Avian Abundance and Habitat Structure

Transcription

1 ESCI 408 FIELD METHODS IN WILDLIFE ECOLOGY Avan Abundance and Habtat Structure INTRODUCTION Research Queston: Whch habtat characterstc(s determne the local abundance of a partcular brd speces (e.g., golden-crowned knglet, wnter wren, red-breasted nuthatch, spotted towhee, chestnut-backed chckadee, Amercan robn,? Explanng patterns n populaton densty or abundance s a basc ecologcal challenge. Answers to questons about speces rchness are mportant to both basc scence and wldlfe management. For scence, varaton n populaton abundance s a pattern that demands a mechanstc explanaton. For wldlfe managers, ncreasng or decreasng local abundance may be a goal whose achevement depends on understandng that explanaton. For restoraton programs, success depends on mprovng relevant habtat characterstcs. Mechansms that determne the relatonshp between a habtat varable and populaton abundance may be drect or ndrect. Few cavty-nestng brds wll be found n area wthout snags due to lmted avalablty of nest stes, whle a gven cavty-nester may be found at relatvely hgh densty where snags are abundant. In ths example, snags determne populaton abundance drectly. In contrast, abundance of some cavty nestng speces may be lower near forest edges due to competton for nest stes by European starlngs. Ths effect of edge dstance would be ndrect, medated by starlngs. In ths proect, we wll study relatonshps between patterns n local populaton abundance and forest structural characterstcs. We wll record brd speces and measure habtat varables at a set of sample locatons n forest and shrub-steppe habtats along the Grande Ronde Rver. Then we wll determne how well models for each hypothess ft the data. Fnally, we wll use nformaton theoretc crtera to evaluate the emprcal support for each hypotheses and to determne the hypothess(es that best explans patterns n local abundance of selected brd speces. Several general hypotheses are stated below. Not all hypotheses wll be relevant or lkely to all speces. The canddate set consdered n model selecton should be restrcted to plausble, relevant models wth clear mechansms relatng habtat characterstcs to populaton abundance. Hypotheses: 1 Random samplng. Local populaton abundance s a random number bounded below by zero. The best predctor of abundance at a gven ste s the mean abundance found at all stes. 2 Folage heght dversty (FHD. Brd abundance ncreases wth FHD, because folage heght dversty s closely related to dversty of food and nestng resources that a gven speces requres. 3 Snag densty. Brd speces rchness ncreases wth snag densty, because snags provde essental nestng stes and foragng structures for many brd speces, e.g., wnter wren, chckadees, nuthatches. 4 Canopy cover. Brd speces rchness ncreases wth canopy cover. 5 Percent decduous. Some speces (e.g., Swanson s thrush nest and forage n decduous forests, whle others (e.g., golden-crowned knglets use resources found n conferous forests. Stll other speces are habtat generalsts (e.g., Amercan robn, found n decduous, conferous, and mxed forests. Forest composton wll account well for abundance of specalsts (postve relatonshp between % decduous and decduous-assocated speces, negatve relatonshp wth conferousassocated speces, and poorly for generalsts. 6 Tree volume. Tree volume (heght x dbh provdes a surrogate for forest age or structural dversty. Speces assocated wth older or more structurally dverse forests wll occur at hgher abundance n forests wth larger volume. The nverse wll be true for speces assocated wth younger forests. brd_habtat3.pdf (contnued JM rev: 4/21/2018

