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Ecologia de Populações
Prof. Dr. Harold Gordon Fowler
19-3526-4230
popecologia@hotmail.com
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text/html; charset="us-ascii" PRESENTATION NAME
<= span lang=3DPT-BR style=3D'font-size:86%'>O número de indivíduos = de uma espécie por unidade de área ou volume
População: Densidade e = Abundancia
3D"*"Como medimo= s o tamanho populacional?
<= span lang=3DPT-BR style=3D'font-size:86%'>Na maior parte dos casos, não = é possível contar todos os indivíduos numa população&= #13;= População
números (e densidades) são        = ;            &n= bsp;            = ;          estimados por indicadores
<= span style=3D'font-size:86%;color:black'>indiretos,  como= o número
<= span style=3D'font-size:86%;color:black'>de ninhos ou cavas
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Densidade
3D"*"Densidade brut= a - No. de indivíduos por unidade de área (sem referencia a  variação na qualidade do habitat)
3D"*"Densidade ecol= ógica - No. de indivíduos = por unidade de = área de habitat apropriado
3D"*"Abundânc= ia absoluta contra abundância relativa
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Outros métodos de medição para o Homem
A densidade aritmético é a maneira mais comum de medir a densi= dade populacional, mas outros métodos foram desenvolvidos para proporcio= nar uma medida mais precisa da densidade populacional numa área particular.
Densidade Aritmética: O número total de pessoas / área  km².
Densidade Fisiológica: A população total  / a quantidade de terra cultivável.
Densidade Agrícola:= A população rural total / quantidade de terra agrícola.
<= b>Densidade Residencial = ;: O número de pessoas que vivem numa área urbana / a área= da terra residencial.
<= b>Densidade Urbana : O número de pessoa= s que vivem numa área urbana / a área urbana total
<= b>Ótimo ecológico= : A densidade da população que pode ser sustentada pelo recursos naturais da área.
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Densidade
nMensuramos a densidade por amos= tragem
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Densidade=
nDependente sobre a entrada e saída <= /div>
&= #8211;entrada: nascimentos, imigração;
&= #8211;saída: mortes, emigração
------=_NextPart_01C9960D.4B36E5C0 Content-Location: file:///C:/6CC390E5/Densidade_arquivos/slide0165.htm Content-Transfer-Encoding: quoted-printable Content-Type: text/html; charset="us-ascii" PRESENTATION NAME
Determinando a Densidade Populacional:
como= os ecólogos contam?<= /span>
nContagem de todos os indivíduos
nSub-amostragem:
&= #8211;Parcelas
&= #8211;Transectos
Marcação e recaptura (organismos = moveis)
n
------=_NextPart_01C9960D.4B36E5C0 Content-Location: file:///C:/6CC390E5/Densidade_arquivos/slide0153.htm Content-Transfer-Encoding: quoted-printable Content-Type: text/html; charset="us-ascii" PRESENTATION NAME
Ecologia de Populações
nESTIMATIVAS DE PARAMETROS POPULACIONAIS I
n <= /div>
e. Estat&iacu= te;stica
f. Premissas = chaves
g. Amostragem= duplo
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Ecologia de Populações
nESTIMATIVAS DE PARAMETROS POPULACIONAIS I
n <= /div>
Densidade Absoluta
a. 
i. Censos totais na área total
ii. Censos totais em parcelas
premissas críticas
exemplos
aplicação
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Ecologia de Populações
nESTIMATIVAS DE PARAMETROS POPULACIONAIS I
Densidade Absoluta
desenhos de amostragem
eliminando vicio (métodos)
iii.
b. Transeto linear ()
c. Parcelas circulares variáveis
d. Marcação e recaptura ()
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Ecologia de Populações
nESTIMATIVAS DE PARAMETROS POPULACIONAIS I
i. Historia
John Graunt (1662) - para estimar a po= pulação de Londres.
C. G. J. Petersen (1896) - para estimar = es= toques pesqueiros em Dinamarca
F. C. Lincoln (1930) - para estimar pa= tos durante sua migração
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Ecologia de Populações
nESTIMATIVAS DE PARAMETROS POPULACIONAIS I
Aplicaç= ;ões=
Tamanho popul= acional
taxa de exploração
taxa de sobrevivência
taxa de recru= tamento
taxas de movimentação (imigração/e= migração)
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Ecologia de Populações
nESTIMATIVAS DE PARAMETROS POPULACIONAIS I
iii. Recapturas solitárias (k=3D1): Po= pulações fechadas
conceitos fundamentais
Estimador de Lincoln-Petersen
premissas
efe= itos de violações das premissas
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Ecologia de Populações
nESTIMATIVAS DE PARAMETROS POPULACIONAIS I
Recapturas múltiplas (k>1): Po= pulações fechadas
co= nceitos fundamentais
Es= timador de Schnabel
pr= emissas
fontes da variação na probabilidade de ca= ptura
métodos computacionais: programa <= span style=3D'position:absolute;top:90.25%;left:19.28%;width:75.65%;height:5.75= %'>CA= PTURE
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Ecologia de Populações
nESTIMATIVAS DE PARAMETROS POPULACIONAIS I
e. Métodos de Retirada
i. captura por esforço
ii. Mudança de proporção
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População contra Amostra
Amostra
Popula= ção verdeidera
Complete “census” of natural populations is often very difficult!


In reality, tr= ue population is different from sampled population.
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Intervalos de Confiança
Medida de  precisão
95% IC geralmente usado
“95%  probabilidade do que a media verd= adeira está no  IC” (Bayesiano)
“Si o process= o de amostragem se replica várias vezes, o IC coberer= a a media verdadeira em  95% das vezes” (Frequentisto)
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Exatidão =3D Precisão + Vicio
Não preciso
sem vicio
Vicio: = A media da estimativa converge a valor verdadeiro?
Precisão: Quanta variáveis são as estimativas? =
Pr= eciso com vicio
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Pseudo-Replica&cc= edil;ão
n= Problema no amostragem de populaç&oti= lde;es naturais
n= Amostras replicadas são as unidades menores = no qual se aplica independentemente os  “tratamentos”=
n= Na  pseudo-replicação, = existe uma dependência entre as amo= stras replicadas (além dos tratamentos!), 
Replication: t= he selection of multiple samples from a sampled population, to assess variability with= in a treatment.
------=_NextPart_01C9960D.4B36E5C0 Content-Location: file:///C:/6CC390E5/Densidade_arquivos/slide0006.htm Content-Transfer-Encoding: quoted-printable Content-Type: text/html; charset="us-ascii" PRESENTATION NAME
3D"Caixa
3D"Caixa
Popula= ção hipotética
Say we want to= estimate population size in the habitat.
If we subsampl= e one area, and take multiple replicate samples from the same subsample, then there is pseudo replications.
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Definida claramente para a populaç&ati= lde;o alvo
População biológica de interesse
=
Precisa ser bem definida = 3;
&= #8211;Extensão Espacial
n Pais, Estado, Região…
Extensão de Habitat
  Rios, córregos...
