Methodological and Ecological Insights on Wind Farm Environmental Impact Assessment

Mafalda Silva Moreira Mestrado em Ecologia e Ambiente Departamento de Biologia 2018

Orientador Doutor Nuno Formigo, Professor Auxiliar, Faculdade de Ciências da Universidade do Porto

Coorientador Doutora Helena Coelho, Bioinsight

Todas as correções determinadas pelo júri, e só essas, foram efetuadas.

O Presidente do Júri,

Porto, ______/______/______

FCUP iii Methodological and Ecological Insights on Wind Farm Environmental Impact Assessment

FCUP iv Methodological and Ecological Insights on Wind Farm Environmental Impact Assessment

Dissertação submetida à Faculdade de Ciências da Universidade do Porto, para a obtenção do grau de Mestre em Ecologia e Ambiente, da responsabilidade do Departamento de Biologia. A presente tese foi desenvolvida sob a orientação científica do Doutor Nuno Eduardo Malheiro Magalhães Esteves Formigo, Professor Auxiliar da FCUP e coorientação da Doutora Helena Isabel Soares Dinis Coelho, técnica superior da empresa Bioinsight.

FCUP v Methodological and Ecological Insights on Wind Farm Environmental Impact Assessment

FCUP vi Methodological and Ecological Insights on Wind Farm Environmental Impact Assessment

Agradecimentos

Para que a presente dissertação de mestrado se tornasse uma realidade foi necessária a colaboração, incentivo e apoio de várias pessoas, às quais gostaria aqui de manifestar a minha sincera gratidão.

Em primeiro lugar, à Bioinsight, que, depois de tanto procurar e de tantos nãos ter ouvido, me deu a oportunidade de integrar numa equipa multidisciplinar de trabalho e me disponibilizou todos os meios imprescindíveis à elaboração da minha tese de mestrado. Nesta equipa adquiri conhecimentos e competências que vou levar sempre comigo. Ainda maior a minha sorte quando esta equipa esteve disposta a dispensar tempo precioso para me orientar e ensinar. A todos os intervenientes e, com especial carinho à Catarina Ferreira e ao Pedro Pereira por me terem ensinado e aturado no trabalho de campo, um grande obrigado!

Ao orientador Professor Nuno Formigo, que depois de tanto me aturar ao longo do meu percurso académico, aceitou ajudar-me nesta etapa da minha vida. Obrigada pela constante orientação, apoio prestado, disponibilidade, permanente aconselhamento e pela inesgotável paciência. Desempenhou um papel crucial, não apenas diretamente no desenvolvimento deste trabalho, mas também pessoalmente. Levou-me a encarar os meus maiores entraves e a combater os mesmos, preparando- me para os desafios que se seguem a esta etapa. Acima de tudo, tentou sempre fazer- me acreditar nas minhas capacidades e relativizar tudo aquilo que para mim parecia um verdadeiro Adamastor. No fundo, “se é problema tem solução, se não tem solução não é problema”, obrigada Professor!

À coorientadora Doutora Helena Coelho, pelo seu incentivo, preocupação e pelo encorajamento que sempre me tentou transmitir para o desenvolvimento do trabalho. Agradeço também todos os estímulos e desafios que me colocou para a realização deste projeto que me tornaram uma pessoa mais proativa e preparada para o futuro.

Ao professor Rubim Almeida, pela disponibilidade e ajuda prestada na identificação da flora presente nos locais de estudo, sempre com a melhor disposição.

Ao professor David Gonçalves que sempre se mostrou, não só disponível para ajudar na identificação da avifauna detetada, como também preocupado com o bom decorrer do trabalho. FCUP vii Methodological and Ecological Insights on Wind Farm Environmental Impact Assessment

Não podia deixar de agradecer à Diana Vieira, que me acompanhou no desenvolvimento do trabalho com a maior das dedicações e fez questão de me relembrar, confiando nas suas palavras, que o resultado iria compensar todo o esforço.

À melhor aventureira que eu conheço, Carolina Barbosa, que não podia faltar nesta lista depois de me ter acompanhado nas saídas de campo. Tenho a agradecer não só a ajuda que me deu, como os bons momentos que se partilharam com tanta boa disposição e a dose certa de parvoíce. Nem todos têm a sorte de poder trabalhar com alguém que possam passar a considerar amigo!

