Spe c ie s Distrib utio n Mo de ling L E CT URE 25 SPE CI E S DI ST RI BUT I ON MODE L I NG UNI T 3: ST UF F Ob je c tive s At the e nd o f this se rie s o f le c ture s, yo u sho uld b e a b le to : De fine te rms E xplain the c o nc e pt o f nic he . Disting uish be twe e n me c hanistic and c o rre lative mo de ls. De sc ribe the ba sic s flo w o f info rma tio n in the de ve lo pme nt o f a SDM. E xplain the re latio nship be twe e n g e o g raphic al spac e and e nviro nme ntal spac e . 1
Ob je c tive s De sc ribe ho w e quilibrium a nd sa mpling a de qua c y influe nc e the de ve lo pme nt o f SDM. E xpla in c ro ss valida tio n. E xplain the e rro rs that a SDM c an g e ne rate . De sc ribe ho w SDM c a n be use d in c o nse rvatio n bio lo g y. Mo de l Appro a c he s Me c ha nistic Appro a c h Do no t re ly o n o b se rve d o c c urre nc e re c o rds Re quire de taile d physio lo g ic al data Co rre la tive Appro a c h Assume c urre nt distributio n g ive s a g o o d indic a to r o f e c o lo g ic a l re quire me nts 2
F lo w dia g ra m o f the ma in ste ps o f spe c ie s distrib utio n mo de l Observed species’ distribution (a list of localities where the species has been observed, and sometimes Modeling algorithm also localities where the species is Predicted species’ (e.g. Maxent, artificial known to be absent) distribution. neural network, Prediction may be for a generalized linear different region (e.g. for model, regression tree) an invasive species) or Processing to for a different time period Database of ‘raw’ generate (e.g. under future climate environmental variables variables of change) Model testing (e.g. temperature, importance in (statistical assessment precipitation, soil type). defining species’ of predictive ability, distributions using test such as Data usually stored in a (e.g. maximum AUC or Kappa) GIS daily temperature, frost days, soil water balance) F a c to rs tha t I nflue nc e the L imits o f the Ge o g ra phic Ra ng e Ab io tic e nviro nme nt T e mpe rature Pre c ipitatio n So il type Bio tic inte ra c tio ns Pre da tio n Pa tho g e ns Mutualisms Histo ry a nd g e o g ra phy Dispe rsa l 3
Re la tio nship b e twe e n g e o g ra phic a l spa c e a nd e nviro nme nta l spa c e 4
E q uilib rium a nd Sa mpling Ade q ua c y E q uilib rium: A spe c ie s is in e q uilib rium with c urre nt e nviro nme nta l c o nditio ns if it o c c urs in a ll suita b le a re a s a nd is a b se nt fro m a ll unsuita b le a re a s. De pe nds bo th o n bio tic inte ra c tio ns (e .g . c o mpe titive e xc lusio n fro m a n a re a ) a nd dispe rsa l a bility. Sa mpling a de q ua c y: T he e xte nt to whic h the o b se rve d o c c urre nc e re c o rds pro vide a sa mple o f the e nviro nme nta l spa c e Hig h e q uilib rium a nd e xc e lle nt sa mpling 5
Hig h e q uilib rium b ut po o r sa mpling Hig h e quilibrium a nd po o r sa mpling in g e o g ra phic a l spa c e , but g o o d sampling in e nviro nme ntal spac e 6
L o w e q uilib rium b ut g o o d sa mpling Cro ss Va lida tio n T he spe c ie s o c c urre nc e da ta is divide d ra ndo mly into two pa rts: T raining data T e st data T he tra ining da ta is use d to c o nstruc t the mo de l. T ypic ally a mo de l fits training data fairly we ll, but that is to be e xpe c te d a nd do e s no t te ll us ho w the mo de l will do o ve ra ll. T he mo de l is the n a pplie d to the te st da ta . I f the mo de l do e s an e ffe c tive jo b o f pre dic ting the te st data, the mo de l is pro bably fairly e ffe c tive . 7
E rro rs SDM mo de ls c a n g e ne ra te two type s o f e rro rs. E rro r o f o missio n Sa ys the spe c ie s do e s no t o c c ur so me whe re it a c tua lly do e s. E rro r o f c o mmissio n Sa ys the spe c ie s o c c urs so me whe re it a c tua lly do e s no t. Use s o f spe c ie s distrib utio n mo de ls in c o nse rva tio n b io lo g y Type of use Example reference(s) Guiding field surveys to accelerate Raxworthy et al. 2003, Bourg et al. 2005, detection of unknown distributional areas Guisan et al. 2006 and undiscovered species Projecting potential impacts of climate Iverson and Prasad 1998, Berry et al. 2002, change Hannah et al. 2005; for review see Pearson and Dawson 2003 Predicting species’ invasion Higgins et al. 1999, Thuiller et al. 2005; for review see Peterson 2003 Supporting conservation prioritization and Araujo and Williams 2000, Ferrier et al. reserve selection 2002 Assessing the impacts of land cover change Pearson et al. 2004 on species’ distributions Guiding reintroduction of endangered Pearce and Lindenmayer 1998 species 8
Recommend
More recommend