Extensão Temporal
  Estação, mês,
n
nPopulaç&ot= ilde;es amostradas devem ser representativas  da população alvo:
Engloba a amplitu= de espacial =
Engloba a amplitude de habitat <= /b>
Engloba a escala<= span style=3D'mso-spacerun:yes'>  temporal dos proc= essos populacionais
nPopulaç&at= ilde;o amostrada deve  representar a agregação espacial e temporal da populaç&at= ilde;o alvo.
Sampling Schem= es
Complete Cover= age
Random Samplin= g
Stratified Ran= dom Sampling
Systematic Sam= pling

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3D"Caixa
Amostr= agem aleatório
 Procedimento estatístico= excelente <= span lang=3DEN-US style=3D'font-family:Arial;font-size:133%;mso-special-format:= lastCR; display:none'>
 Muitos locais podem = estar vazios
Pode pular alguns habitats
•<= /span>Pode= exigir muito tempo para encontrar loca= is
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3D"Caixa
Amostragem aleatório
estratificado
nSeleciona  habitats qualitativamente diferentes:: estrato
Amostragem aleatória <= /span>dentre de cada estrato =
nEsforço varia entre estratos
Media é a media ponderada dos estratos = individuais  (ponderada = pela área do estrato)= 3;
calculo do IC é  complexo (Greenwood pp. 104-105.)
nGaranta cobertura de todos os tipos de  habitats nPermite esforço enfocado onde rende m= ais: densidade elevada, variabilidade, e outros
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=
3D"Caixa=
Amostr= agem sistemático
Amostragem sistemático é útil si a área amostrada (ou = volume) é heterogêneo (Pielou, p.107). Si  o tamanho <= span style=3D'position:absolute;top:36.75%;left:10.11%;width:98.68%;height:6.0%= '>= da mancha e muito maior do que a  distância inter-amostras, então o krigagem pode ser empregado em vez = do amostragem estratificado.
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=
3D"Caixa=
Amostr= agem sistemático
No amostragem estratificado, a área (volume) é dividido em 2 ou = mais porções que são amostradas separadamente.
=
A densidade de amostras em cada estrato deve ser proporcional a variança dos contagens dos indiví= duos no estrato -- neste caso  a precisão máxima  é atingida.  A lei de potência de Taylor= pode <= span lang=3DPT-BR style=3D'font-family:"Times New Roman";font-size:133%;color:#= EAEAEA; mso-color-index:3'>ser usada para prever a variança da  media. A variância = geralmente aumenta com a media, e assim, o estrato com uma densidade maior de indivíduos deve ser amostrado com mais intensidade.
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=
3D"Caixa=
Amostr= agem sistemático
A densidade media da população, M, é estimada como <= /span>= uma media ponderada das densidades  medias, Mi= , em = cada estrato, i= , com pesos, wi= igual a área coberta pelo estrato <= /b>i:
O erro padrão, SE= , da media é igual a
=
=
=
onde = SEi é o error padrão da media do estrato i= .
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3D"Caixa
Amostr= agem sistemático
nViciado sea malha amostrada corresponde a uma periodicidade ambiental
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Esquema= s de Amostragem para evitar
n= Azar: sem ser aleatória ou sistemática: como a primeira planta vista no campo
n= Acessibilidade: locais onde você chega fácil
n= Juízo: locais que você acha que são  “típicos”


Única vantagem é menor custo= de encontar locais
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M&Eacut= e;TODOS DE AMOSTRAGEM
nContagens Direitos
&= #8211;Armadilha, Quadrante, transeto
&= #8211;Avaliar a densidade, CPUE
nMarcação-Recaptura
<= /span>
nSem área
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Contando Indivíduos
nCenso completo:  co= ntagem de cada indivíduo da população
nAmostra da População:=
contagem de indivíduos em partes pequenas da área ( quadramtes= ) e após calcular a densidade = ;
marcação e recaptura
índice de=   abundância relativa (uso de armadilhas)
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CONTAGEM
DIRE= ITO
nContagem de todos= os indivíduos= na unidade amostral nDifícil quando os indivíduos são abundantes = (levantamentos aéreos de= jacarés) nPode subestimar si alguns indivíduos são crípticos= nPode superestimar si os mesmos indiv&iacu= te;duos são contados mais de uma vez =
nEstações que capturam ou registram o passagem de animais moveis
Armadilhas vivas ou pegajosas, redes, câmara automática
Armadilhas com isça, precisa conhecer a  área de atração da isç= ;a
n
ARMADILHA
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Contagem de Indivíduos
nContagem completa:  co= ntagem de cada indivíduo na população
nAmostragem da População:
Contagem de indivíduos em porções pequenas da área (como parcel= as) e depois calcular a densidade
Marcação e recaptura
Índice de abundancia relativa (armadilhas)
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Content-Type: text/html; charset="us-ascii" PRESENTATION NAME
Determinando a Densidade Populacional:
como= os ecólogos contam?<= /span>
nContagem de todos os indivíduos
= 211;Funciona para populações pequenas de organismos= = grandes ou devagar
Orcas
200-300 em registros = histó= ricos
71 em 1973
99 em 1995
88 em 2008
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Determinando a Densidade = Populacional:
como os ecó= ;logos contam?