Sou muito grata à minha família, principalmente aos meus pais por me terem proporcionado as condições para acabar um curso superior e me terem apoiado sempre nas minhas escolhas, como também aos restantes familiares pelo contínuo incentivo e preocupação. Porque não há nada melhor que chegar a casa e ouvir “tu consegues tudo”! Ao Diogo, que me acompanhou desde o primeiro passo nesta faculdade, um gigante obrigado por ter sido o meu grande apoio em todos os momentos, por ter ouvido todos os meus desabafos a qualquer hora do dia, por ter sacrificado muito do nosso tempo, por retribuir com o maior carinho e por acreditar sempre em mim. Não esquecendo aqueles que trouxeste à minha vida e que têm sempre torcido por mim.

Espero ser um orgulho para ti tanto como tu és para mim.

Não podia deixar passar em branco os amigos que se preocuparam durante todo o percurso e que sempre me deram força para continuar. No meio destes um obrigado ao João Pereira e ao João Faria que abdicaram de um pouco do seu tempo e trabalho para me ajudar. Um especial carinho pelas duas velhotas, Rosa e Vanessa, que comigo partilharam a sala onde muito do trabalho foi desenvolvido. Não teria sido a mesma coisa sem a vossa companhia diária, sem as nossas pausas para mais uma gargalhada e, principalmente, sem o nosso espírito de apoio mútuo. Obrigada por confiarem nas minhas capacidades e pela motivação contínua. Por fim, Marta Gonçalves, obrigada por me provares que a distância nunca será um impedimento para nos apoiarmos em todas as etapas das nossas vidas.

A todos, Muito Obrigada!

FCUP viii Methodological and Ecological Insights on Wind Farm Environmental Impact Assessment

Resumo

A produção de energia renovável pela ação do vento (energia eólica) é uma componente essencial para responder aos requisitos energéticos mundiais e, ao mesmo tempo, atingir os objetivos a nível Europeu da redução de emissões de gases com efeito de estufa, combatendo assim as alterações climáticas. Contudo, o desenvolvimento do setor eólico apresenta algumas ameaças potenciais significativas, onde estão incluídas as ameaças à gestão da vida selvagem. As comunidades de aves e morcegos são a principal preocupação, devido à possibilidade de morte por colisão com as turbinas. Os impactes dos parques eólicos na fase de pós-construção são avaliados através de protocolos de monitorização, onde se inclui a quantificação da mortalidade de aves e morcegos, tendo em conta a remoção das carcaças e a eficiência de deteção de carcaças do técnico de prospeções (fatores de correção na estimativa de mortalidade). Estas duas últimas fontes de enviesamento na estimativa da mortalidade dependem de várias variáveis, tais como as características do local de colocação dos modelos de detetabilidade ou da composição da comunidade de necrófagos, respetivamente, entre outros. O objetivo geral desta dissertação de mestrado passa por otimizar a metodologia aplicada na avaliação de impactos na fase de pós-construção e aumentar o conhecimento ecológico relacionado com a avaliação de impacto ambiental da energia eólica, de forma a tentar melhorar os fatores de correção usados na estimativa de mortalidades dos parques eólicos, através de programas de monitorização na fase de pós-construção. Mais precisamente, no presente trabalho, é proposta a utilização de novos modelos de detetabilidade na avaliação de impactes ambientais. Para além disso, outras questões ecológicas são colocadas, tais como o distanciamento induzido nos necrófagos pelas infraestruturas eólicas e a existência de um padrão de remoção entre parques eólicos distribuídos ao longo do território português. Foram realizados testes de remoção e de detetabilidade, de forma a calcular os fatores de correção inerentes e as fotos obtidas, pelo método de fotoarmadilhagem, foram analisadas para determinar a comunidade de necrófagos existente. Os resultados mostram que a estação do ano influencia a persistência das carcaças de forma diferente de acordo com o tipo de carcaça. Relativamente ao padrão de remoção de carcaças entre diferentes parques eólicos, apesar de terem sido observadas algumas semelhanças em alguns casos, não foi possível estabelecer uma relação comum entre a composição da comunidade de necrófagos e a persistência das carcaças que fosse válida para todos os parques eólicos ao longo do território português. Foi também encontrada uma influência significativa da distância das turbinas na persistência das carcaças, quando a estação do ano é FCUP ix Methodological and Ecological Insights on Wind Farm Environmental Impact Assessment