2 Avan Abundance and Habtat Structure 7 Dstance to edge. Brds that nest n forests and often forage n open areas (e.g., Amercan robn wll be found at hghest densty at forest edges, whch provde ready access to both knds of habtats. Interor speces wll follow the opposte pattern, due to ether resource dstrbuton or the densty of edge-assocated predators. Bvarate combnatons: several bvarate hypotheses may be approprate for a gven speces. These hypotheses should nclude ether the two varables wth the strongest mechanstc relatonshp to the focal speces or two varables that nteract n some way that s not represented n lnear models. Many bvarate combnatons are possble, ncludng the three lsted below. 8 Folage heght dversty and snag densty. Cavty nesters that forage on dverse food sources wll be found at greatest abundance forests wth many nest stes (snags and hgh structural dversty (FHD. 9 Volume and percent decduous. Abundance of speces assocated wth mature or late successonal confer forests wll be greatest n habtats wth hgh tree volume and low decduous composton. 10 Volume and dstance to edge. Abundance of speces assocated wth old growth forest nterors wll be greatest n forests wth hgh volume, at greatest dstance from forest edges. FIELD METHODS Equpment Needed (per group: data sheets pencl/pen bnoculars marker flags (5 compass sphercal crown densometer table of random numbers ocular tube laser rangefnder or 30 meter measurng tape feld gude to brds (optonal Locatng Samplng Ponts Samplng ponts should be located wthn a gven habtat type, and along roads and trals (to allow for greater sample szes. Successve sample ponts should be at least 250 meters apart to mnmze countng the same brds more than once. Actual dstances between ponts may have to be less than 250 m, due to lmtatons of tme and small areas of habtat types. Avan Pont Counts Conduct pont counts mmedately after dentfyng your samplng ponts. For fve mnutes, record all speces seen or heard from the pont. Only one person should do speces dentfcatons. A second person may serve as a recorder. For ths exercse, dstngushng between brds of dfferent speces s more mportant than accurately dentfyng those speces. (Your dentfcaton skll wll be evaluated later n the course. If you do not know the name of a brd, record t as speces #1, #2, etc. For example, f you hear the song of an unknown speces, record t as speces #1. If you hear another unknown song that dffers from the frst, record t as speces #2. If you hear song lke the frst, but sung by a dfferent ndvdual, record t as a second brd of speces #1. Brds heard or seen wthn 50 meters of your sample pont should be recorded dstnctly from brds observed beyond 50 meters from the pont. Brds seen or heard flyng overhead should be recorded n the desgnated porton of the data sheet. Count any brds orgnally wthn your habtat that flush whle you approach your pont(s. The followng gudelnes should help answer addtonal questons about pont count procedures. (Excerpted from: Ralph, C. J., S. Droege, and J. R. Sauer, eds. [1995] Montorng Brd Populatons by Pont Counts, USDA-Forest Servce, Gen. Tech. Rep. PSW-GTR-149. brd_habtat3.pdf 2 JM rev: 4/21/2018

3 Avan Abundance and Habtat Structure 1 Placement of [pont] should avod boundares between habtat types, f possble. 2 Tme spent at each count staton should be 5 mnutes 3 The mnmum dstance between pont count statons s 250 m. (For ths lab, <250m s OK. JM 4 Brds prevously recorded at another samplng [pont] should not be recorded agan. 5 All ndvdual brds detected at a [pont] should be recorded. 6 Brds detected wthn a radus of 50 m surroundng the [pont] should be recorded separately from those at all dstances. 7 Only one observer should be permtted to count brds at a sngle [pont]. 8 Brds that were detected flyng over the [pont], rather than detected from wthn the vegetaton, should be recorded separately. 9 Counts should begn mmedately when the observer reaches the [pont]. 10 A brd flushed wthn 50 m of a [pont], as an observer approaches or leaves the [pont], should be counted as beng at the [pont] f the observer feels that ths ndvdual was not seen durng the count perod. 11 A brd gvng an unknown song or call may be found and dentfed after the count perod. 12 No attractng devces or records should generally be used Samplng Habtat Structure 1 Sample habtat structure wthn crcular plots of 25 meters radus. The ponts of the brd counts should be used as centers of your plots. Place flags at four ponts around the permeter of each plot, correspondng to the drectons N, E, S, and W. 2 Complete the upper porton of the Vegetaton Sheet." Wrte a bref descrpton of your surroundngs, ncludng landmarks. 3 At the center and N,E,S, and W ponts n each plot, record presence of folage at the followng eght heghts above ground level: m, m, 0.5-2m, 2-5m, 5-10m, 10-20m, 20-30m, >30m. For heghts > 2m, use an ocular tube to avod basng your presence/absence of folage. 4 At the center and N,E,S, and W ponts n each plot, record percent canopy cover usng a sphercal crown denstometer. 5 Count and record the number of snags (standng dead trees, 3 m tall wthn the plot. 6 Count the number of trees wthn the plot. Record ths number and the decduous and confer subtotals. 7 Measure (usng rangefnder the dstance n meters from the plot center and the nearest forest edge. 8 Retreve all flags used to mark the plot. 9 Repeat steps 1-8 at the remanng sample locatons. 10 Check-n your group s equpment and reconvene to dscuss data analyss. brd_habtat3.pdf 3 JM rev: 4/21/2018