nContagem de todos os indivíduos
nSub-amostragem:
&= #8211;Transectos
&= #8211; Parcelas=
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Transect 3
Black Point 12/5/2002
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Transect 3
Black Point 12/5/2002
8 Quadrats
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- 40 Parcelas em 5 Transectos
- Fotografado = cada duas semanas entre janeiro de 2001 e dezembro de 2003
- Métod= o de ponto aleatório para quantificar percentagem de cobertura: moluscos, rocha e = “outro”= ;
Quantificando a Mortalidade de Moluscos =
2001=
2001=
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Parcela
nÚtil para organismos pequenos e cesseis
http://simp.ucsc.edu/= Sites/Images/quadrats/pp1mquad.jpg
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Probabi= lidade de visitação deca= i com distância
nAjuste uma função         (g(x) =3D probabilidade de visitação a dist&ac= irc;ncia  x) a número observado em várias distâncias =
Wallaroo em
Queensland
McCallum (2000)=
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Estima&= ccedil;ão da densidade com transetos li= neares =
n= Integral de g(x= ) da a probabilidade media = de visitação na largura inteira do transeto
n= É a  = largura efetiva da banda  (ESW) = do transeto
n= Densidade é D =3D n / (2*L*ESW)
n =3D número observado
L =3D comprimento do transeto
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MARCAÇ&At= ilde;O-RECAPTURA
n= Capture e marque um número conhec= ido de indivíduos
n= Após retornar para capturar de novo
Dan= do tempo necessário para mesclagem mas não mortalidade
n= Fração de indivíduos marcados na amostr= a de recapturas é uma estima= tiva da proporção da população na primeira captura
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nMarcaç&a= tilde;o e recaptura <= /div>
A= nimais são capturados,  marca= dos, e depois recapturados após um período de tempo
Ta= manho Populacional Total =3D CM1<= /span>/M2
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Marcação
Recaptura
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Índice de Lincoln-Peterson
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------=_NextPart_01C9960D.4B36E5C0 Content-Location: file:///C:/6CC390E5/Densidade_arquivos/slide0026.htm Content-Transfer-Encoding: quoted-printable Content-Type: text/html; charset="us-ascii" PRESENTATION NAME
Métodos de Marcação
nTinta
nBandas Coloridas
aves
nMarcas únicas
Mamíferos grandes; mantém registro = fotográfico =
nCorte de dedos
Repteis, anfíbios, roedores
(Sutherland 1996)
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file:///C:/6CC390E5/Densidade_arquivos/slide0150.htm Content-Transfer-Encoding: quoted-printable Content-Type: text/html; charset="us-ascii" PRESENTATION NAME
Índice de Peterson - Lincoln
nM/N =3D m/n,  N =3D Mn/m&= #13;
nN =3D estimativa da abundância =
nM =3D # indivíduos marcados em t1
nm =3D # indivíduos marcados em  t2
nn =3D total # indivíduos capturados em  t2
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Premissas
nMarcação não tem  efeito na mortalidade dos indivíduos marcados
nMarcação não tem  efeito na probabilidade de recaptura de indivíduos marcados
nPopula<= span lang=3DPT-BR style=3D'font-family:"Times New Roman";mso-fareast-font-famil= y:\65B0\7D30\660E\9AD4; mso-hansi-font-family:"Times New Roman";color:#00FF99;mso-fareast-language: ZH-TW'>ção fechada (sem imigração ou <= /span>emigra<= span lang=3DPT-BR style=3D'font-family:"Times New Roman";mso-fareast-font-famil= y:\65B0\7D30\660E\9AD4; mso-hansi-font-family:"Times New Roman";color:#00FF99;mso-fareast-language: ZH-TW'>ção, mortalidade ou reprodução no intervalo temporal entre  t1 e t2) <= /span>
nMistura comple= ta
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Exemplo= de Marcação-Recap= tura =
nUma população de morcegos mora sob uma ponte na Rodovia Bandeirantes.  Segunda feira, 50 morcegos são captur= ados e marcados com bandas nas orelhas.  Terceira feira os mesmos <= /span>biólogos retornam e capturam  100 morcegos.  Desse= s, 13 têm marcas. nQuantos morcegos moram s= ob a  ponte?
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Resposta
nPrimeira amostra =3D  Recapturas marcadas nPopu= lação total        = ;     Recapturas totais
<= span style=3D'visibility:hidden'>n
n50/T= =3D13/100 (onde T é a população de morcegos sob a = ponte)
<= span style=3D'font-size:75%'>nmulti= plica ambos lados por 100T
<= span style=3D'font-size:75%'>n5000= =3D13T
<= span style=3D'font-size:75%'>nT= =3D385 = ;
<= span style=3D'visibility:hidden'>n =
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Métodos de Marcação
nUma população tem taman= ho  N, de modo que N é o número que queremos estimar.  M indivíduos foram capturados, marcados e sol= tos de novo na população. Após um tempo suficiente para os  indivíduos me= sclar, n indivíduos foram capturados, e= desses mtinham marcas. Se presume que a proporção dos organismos recapturados é a mesma que a= proporção de indiv&iacu= te;duos marcados:
<= span style=3D'font-size:88%;visibility:hidden'>n
Tamanho populacional : N =3D nM/m (= ïndice de Lincoln).
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Métodos de Marcação
<= span style=3D'font-size:88%'>nPreci= sa atender as  condiç&oti= lde;es de:
<= span style=3D'font-size:88%;visibility:hidden'>n
n   1. Sem imigraç&atild= e;o, emigração, nascimentos ou mortes entre os tempos de soltura e recaptura. n   2. As probabilidades de ser= em capturados s&ati= lde;o iguais entre os indivíduos  (incluindo os marcados). n   3. Marcas (ou bandas)  não se perdem e podem ser reconhecidos sempre.
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Métodos de Marcação
nAs primeiras duas condiç&otild= e;es geralmente não são realistas, e por isso, várias modificações deste método foram desenvolvid= os que relaxam essas condições. O  método mais popular é de  Jolly-Seber que requer capturando e marcando animais em intervalos regulares do te= mpo. Os animais, marcados e soltos cada vez, = devem ter marcas diferentes para distinguir entre indivíduos marcados em datas diferentes.  Southwood (1978) descreve o algoritmo. =
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Métodos de Marcação
nO método de Jolly-Seber  estima o tamanho populacional em cada data espec&iacut= e;fica; a prime= ira condição pode ser violada. Porém, a segunda condição precis= a ser respeitada. É possível  estimar a taxa de <= span style=3D'position:absolute;top:55.0%;left:12.17%;width:83.52%;height:6.5%'= >mortalidade+emigração r= ate e a taxa de  natalidade+imigração em cada  dia específico. = A premissa é que essas taxas são constante para todos os indivíduos  (incluindo os marcados).
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Métodos de Marcação
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nV&aac= ute;rios outros modelos de captura-recaptura existem, específicos a uma população particular. Por exemplo, a estrutura etária= da população possa ser imp= ortante, ou alguns indiv&= iacute;duos tenham uma maior probabilidade de serem capturados do que outros.
<= span style=3D'font-size:88%;visibility:hidden'>n
<= span style=3D'font-size:88%;visibility:hidden'>n
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Métodos de Marcação
nOutro problema acontece se a  população nã= o possua fronteiras. Neste caso, uma ma= lha de armadilhas pode ser estabelecida, &ea= cute; a porção central da malha é usada na  análise (porque as armadilhas próximas a borda podem ser influenciadas pela  migração). A &aacut= e;rea coberta pela m= alha deve ser muito superior  do q= ue a distância media da dispers&atil= de;o do animal. nPorque a biologia de espécies = é variável, pode ser necessário modificar o modelo de captura-recaptura.
<= span style=3D'font-size:88%;visibility:hidden'>n
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Amostragem por Retirada
nO modelo de retirada: <= /span>dN/dt =3D -aN, onde = a é a taxa de retirada.&#= 13;nA solução dessa equação diferencial é  N =3D Noexp(-at), onde No é a populaç&atild= e;o inicial
O número de indivíduos = capturados por unidade de tempo é igua= l a A(t) =3D aNoexp(-at) Os <= /span>= parâmetros a= e No podem ser <= span style=3D'position:absolute;top:86.0%;left:6.92%;width:65.73%;height:6.0%'>= = estimados usando a regressão não = linear.
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Amostragem por Retirada
nPielou (p. 127) usou um m&eacut= e;todo diferente na estimação dos parâmetros baseada na análise dos dois primeir= os intervalos de tempo só. . Por exemplo, si as capturas nos dois primeiros intervalos d= e tempo foram  29 e 18 indivíduos, então
na =3D (29-18)/29 =3D 0.38.<= /span>
nEsse modelo pode ser gerado sob a premissa de recrutamento de indivíduo= s (emergência de insetos ad= ultos).