considerada. No que diz respeito ao objetivo da detetabilidade dos modelos, os novos modelos apresentaram, no geral, uma maior detetabilidade, apesar de esta não ser estatisticamente significativa. Para além disso, foi possível demonstrar que a eficiência do técnico de prospeção pode variar de acordo com a classe de visibilidade e o tamanho do modelo. Futuras direções de trabalho com base nesta Tese de mestrado passam por perceber a importância relativa do número total de indivíduos, da riqueza específica e da diversidade da comunidade de necrófagos, tal como da competição intra- e interespecífica, na persistência das carcaças. Este foi o primeiro passo para perceber a relação entre as características da comunidade de necrófagos e a remoção de carcaças devendo, por isso, ser replicado em estudos futuros. Outras gamas de distâncias deveriam, também, em trabalhos futuros, ser testadas conjugadas com outras variáveis. Por fim, os novos modelos de detetabilidade (e adicionalmente outros para simular as espécies passeriformes) poderiam ser estudados noutros parques eólicos, com o objetivo de avaliar quais originam resultados menos enviesados.

Palavras-chave: parque eólico, remoção de carcaças, modelos de detetabilidade, comunidade de necrófagos, comportamento de evitação, AIA monitorização pós- construção

FCUP x Methodological and Ecological Insights on Wind Farm Environmental Impact Assessment

Abstract

Renewable energy production by wind power is a vital component to meet worldwide demands as well as to accomplish Europe-wide objectives to reduce the emissions of greenhouse gases, reinforcing the fight against climatic change. However, its development represents some significant potential threats, including the ones for wildlife management. Bird and bat communities are the major concern due to the exiting possibility of deaths by collision with the turbines. Monitoring protocols at wind farms were created for post-construction impact assessment, which accounts for bird and bat mortality caused by collision as also for carcass removal and search efficiency (correctional factors of mortality estimation). These last two mortality estimation biases are dependent on several variability sources, such as models’ placement site characteristics or ’ composition, respectively, among other factors. In a broad scale, the objective of this master thesis is to improve the applied methodology in post-construction impact assessment and increase the ecologic knowledge correlated with wind energy environmental impact assessment, in an attempt to upgrade correctional factors used to estimate mortality in Wind Power Plants through post-construction monitoring programmes. More precisely, in this work, it is proposed the use of new detectability models in post-construction environmental impact assessment. Moreover, some other ecological aspects are studied, such as the turbine induced avoidance and the carcass removal pattern between wind farms spread along Portuguese territory. Carcass removal and detectability trials were performed for correctional factors calculation purposes and the obtained photos, through camera trap method, were used to analyse the existent scavenger community. Results showed that season influenced carcass persistence differently concerning carcass type. Regarding the carcass removal pattern between wind farms, it was not possible to establish a common relation between scavenger community composition and carcass-persistence, valid for all the available wind farms along the Portuguese territory, despite some observed similarities in some cases. It was also found a significant influence of turbines distance to carcass persistence when season variable was considered. Concerning the detectability models’ objective, new models presented, in general, a higher detectability, although not statistically significant. Moreover, it was possible to demonstrate that searcher efficiency can vary both with visibility class and models’ size. Understanding the relative importance of the total number of individuals, and scavenger community diversity, as well as the effects of intra and interspecific on carcass persistence would be an interesting future work direction. FCUP xi Methodological and Ecological Insights on Wind Farm Environmental Impact Assessment

Moreover, this first step into looking for a relation between scavenger community characteristics and carcass removal should be replicated. In future works, another distance ranges should be tested conjugated with other variables. Finally, the new models could be tested on other wind farms plus a new one for passerines species for assessing which ones originate less biased results.