4 DATA ANALYSIS Models for Each Hypothess 1 Abundance (S s a random devaton from the mean (e. Model: S exp( K = 2 2 Abundance ncreases wth folage heght dversty (F. F 3 Abundance ncreases wth snag densty (W. W 4 Abundance ncreases wth canopy cover (C. Model: S exp( C 5 Abundance ncreases decdous (% forest composton (P. P 6 Abundance ncreases wth tree volume (V. V 7 Abundance ncreases wth dstance from forest edge (D. Model: S exp( D Avan Abundance and Habtat Structure 8 Abundance ncreases wth folage heght dversty (F and snag densty (W. Model: S exp( F W k 9 Abundance ncreases wth tree volume (V and percent decduous (P. Model: S exp( V P k 10 Abundance ncreases wth tree volume (V and dstance to edge (D. Model: S exp( VF D k Model Fttng and Selecton k k k Because brd count data are lkely to be Posson dstrbuted, Posson regresson s a natural method for fttng models to the data. Emprcal support for each model relatve to others s evaluated usng Akake s Informaton Crteron (AIC. Some speces may occur n flocks or other groups, leadng to clumped (overdspersed detecton data. In such cases, AIC scores are adusted wth an overdsperson parameter, yeldng quas-aic scores (QAIC. When the rato of sample sze to number of parameters s less than 40, an adustment for small sample szes s appled, producng AIC c or QAIC c. (For detals, see the followng course readngs: Anderson et al J. Wldl. Manage. 64: ; Anderson and Burnham J. Wldl. Manage. 66: We completed steps 1-7 below usng data for one brd speces n the computer lab after collectng feld data n the mornng. 1 Enter your data nto some electronc format. 2 Calculate null devance n number of brd speces (S per pont. Ths s the resdual devance for hypothess 1. 3 Create scatterplots for S versus each predctor varable. Assess the dstrbutons. 4 Ft models 2-10 to the data usng Posson regresson. For each model, determne estmates for ( for bvarate models, and resdual devance. brd_habtat3.pdf 4 JM rev: 4/21/2018

5 Avan Abundance and Habtat Structure 5 Calculate a QAIC c score for each model, usng resdual devance values and K gven above. QAIC s the model selecton crteron approprate to overdspersed (c>1 data, such as abundance counts. The subscrpt c denotes a small sample adustment, requred when n/k < 40. RD 2K( K 1 QAIC c 2K cˆ n K 1 RD = resdual devance for a gven model 2 c ˆ / df 2 = ch-square statstc for global model df = global model degrees of freedom 6 Identfy the model wth the smallest QAIC c score, and subtract t from QAIC c scores for the other models to determne values. QAIC mn(qaic 7 Use values to determne Akake weghts, w, whch measures the probablty that model s the best among all models consdered. 1 exp 2 w R 1 exp r r Identfy the confdence set for best model. The confdence set can be determned n three ways. The smplest n practce s to nclude all models wth 2. A second method ncludes models wth the largest weghts (w, such that the weghts sum to a gven fracton (e.g., 95%. For more nformaton, ncludng the thrd method, see 9 Calculate log-lkelhood R 2 for each model n the confdence set: RD 1 ND log-lkelhood R 2, where RD = resdual devance, and ND = null devance. 10 Interpret results of your analyss to answer the research queston. Consder the followng questons n your nterpretaton. Whch model(s best explaned patterns n speces rchness? How confdent are you n your selecton of best model(s? Why mght the varable(s n that (those model(s be more nformatve about avan speces rchness than the other varables? How strong s the relatonshp descrbed by your selected model(s? (How large s the predctor varable coeffcent? How much of the varaton n speces rchness s explaned by the selected model? (e.g., what s the value of r 2? 11 In the Results secton of your report about ths proect, you should nclude the followng quanttes for each model consdered: (1 log-lkelhood ( RD / cˆ, number of estmated parameters (K, the value of the selecton crteron (QAICc, dfference (, and the Akake weght (w. In addton, you should report the followng for each model n the confdence set: log-lkelhood R 2, model parameters (, and standard errors of model parameters. Do not report P- values or other results of statstcal hypothess testng, whch are not relevant n ths model selecton/nformaton-theoretc approach. 12 You should consder selected models closely, ncludng nterpretaton of all model parameter values. (e.g., How many more brds are there for each addtonal snag? Why that many? brd_habtat3.pdf 5 JM rev: 4/21/2018