------=_NextPart_01C9960D.4B36E5C0 Content-Location: file:///C:/6CC390E5/Densidade_arquivos/slide0037.htm Content-Transfer-Encoding: quoted-printable Content-Type: text/html; charset="us-ascii" PRESENTATION NAME
Sem Área<= /span>
(Greenwood 1996)
nSelecione indivíduos aleatoriamente ou local e medir a distância a vizinho mais próximo
Bom para árvores e sauveir= os
&= #8211;Sutherland pp. 60-62
Good if sessil= e organisms are sparsely distributed.
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Common failures in monitoring <= /span>programs (Elzinga = et al. 2002)
nProblemas técnicos
= 211;Desenho inadequado leva a resultados não conclusivos
Uso de observadores não confiáveis complica interpretação dos resultados = ;
= 211;Perda de dados (incluindo a falta de capacidaee de interpretar registros)
&= #8211;Não análise dos dados =
Flutuação no sistema natural esconde = mudanças causadas pelo manejo
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Common failures in monitoring <= /span>programs (Elzinga = et al. 2002)
<= span style=3D'color:#EAEAEA;mso-color-index:3'>Problemas institucionais
<= span style=3D'color:#EAEAEA;mso-color-index:3'>Termino cedo de monitoramento =
Recursos não suficientes para coletar e analisar os dados O não = uso de dados de monitoramento na tomada de decisões&= #13;A não colocação do monitoram= ento no marco de manejo leva a percepção  (ou realidade) de= que os dados não são relevantes<= span lang=3DEN-US style=3D'color:#EAEAEA;mso-color-index:3;display:none'>=
<= span style=3D'color:#EAEAEA;mso-color-index:3;visibility:hidden'>
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nCuidado com a Escala
nGrão -o tamanho das manchas de habitat do ani= mal relativo a vagilidade do animal, ou capacidade de se <= span style=3D'position:absolute;top:46.0%;left:13.85%;width:88.95%;height:7.5%'= >locomover  a distancias relativamente grandes
n
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nCuidado com a Escala
nGrão Gr= osso - se a vagilidade do organismo é baixa relativa ao tamanho das manchas de habitat
nGrão Fi= no - se a vagilidade do organismo é alta relativa ao tamanho das manchas de habitat = ;
n(Organismos po= dem ocorrer em várias manchas= )
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Métodos indiretos
nExistem vários métodos e medidas que podem ser correlacionadas com a densidade populacional: capturas em = armadilhas, levantamentos visuais, levantamentos de produtos (fezes, ninhos), proporção de  hospedeiros infestados . Medidas indiretas podem = se relacional a densidade populacional p= or meio de análise de regress&ati= lde;o (linear e n&ati= lde;o linear).
n
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=
A coeficiente de correlação, r, é uma medida do grau de <= /i>= associação entre dois variáveis sob um modelo teórico = particular. A correlação estatística do modelo linear = se calcula usando
onde X é Y = são dois variáveis observados simultaneamen= te I vezes. =
<= b>Correlação
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=
Para comparar  r precisamos somar o produto dos dois  <= span style=3D'position:absolute;top:28.5%;left:5.05%;width:102.8%;height:6.0%'>= = variáveis, some de cada variável, e soma do quadrado = de cada variável. Um coeficiente de correlação de 1 <= /span>= = implica que a relação entre os dois variáveis é = perfeita, e um valor de  r de  zero implica que as duas = = variáveis não tem relação.
<= b>Correlação
------=_NextPart_01C9960D.4B36E5C0 Content-Location: file:///C:/6CC390E5/Densidade_arquivos/slide0181.htm Content-Transfer-Encoding: quoted-printable Content-Type: text/html; charset="us-ascii" PRESENTATION NAME
=
O  ”ajuste" do modelo &ea= cute; medido pelo coeficiente de determina&cce= dil;ão, r2= = . As vezes esse valor se multiplica por = 100 para indicar o porcentagem da variação nos = = = dados explicada pelo modelo
<= b>Correlação
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=
O coeficiente de correlação mede o grau de associação entre dois variáveis X= e Y mas a análise de regressão ajusta um mod= elo que descreve essa relação aos = dados Os dados são observações emparedas como  a = = altura e peso de uma amostra de  100 pessoas, ou os número= s de predadores e presas estimados anualmente po= r  20 anos. Assim, os dados consiste= m = = de duas cadeias iguais de dados emparedas; como, = = 100 alturas  (o variável  H) e 100 pesos (W), ou 20 densidades de predadores  (variável<= span style=3D'mso-spacerun:yes'>  P) e 20 densidades de= presa (N= ).
   
<= b>Correlação
------=_NextPart_01C9960D.4B36E5C0 Content-Location: file:///C:/6CC390E5/Densidade_arquivos/slide0183.htm Content-Transfer-Encoding: quoted-printable Content-Type: text/html; charset="us-ascii" PRESENTATION NAME
=
=  Para calcular esses exemplos, prec= isa fazer
   
<= b>Correlação
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=
O desvio padrão, s, é a raiz quadrada da variança. O = desvio padrão é útil na medida da dispersão de dados = = normalmente distribuídos ao redor de seu valor = médio.
<= b>Media, Variança e Desvio Padrão
------=_NextPart_01C9960D.4B36E5C0 Content-Location: file:///C:/6CC390E5/Densidade_arquivos/slide0068.htm Content-Transfer-Encoding: quoted-printable Content-Type: text/html; charset="us-ascii" PRESENTATION NAME
Regressão linear
nO método dos quadrados mínimos é o mais usado para traçar  a linha  ”melhor" de uma nuvem de pontos de dado= s. Esse método ajusta os valores= dos parâmetros da regressão = (a e b) de modo que o soma de quadrados residuais  (=3Dsoma dos desvios quadrados dos pontos da linha) atingia o&n= bsp; mínimo
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y' =3D a + bx =
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Regressão linear
n= O soma dos quadrados residuais é:
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n<= span lang=3DPT-BR style=3D'mso-special-format:lastCR;display:none'> =
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Regressão linear
nA regressão dos quadrados mínimos é um método para encontrar o melhor ajuste do modelo aos dados  por minimizar o soma dos quadrados dos desvios entre = os dados e o modelo (a linha de regressão). Na análise de regressão existe um variável dependente no eixo  Y que está <= span style=3D'position:absolute;top:75.25%;left:18.72%;width:72.28%;height:6.5%= '>associado com um   variável independente  no eixo  X. Se a associação é  linear, então o  modelo apropriado é Y =3D a + bX.
