum aggr; Prunella vulgaris; Carex alba; Hippocrepis emerus; Boloria napaea; Valeriana tripteris; Solanum dulcamara; Lycaena tityrus; Listera ovata; Trifolium pratense; Hedera helix; Prenanthes purpurea; Crepis paludosa; Campanula cochleariifolia; Actaea spicata; Leptidea reali sinapis; Taraxacum officinale; community models; Carex ferruginea; Juglans regia; Dryopteris carthusiana; Poa nemoralis; Hieracium vogesiacum; Lonicera xylosteum; Larix decidua; Fraxinus excelsior; Cardamine heptaphylla; Potentilla sterilis; Gentiana lutea; Rosa arvensis; Aquilegia vulgaris; Anthriscus nitida; Impatiens noli tangere; Veronica urticifolia; Pinus uncinata; Aster bellidiastrum; Rubus saxatilis; Acer opalus; Achillea macrophylla; Maculinea arion; Zygaena lonicerae; Orchis mascula; Elymus caninus; Cornus mas; Polygonum viviparum; Aricia eumedon; Rosa corymbifera; Lunaria rediviva; Pinus sylvestris aggr; Sorbus chamaemespilus; Campanula rhomboidalis; Helleborus foetidus; Athyrium filix femina; Pyrgus alveus; Tilia cordata; Luzula nivea; Erebia tyndarus; Chrysosplenium alternifolium; Veronica officinalis; Aporia crataegi; Galium mollugo; Picea abies; Valeriana dioica; Aglais urticae; Silene dioica; Quercus robur; Aegopodium podagraria; Lathyrus pratensis; Rhamnus alpina; Hordelymus europaeus; Stachys alpina; Euphorbia amygdaloides; Geranium sanguineum; Adoxa moschatellina; Epilobium alpestre; Teucrium chamaedrys; Alnus viridis; Heracleum sphondylium aggr; Gomphocerippus rufus; Thesium alpinum; Angelica sylvestris; Paris quadrifolia; Pimpinella major aggr; Scabiosa columbaria; Cardamine pentaphyllos; Trollius europaeus; Carex sylvatica; Hepatica nobilis; Hypericum montanum; Lysimachia nemorum; Deschampsia cespitosa; Myrrhis odorata; butterflies; S-SDM; Juniperus communis; Lilium martagon; Polygonum bistorta; Asplenium viride; Cephalanthera damasonium; Oxalis acetosella; Cardamine impatiens; Anthocharis cardamines; Quercus petraea; Anthericum ramosum; Erebia pandrose; Astrantia major; Vaccinium vitis idaea aggr; Myosotis scorpioides; Pyrgus malvae; Colchicum autumnale; Erebia manto; Knautia dipsacifolia; Euphorbia cyparissias; Rubus idaeus; Mecostethus parapleurus; Polyommatus eros; Ligustrum vulgare; Polystichum aculeatum; Galium aparine; Pieris bryoniae; Lycopodium annotinum; Polygonatum odoratum; Milium effusum; Primula elatior; Orthilia secunda; Origanum vulgare; Campanula barbata; Cirsium oleraceum; Ilex aquifolium; Thymus pulegioides; Epilobium montanum; Daphne mezereum; Ochlodes venatus; Vicia sepium aggr; Tamus communis; Campanula trachelium; Polygonatum verticillatum; Epipactis atrorubens; Equisetum sylvaticum; Holocene/Anthropocene; Dactylis glomerata; Tussilago farfara; species richness modelling; Euonymus europaeus; Listera cordata; Petasites hybridus; Erebia pharte; Luzula luzulina; Euphydryas aurinia; Stauroderus scalaris; Lasiommata maera; Rosa pendulina; Pieris napi; Gentiana purpurea; Laburnum alpinum; Salix appendiculata; Polygonatum multiflorum; Vicia sylvatica; Vanessa atalanta; Daphne laureola; Briza media; Clinopodium vulgare; Lonicera caerulea; Luzula pilosa; Thymelicus lineola; Cornus sanguinea; Nardus stricta; Melanargia galathea; Chorthippus parallelus; Metrioptera saussuriana; Carex humilis; Aquilegia atrata; Pieris brassicae; Dryopteris dilatata; Dryopteris filix mas; Taxus baccata; Thalictrum aquilegiifolium; Corylus avellana; Melica uniflora; Maianthemum bifolium; Malus sylvestris; Scrophularia nodosa; Adenostyles alliariae; Epipactis helleborine; Gentiana asclepiadea; Blechnum spicant; Miramella alpina; Streptopus amplexifolius; Eros; Malus; Napi; Nivea; Rhamnus; Ribes
• Online Access: https://dx.doi.org/10.5061/dryad.nf925ps; http://datadryad.org/stash/dataset/doi:10.5061/dryad.nf925ps