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Regressão linear
nNa regressão dos ”quadrados mínimos", o melhor ajuste resulta pela  minimizarão da  soma de quadrados,  SSQ,= das diferenças entre  valores observados do = variável dependente, Yi,e os valores esperados do modelo, E[Yi];
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Regressão linear
nOs modelos lineares podem ser ajusta= dos pelo calculo da tangente da regressão
Após calcular b= , a tangente da linha de regressão no eixo = Y é:
<= /span>
onde = a é a tangente e a linha = encima identifica a media das duas series de observações&= #13;
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Regressão linear
nNa regressão dos quadrad= os mínimos  encontramos o= melhor ajuste aos dados pela minimizarão doe desvios entre os dados e o modelo. Após isso,= a variação residual dos dados do modelo ajustado se pre= sume é devida a variabilidade aleatória. Si  Yi é o valor atual do variável dependente  associado com o valor de  Xi do variável independente, então o valor esperado de  Yi segundo o modelo é  E[Yi] =3D a = + bXi , e a variança do <= /b>valor observado desta expectação é  <= /span>Yi - (a + bXi).  A "variância ao redor = da linha de regressão, por isso, é:,
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Regressão linear
n= Significança
n<= span lang=3DPT-BR style=3D'display:none'>
n<= span lang=3DPT-BR style=3D'display:none'>
n<= span lang=3DPT-BR style=3D'mso-special-format:lastCR;display:none'> =
R =3D 0.82, N.S.! Porém, a = Significança não implica Significança biológica  ( se R = =3D 0.01)
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Regressão linear
n= Diagnostica de Influencia
n<= span lang=3DPT-BR style=3D'display:none'>
n<= span lang=3DPT-BR style=3D'display:none'>
n<= span lang=3DPT-BR style=3D'mso-special-format:lastCR;display:none'> =
É importante verificar se = os pontos mais influenciais estão corretos. = ;
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Regressão linear
n= Pontos extremos
n<= span lang=3DPT-BR style=3D'display:none'>
n<= span lang=3DPT-BR style=3D'display:none'>
n<= span lang=3DPT-BR style=3D'mso-special-format:lastCR;display:none'> =
Soluções:
1. Ignore o ponto
2. Muda modelo de regressão
3. Não fazer nada
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Regressão linear
n= Não linearidade
n<= span lang=3DPT-BR style=3D'display:none'>
n<= span lang=3DPT-BR style=3D'display:none'>
n<= span lang=3DPT-BR style=3D'mso-special-format:lastCR;display:none'> =
Antes de fazer análise = de = regressão faz uma gráfica <= /span>dos dados.. Use a regressão polinomial ou não linear se a regressão <= /span>não é linear =
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Regressão linear
n= Transformação dos variáveis
n<= span lang=3DPT-BR style=3D'display:none'>
n<= span lang=3DPT-BR style=3D'display:none'>
n<= span lang=3DPT-BR style=3D'mso-special-format:lastCR;display:none'> =
Use transformaçõ= es = somente se têm sentido biológico,  É melhor usar modelos não lineares. =
 
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Regressão linear
Tabela de estatísticas do X quadrado
nEstatística F com outros valores de P:
n=
Use transformações somente se têm sentido biológico,  É = melhor usar modelos não lineares.

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Epinephelus striatus-
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Problema =
nExistem 10,400 ratos vivendo num campo de 1000m x 1000m.  Qual é= a densidade da população?
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Resposta
<= span class=3DBB style=3D'position:absolute;left:-4.1%;top:.39em'>nA área do campo e de 1,000,000 (m2<= /span>).
nA densidade de ratos é 10,40= 0 mice/1,000,000m2=3D.0104/m2.
<= span style=3D'visibility:hidden'>n =
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Ecologia de Populações
Perguntas=
Quais são os benefícios e falhas de índices de <= /span>densidade, ce= nsos totais, transetos lineares e métodos de marcação e recaptura?
Quais s&atild= e;o as premissas de cada método?
.
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Ecologia de Populações
Perguntas=
Que tipos de populações e situações são apropriados p= ara método? Em quais condições esses modelos não devem ser aplicados?
Quais requisitos de cada método e como esses influenciem o= desenho e métodos de estudos de campo? =
Distinguir as premissas dos métodos de marcação e recaptura e de capturas por esforço<= /span>
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Referencias
nBuckland= , S.T., Anderson, D.R., Burnham, K.P. and Laake, J.L. 1993. Distance Sampling: Estimating Abundance of Biological Populations. Chapman= and Hall, London, reprinted 1999 by RUWPA, University of St. Andrews, Scotland. 446pp. Available online at http://www= .colostate.edu/depts/coopunit/distancebook/download.html. nElzinga, C.L., D.W. Salzer, J.W. Willoughby, and J= .P. Gibbs. 2002. Monitori= ng plant and animal populations. Blackwell Science, Malden, NY. = nHayek, L.-A. C., and M. A. Buzas. 1997. Surveying Natural Populations.= Columbia University Press, New York.
nHenderson,P.A. 2003. Practical Methods in Ecology Blackwell, Oxford.
nHilborn, R., and M. Mangel. 1997. The Ecological Detective: Confront= ing Models w= ith Data. Princeton Univ= ersity Press, Princeton, NJ. nMcCallum= , H. 2000. Population Para= meters: Estimation for Ecological Models.  Blackwell, Oxford. = nNew, T. R. 1998. Invertebrate Surveys for Conservation. Oxford University Press, Oxford.
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Referencias
National Park Servi= ce (NPS). 2000. Glacier Bay National Park and Preser= ve: Humpback Whales. Online document at: http://www= .nps.gov/glba/learn/preserve/projects/whale/index.htm
Schmitt, R. J., and= C. W. Osenberg, eds. 1996. Detecting Ecological Imp= acts: Concepts and Applications in Coastal Habitats. Academic Press,= San Diego.
Sutherland, W. J., = ed. 1996. Ecological Census Techniques: A Han= dbook. Cambr= idge University Press, Cambridge, UK.
Thompson, W. L., G.= C. White, and C. Gowan. 1998. Monitoring Vertebrate Populations. Academic Press, San Diego.
Young, L. J., and J= . H. Young. 1998. Statistical Ecology: A Population Per= spective. Kluwer Academic Publishers, Boston.
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‹nº›<= span lang=3DPT-BR style=3D'font-size:67%;mso-special-format:lastCR'> =
Complete “census” of natural populations is often very difficult!
In reality, true population is different from samp= led population.
Replication: t= he selection of multiple samples from a sampled pop= ulation, to assess variability within a treatment.  
Say we want to estimate population size in the hab= itat.
If we subsample one area, and take multiple replicate s= amples from the same subsample, then there is pseudo replications.
Sampling Schemes
Complete Coverage
Random Sampling<= br> Stratified Random Sampling
Systematic Sampling
Good if sessile organisms are sparsely distributed.
Use transformações somente se têm sentido biológico,  É m= elhor usar modelos não lineares.
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!g_supportsPPTHTML || IsNts() || ( !g_scaleInFrame && (!IsWin("PPTSld") || !parent.IsFullScrMode()) ) ) return var padding=3D0; if( IsWin("PPTSld") && parent.IsFramesMode() ) padding=3D6 cltWidth=3Ddocument.body.clientWidth-padding cltHeight=3Ddocument.body.clientHeight-padding factor=3D(1.0*cltWidth)/g_origW if( cltHeight < g_origH*factor ) factor=3D(1.0*cltHeight)/g_origH newSize =3D g_origSz * factor if( newSize < 1 ) newSize=3D1 s=3DSlideObj.style s.fontSize=3DnewSize+"px" s.posWidth=3Dg_origW*factor s.posHeight=3Dg_origH*factor s.posLeft=3D(cltWidth-s.posWidth+padding)/2 s.posTop=3D(cltHeight-s.posHeight+padding)/2 if( g_scaleHyperlinks ) ScaleHyperlinks( factor ) } function _InitAnimations() { animRuntimeInstalled =3D ''+document.body.localTime !=3D 'undefined'; isFullScreen =3D (window.name =3D=3D "PPTSld") && !parent.IsFramesMode(); g_animUseRuntime =3D g_showAnimation && animRuntimeInstalled && !(isFullSc= reen && parent.IsSldVisited()); if( g_animUseRuntime ) { collSeq =3D document.all.tags("seq"); if( collSeq !=3D null ) { for(ii=3D0;ii numSlds ) gSldJumpIdx =3D numSlds; if ( gSldJumpIdx >=3D 0 ) { if ( gSldJumpIdx =3D=3D 0 ) gSldJumpIdx =3D 1; var jumpTo =3D parseInt(gSldJumpIdx); gSldJump =3D 0; gSldJumpIdx =3D ""; win.GoToSld( parent.GetSldList().mList[jumpTo-1].mSldHref ) } } } function _KDH() { if( event.keyCode =3D=3D 8 ) { event.returnValue =3D 0; parent.GoToPrevSld(); } } function DocumentOnClick() { if( IsNts() || parent.HideMenu() ) return; if( ( g_allowAdvOnClick && (window.name=3D=3D"PPTSld") && !parent.IsFrames= Mode() ) || (event && event.keyCode=3D=3D32) ) { if( g_animUseRuntime && g_animMainSequence && g_animMainSequence.cangonex= t ) return; parent.GoToNextSld(); } } var g_supportsPPTHTML =3D SupportsPPTHTML(), g_scaleInFrame =3D 1, gId=3D""= , g_bgSound=3D"", g_scaleHyperlinks =3D false, g_allowAdvOnClick =3D 1, g_showInBrowser = =3D 0, gLoopCont =3D 0, gUseSldTimings =3D 1; var g_showAnimation =3D g_supportsPPTHTML && SupportsPPTAnimation() && ( (w= indow.name=3D=3D"PPTSld" && !parent.IsFramesMode()) || g_showInBrowser );va= r g_animManager =3D null; var g_animUseRuntime =3D false; var g_animItemsToHide, g_animInteractiveItems, g_animSlideTime; var g_animMainSequence =3D null; var ENDSHOW_MESG=3D"Fim da apresentação de slides. 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function FullScrInit() { g_allowAdvOnClick =3D GetCurSld().mAdvOnClk document.body.style.backgroundColor=3D"black" document.oncontextmenu=3Dparent._CM; document.onkeydown =3D _KDH; document.ondragstart=3DCancel document.onselectstart=3DCancel self.focus() } function Redirect( frmId ) { var str=3Ddocument.location.hash,idx=3Dstr.indexOf('#'), sId=3DGetSldId() if(idx>=3D0) str=3Dstr.substr(1); if( window.name !=3D frmId && ( sId !=3D str) ) { obj =3D GetObj("Main-File") window.location.href=3Dobj.href+"#"+sId return 1 } return 0 } var MHTMLPrefix =3D CalculateMHTMLPrefix(); function CalculateMHTMLPrefix() { if ( document.location.protocol =3D=3D 'mhtml:') { href=3Dnew String(document.location.href) Start=3Dhref.indexOf('!')+1 End=3Dhref.lastIndexOf('/')+1 if (End < Start) return href.substring(0, Start) else return href.substring(0, End) } return ''; } function GetTags(base,tag) { if(g_supportsPPTHTML) return base.all.tags(tag); else return base.getElementsByTagName(tag); } function UpdNtsPane(){ if(frames["PPTNts"]) PPTNts.location.replace( MHTMLP= refix+GetHrefObj( gCurSld ).mNtsHref ) } function UpdNavPane( sldIndex ){ if(gNavLoaded) PPTNav.UpdNav() } function UpdOtNavPane(){ if(gOtlNavLoaded) PPTOtlNav.UpdOtlNav() } function UpdOtlPane(){ if(gOtlLoaded) PPTOtl.UpdOtl() } function SetHasNts( fVal ) { if( gHasNts !=3D fVal ) { gHasNts=3DfVal UpdNavPane() } } function ToggleOtlText() { gOtlTxtExp=3D!gOtlTxtExp UpdOtlPane() } function ToggleOtlPane() { frmset=3Ddocument.all("PPTHorizAdjust") frm=3Ddocument.all("PPTOtl") if( gOtlOpen ) frmset.cols=3D"*,100%" else frmset.cols=3D"25%,*" gOtlOpen=3D!gOtlOpen frm.noResize=3D!frm.noResize UpdOtNavPane() } function ToggleNtsPane() { frmset=3Ddocument.all("PPTVertAdjust") frm=3Ddocument.all("PPTNts") if( gNtsOpen ) frmset.rows=3D"100%,*" else frmset.rows=3D"*,20%" gNtsOpen=3D!gNtsOpen UpdNtsPane() } function ClearMedia() { if (PPTSld.pptSound) PPTSld.pptSound.loop =3D 0; } function FullScreen() { if ( PPTSld.g_animUseRuntime ) PPTSld.document.body.pause(); ClearMedia(); var href =3D ( document.location.protocol =3D=3D 'mhtml:') ? FULLSCR_HREF = : FULLSCR_HREF+"#"+GetHrefObj(gCurSld).mSldHref; if(PPTNav.event.ctrlKey) { var w =3D (window.screen.availWidth * 1.0) / 2.0 var h =3D w * (PPTSld.g_origH * 1.0) / PPTSld.g_origW win =3D window.open( MHTMLPrefix+href,null,"toolbar=3D0,resizable=3D1,top= =3D0,left=3D0," + "width=3D"+ w + ",height=3D" + h ); if( PPTSld.g_animUseRuntime ) win.document.body.PPTSldFrameset=3Dwindow; } else { win =3D window.open( MHTMLPrefix+href,null,"fullscreen=3Dyes" ); if( PPTSld.g_animUseRuntime ) win.document.body.PPTSldFrameset=3Dwindow; } } function