PLEASE NOTE, THESE DATA ARE ALSO REFERRED TO IN TWO OTHER PUBLICATIONS. PLEASE SEE DOI: 10.1111/ddi.12548 AND https://doi.org/10.1111/geb.12357 --- The popularity of species distribution models (SDMs) and the associated stacked species distribution models (S‐SDMs), as tools for community ecologists, largely increased in recent years. However, while some consensus was reached about the best methods to threshold and evaluate individual SDMs, little agreement exists on how to best assemble individual SDMs into communities, that is, how to build and assess S‐SDM predictions. Here, we used published data of insects and plants collected within the same study region to test (a) if the most established thresholding methods to optimize single species prediction are also the best choice for predicting species assemblage composition, or if community‐based thresholding can be a better alternative, and (b) whether the optimal thresholding method depends on taxa, prevalence distribution and/or species richness. Based on a comparison of different evaluation approaches, we provide guidelines for a robust community cross‐validation framework, to use if spatial or temporal independent data are unavailable. Our results showed that the selection of the “optimal” assembly strategy mostly depends on the evaluation approach rather than taxa, prevalence distribution, regional species pool or species richness. If evaluated with independent data or reliable cross‐validation, community‐based thresholding seems superior compared to single species optimisation. However, many published studies did not evaluate community projections with independent data, often leading to overoptimistic community evaluation metrics based on single species optimisation. The fact that most of the reviewed S‐SDM studies reported over‐fitted community evaluation metrics highlights the importance of developing clear evaluation guidelines for community models. Here, we move a first step in this direction, providing a framework for cross‐validation at the community level. : Forest plant distribution and environmental data in the western Swiss AlpsPlant species (presence-absence records) and environmental data for each of the 3076 forest plots. The Scherrer et al. (2018, Methods in Ecology and Evolution) paper uses the associated Dryas dataset of plant species distribution data and environmental predictors to compare a number of thresholding methods for stacked species distribution models (S-SDMs) and how to best evaluate them with an appropriate cross-validation framework. Subsets of these data have also been used in previous works by the same research group: Scherrer et al. (2017, Diversity and Distributions) and others (see full publication list under http://www.unil.ch/ecospat).forest_speciesEnvironment.csvButterfly distribution and environmental data in the western Swiss AlpsButterfly species (presence-absence records) and environmental data for each of the 192 plots. The Scherrer et al. (2018, Methods in Ecology and Evolution) paper uses the associated Dryas dataset of plant species distribution data and environmental predictors to compare a number of thresholding methods for stacked species distribution models (S-SDMs) and how to best evaluate them with an appropriate cross-validation framework. Subsets of these data have also been used in previous works by the same research group: Pellisier et al. (2012, Ecography), D’Amen et al. (2015, Global Ecology and Biogeography) and others (see full publication list under http://www.unil.ch/ecospat).butterflies_speciesEnvironment.csvGrasshopper distribution and environmental data in the western Swiss AlpsGrasshopper species (presence-absence records) and environmental data for each of the 202 plots. The Scherrer et al. (2018, Methods in Ecology and Evolution) paper uses the associated Dryas dataset of plant species distribution data and environmental predictors to compare a number of thresholding methods for stacked species distribution models (S-SDMs) and how to best evaluate them with an appropriate cross-validation framework. Subsets of these data have also been used in previous works by the same research group: Pradervand et al. (2013, Bulletin de la Société Vaudoises des Sciences Naturelles), D’Amen et al. (2015, Global Ecology and Biogeography) and others (see full publication list under http://www.unil.ch/ecospat).grasshoppers_speciesEnvironment.csv