ToggleVNarration() { rObj=3DPPTSld.document.all("NSPlay") if( rObj && !PPTSld.g_animUseRuntime ) { if( (rObj.playState =3D=3D 1)||(rObj.playState =3D=3D 0) ) rObj.Play() else if( rObj.playState =3D=3D 2 ) rObj.Pause() else return; } else if( PPTSld.g_animUseRuntime ) { narObj =3D PPTSld.document.all("narrationID") if( narObj ) narObj.togglePause() } } function GetCurSldNum() { obj=3DGetHrefObj(gCurSld) if( obj.mOrigVis =3D=3D 1 ) return obj.mSldIdx else return gCurSld } function GetNumSlds() { if( GetHrefObj(gCurSld).mOrigVis =3D=3D 1 ) return GetSldList().mNumVisSlds; else return GetSldList().mList.length } function GetSldNum( href ) { for(ii=3D0; ii 1 ) PopSldList(); else if( !IsFramesMode() ) { if( gLoopCont ) GoToFirst() else EndShow() } } function GoToPrevSld() { ii=3DgCurSld-1 if( ii > 0 ) { obj=3DGetHrefObj(ii) while ( obj && ( obj.mVis =3D=3D 0 ) && ( ii>0 ) ) obj=3DGetHrefObj(--ii) if( ii =3D=3D 0 ) ii=3D1 GoToSldNum(ii) } } function GoToFirst(){ GoToSld( GetHrefObj(1).mSldHref ) } function GoToLast() { ii=3DGetSldList().mList.length if( ii !=3D gCurSld ) GoToSld( GetHrefObj(ii).mSldHref ) } function GoToSldNum( num ) { if( PPTSld.event ) PPTSld.event.cancelBubble=3Dtrue obj =3D GetHrefObj( num ) obj.mVis=3D1 gPrevSld=3DgCurSld gCurSld =3D num; PPTSld.location.replace(MHTMLPrefix+obj.mSldHref) if( IsFramesMode() ) { UpdNavPane(); UpdOtlPane(); UpdNtsPane() } } function GoToSld( href ) { if( PPTSld.event ) PPTSld.event.cancelBubble=3Dtrue GetHrefObj( GetSldNum(href) ).mVis=3D1 PPTSld.location.replace(MHTMLPrefix+href) } function SldUpdated( id ) { if( id =3D=3D GetHrefObj(gCurSld).mSldHref ) return gPrevSld=3DgCurSld gCurSld=3DGetSldNum(id) if( IsFramesMode() ) { UpdNavPane(); UpdOtlPane(); UpdNtsPane() } } function PrevSldViewed(){ GoToSld( GetHrefObj(gPrevSld).mSldHref ) } function HasPrevSld() { return ( gIsEndShow || ( gCurSld !=3D 1 && GetHrefO= bj( gCurSld-1 ).mVis =3D=3D 1 )||( GetCurSldNum() > 1 ) ) } function HasNextSld() { return (GetCurSldNum() !=3D GetNumSlds()) } function CloseWindow() { if( HideMenu() ) return; var event =3D PPTSld.event; if( !IsFramesMode() && event && (event.keyCode=3D=3D27 || event.keyCode=3D= =3D32 || event.type=3D=3D"click" ) ) window.close( self ); CatchNumKeys( self, event ); } function Unload() { gIsEndShow=3D0; } function SetupEndShow() { gIsEndShow=3D1; PPTSld.document.body.scroll=3D"no"; PPTSld.document.onkeypress=3DCloseWindow; PPTSld.document.onclick=3DCloseWindow; PPTSld.document.oncontextmenu=3D_CM; } function EndShow() { if( IsFramesMode() ) return if( PPTSld.event ) PPTSld.event.cancelBubble=3Dtrue doc=3DPPTSld.document doc.open() doc.writeln('


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') doc.close() } function SetSldVisited(){ GetSldList().mList[gCurSld-1].mVisited=3Dtrue } function IsSldVisited(){ return GetSldList().mList[gCurSld-1].mVisited } function hrefList( sldHref, visible, advDelay, advClk ) { this.mSldHref=3D this.mNtsHref =3D sldHref this.mOrigVis=3D this.mVis =3D visible this.mVisited=3D false this.mAdvDelay=3D advDelay this.mAdvOnClk=3D advClk } function SldList(arr,curSld,fEnd) { this.mCurSld =3D curSld; this.mList =3D new Array(); var idx =3D 1; for(ii=3D0;ii 0) { PushSldList(sldList,fEnd); gCurSld =3D 1; } else if( PPTSld.event ) PPTSld.event.cancelBubble=3Dtrue } function PushSldList(arr,fEnd) { var ii =3D gSldStack.length; gSldStack[ii] =3D new SldList(arr,gCurSld,fEnd); GoToSld( gSldStack[ii].mList[0].mSldHref ); } function PopSldList() { if (gSldStack[gSldStack.length-1].fEndShow) EndShow() else { gCurSld =3D gSldStack[gSldStack.length-1].mCurSld; gSldStack[gSldStack.length-1] =3D null; gSldStack.length--; var sldList =3D gSldStack[gSldStack.length-1]; GoToSld( sldList.mList[gCurSld - 1].mSldHref ); } } var custShowList=3Dnew Array(); function ImgBtn( oId,bId,w,action ) { var t=3Dthis t.Perform =3D _IBP t.SetActive =3D _IBSetA t.SetInactive=3D _IBSetI t.SetPressed =3D _IBSetP t.SetDisabled=3D _IBSetD t.Enabled =3D _IBSetE t.ChangeIcon =3D null t.UserAction =3D action t.ChgState =3D _IBUI t.mObjId =3D oId t.mBorderId=3D bId t.mWidth =3D w t.mIsOn =3D t.mCurState =3D 0 } function _IBSetA() { if( this.mIsOn ) { obj=3Dthis.ChgState( gHiliteClr,gShadowClr,2 ) obj.style.posTop=3D0 } } function _IBSetI() { if( this.mIsOn ) { obj=3Dthis.ChgState( gFaceClr,gFaceClr,1 ) obj.style.posTop=3D0 } } function _IBSetP() { if( this.mIsOn ) { obj=3Dthis.ChgState( gShadowClr,gHiliteClr,2 ) obj.style.posLeft+=3D1; obj.style.posTop+=3D1 } } function _IBSetD() { obj=3Dthis.ChgState( gFaceClr,gFaceClr,0 ) obj.style.posTop=3D0 } function _IBSetE( state ) { var t=3Dthis GetObj( t.mBorderId ).style.visibility=3D"visible" if( state !=3D t.mIsOn ) { t.mIsOn=3Dstate if( state ) t.SetInactive() else t.SetDisabled() } } function _IBP() { var t=3Dthis if( t.mIsOn ) { if( t.UserAction !=3D null ) t.UserAction() if( t.ChangeIcon ) { obj=3DGetObj(t.mObjId) if( t.ChangeIcon() ) obj.style.posLeft=3Dobj.style.posLeft+(t.mCurState-4)*t.mWidth else obj.style.posLeft=3Dobj.style.posLeft+(t.mCurState-0)*t.mWidth } t.SetActive() } } function _IBUI( clr1,clr2,nextState ) { var t=3Dthis SetBorder( GetObj( t.mBorderId ),clr1,clr2 ) obj=3DGetObj( t.mObjId ) obj.style.posLeft=3Dobj.style.posLeft+(t.mCurState-nextState)*t.mWidth-obj= .style.posTop t.mCurState=3DnextState return obj } function TxtBtn( oId,oeId,action,chkState ) { var t=3Dthis t.Perform =3D _TBP t.SetActive =3D _TBSetA t.SetInactive=3D _TBSetI t.SetPressed =3D _TBSetP t.SetDisabled=3D _TBSetD t.SetEnabled =3D _TBSetE t.GetState =3D chkState t.UserAction =3D action t.ChgState =3D _TBUI t.mObjId =3D oId t.m_elementsId=3D oeId t.mIsOn =3D 1 } function _TBSetA() { var t=3Dthis if( t.mIsOn && !t.GetState() ) t.ChgState( gHiliteClr,gShadowClr,0,0 ) } function _TBSetI() { var t=3Dthis if( t.mIsOn && !t.GetState() ) t.ChgState( gFaceClr,gFaceClr,0,0 ) } function _TBSetP() { if( this.mIsOn ) this.ChgState( gShadowClr,gHiliteClr,1,1 ) } function _TBSetD() { this.ChgState( gFaceClr,gFaceClr,0,0 ) this.mIsOn =3D 0 } function _TBSetE() { var t=3Dthis if( !t.GetState() ) t.ChgState( gFaceClr,gFaceClr,0,0 ) else t.ChgState( gShadowClr,gHiliteClr,1,1 ) t.mIsOn =3D 1 } function _TBP() { var t=3Dthis if( t.mIsOn ) { if( t.UserAction !=3D null ) t.UserAction() if( !t.GetState ) return if( t.GetState() ) t.SetPressed() else t.SetActive() } } function _TBUI( clr1,clr2,lOffset,tOffset ) { SetBorder( GetObj( this.mObjId ),clr1,clr2 ) Offset( GetObj( this.m_elementsId ),lOffset,tOffset ) } function Offset( obj, top, left ){ obj.style.top=3Dtop; obj.style.left=3Dle= ft } function SetBorder( obj, upperLeft, lowerRight ) { s=3Dobj.style; s.borderStyle =3D "solid" s.borderWidth =3D 1 s.borderLeftColor =3D s.borderTopColor =3D upperLeft s.borderBottomColor=3D s.borderRightColor =3D lowerRight } function GetBtnObj(){ return gBtnArr[window.event.srcElement.id] } function BtnOnOver(){ b=3DGetBtnObj(); if( b !=3D null ) b.SetActive() } function BtnOnDown(){ b=3DGetBtnObj(); if( b !=3D null ) b.SetPressed() } function BtnOnOut(){ b=3DGetBtnObj(); if( b !=3D null ) b.SetInactive() } function BtnOnUp() { b=3DGetBtnObj() if( b !=3D null ) b.Perform() else Upd() } function GetNtsState(){ return parent.gNtsOpen } function GetOtlState(){ return parent.gOtlOpen } function GetOtlTxtState(){ return parent.gOtlTxtExp } function NtsBtnSetFlag( fVal ) { s=3Ddocument.all.item( this.m_flagId ).style s.display=3D"none" if( fVal ) s.display=3D"" else s.display=3D"none" } function _BSetA_Border(){ b =3D gBtnArr[this.mObjId]; if( b !=3D null ) b.S= etActive() } function _BSetI_Border(){ b =3D gBtnArr[this.mObjId]; if( b !=3D null ) b.S= etInactive() } var gHiliteClr=3D"THREEDHIGHLIGHT",gShadowClr=3D"THREEDSHADOW",gFaceClr=3D"= THREEDFACE" var gBtnArr =3D new Array() gBtnArr["nb_otl"] =3D new TxtBtn( "nb_otl","nb_otlElem",parent.ToggleOtlPan= e,GetOtlState ) gBtnArr["nb_otlElem"] =3D new TxtBtn( "nb_otl","nb_otlElem",parent.ToggleOt= lPane,GetOtlState ) gBtnArr["nb_nts"] =3D new TxtBtn( "nb_nts","nb_ntsElem",parent.ToggleNtsPan= e,GetNtsState ) gBtnArr["nb_prev"]=3D new ImgBtn( "nb_prev","nb_prevBorder",30,parent.GoToP= revSld ) gBtnArr["nb_next"]=3D new ImgBtn( "nb_next","nb_nextBorder",30,parent.GoToN= extSld ) gBtnArr["nb_sldshw"]=3D new ImgBtn( "nb_sldshw","nb_sldshwBorder",18,parent= .FullScreen ) gBtnArr["nb_sldshwBorder"] =3D new TxtBtn( "nb_sldshw","nb_sldshwBorder",pa= rent.FullScreen,null ) gBtnArr["nb_sldshwBorder"].SetActive =3D _BSetA_Border; gBtnArr["nb_sldshwBorder"].SetInactive =3D _BSetI_Border; gBtnArr["nb_sldshwText"] =3D new TxtBtn( "nb_sldshw","nb_sldshwText",parent= .FullScreen,null ) gBtnArr["nb_sldshwText"].SetActive =3D _BSetA_Border; gBtnArr["nb_sldshwText"].SetInactive =3D _BSetI_Border; gBtnArr["nb_voice"] =3D new ImgBtn( "nb_voice","nb_voiceBorder",18,parent.= ToggleVNarration ) gBtnArr["nb_otlTxt"]=3D new ImgBtn( "nb_otlTxt","nb_otlTxtBorder",23,parent= .ToggleOtlText ) gBtnArr["nb_nts"].m_flagId=3D "notes_flag" gBtnArr["nb_nts"].SetFlag =3D NtsBtnSetFlag gBtnArr["nb_otlTxt"].ChangeIcon=3D GetOtlTxtState var sNext=3D"Avançar",sPrev=3D"Voltar",sEnd=3D"Finalizar apresenta&c= cedil;ão",sFont=3D"Arial",sArrow=3D"Seta",sFreeform=3D"Forma livre",= sRect=3D"Retângulo",sOval=3D"Elipse" function ShowMenu() { BuildMenu(); var doc=3DPPTSld.document.body,x=3DPPTSld.event.clientX+doc.scrollLeft,y= =3DPPTSld.event.clientY+doc.scrollTop m =3D PPTSld.document.all.item("ctxtmenu") m.style.pixelLeft=3Dx if( (x+m.scrollWidth > doc.clientWidth)&&(x-m.scrollWidth > 0) ) m.style.pixelLeft=3Dx-m.scrollWidth m.style.pixelTop=3Dy if( (y+m.scrollHeight > doc.clientHeight)&&(y-m.scrollHeight > 0) ) m.style.pixelTop=3Dy-m.scrollHeight m.style.display=3D"" } function _CM() { if( !parent.IsFullScrMode() ) return; 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charset="us-ascii" PRESENTATION NAME ------=_NextPart_01C9960D.4B36E5C0 Content-Location: file:///C:/6CC390E5/Densidade_arquivos/outline.htm Content-Transfer-Encoding: quoted-printable Content-Type: text/html; charset="us-ascii"
Apresenta= ção de slides
Estrutura de tópicos
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