Keywords: wind farm, carcass removal, detectability models, scavenger community, avoidance behaviour, EIA post-construction monitoring

FCUP xii Methodological and Ecological Insights on Wind Farm Environmental Impact Assessment

Table of Contents

1. Introduction ...... 1

1.1 The Energetic Challenge ...... 1

1.1.1 Wind Energy in Portugal ...... 2

1.2 Wind Energy Impacts ...... 4

1.3 Bird and Bat Fatalities ...... 14

1.4 Environmental Impact Assessment ...... 21

1.5 Monitoring Methodologies and Correctional Factors ...... 23

1.5.1 Carcass Removal/Decay ...... 23

1.5.2 Searcher Efficiency ...... 27

1.6 Mortality Estimators ...... 32

1.7 Bioinsight Company ...... 36

1.8 Thesis objectives ...... 37

2. Material and Methods ...... 39

2.1 Study Area ...... 39

2.2 Carcass Removal Trials ...... 46

2.3 Searcher Efficiency Trials ...... 50

2.4 Data Treatment ...... 54

2.5 Data Analysis ...... 56

2.5.1 Correctional Factors Calculation ...... 56

2.5.2 Scavenger Communities ...... 57

2.5.3 Carcass - Persistence Versus Turbine Distances ...... 57

2.5.4 Use of New Models in Detectability Trials ...... 58

3. Results and Discussion ...... 59

3.1 Carcass Persistence Calculation ...... 59

3.2 Scavenger Communities ...... 68

3.2.1 Photo Analysis ...... 68

3.2.2 Carcass Persistence Between Wind Farms ...... 73 FCUP xiii Methodological and Ecological Insights on Wind Farm Environmental Impact Assessment

3.3 Carcass - Persistence Versus Turbine Distances ...... 89

3.4 Use of New Models in Detectability Studies ...... 97

4. Conclusion ...... 105

References ...... 107

Attachments ...... 131

FCUP xiv Methodological and Ecological Insights on Wind Farm Environmental Impact Assessment

List of Tables

Table 1 - Visibility classes according to respective ground cover and vegetation characteristics ...... 50

Table 2 - Results of the Stepwise process, for the final model. The Autumn season was considered as the reference season for the variable. The statistical significant p values (<0.05) are indicated with a *...... 59

Table 3 - Correction factor for carcass removal, required to estimate mortality using the estimator Huso 2010 (Mean Persistence Time, in days - t),̅ for birds (A) and bats (B), for Vila Lobos wind farm. It is also presented the standard error (se) and the upper (UCI) and lower (LCI) 95% confidence intervals...... 60

Table 4 - Results of the Stepwise process, for the final model. The Autumn season was considered as the reference season for the variable. The statistical significant p values (<0.05) are indicated with a *...... 62

Table 5 - Correction factors for carcass removal, required to estimate mortality using the estimator Huso 2010 (Mean Persistence Time, in days - t)̅ , for birds (A) and bats (B) for Lousã II wind farm...... 65

Table 6 - Correction factor for carcass removal, for weekly prospections, required to estimate mortality using the estimators Huso 2010 (t)̅ , for bats, for Vila Lobos and Três Marcos wind farm...... 73

Table 7 - Scavenger species captured through camera-trapping, in all seasons, in Vila Lobos wind farm...... 74

Table 8 - Scavenger species captured through camera-trapping, in all the four seasons, in Três Marcos wind farm...... 74

Table 9 - Total number of captures of each species for all seasons, in Vila Lobos and Três Marcos wind farms...... 76

Table 10 - Scavenger species captured through camera-trapping, in dry and wet season (July and October, respectively), in Picos - Vale do Chão wind farm...... 78

Table 11 - Scavenger species captured through camera-trapping in all seasons, in Lousã II wind farm...... 79 FCUP xv Methodological and Ecological Insights on Wind Farm Environmental Impact Assessment

Table 12 - Total number of captures of each species for all seasons, in Lousã II and Picos - Vale do Chão (PVC) wind farms...... 79

Table 13 - Total frequencies of the species observed at least one-time scavenging, in Vila Lobos (VL) and Lousã II (LO) wind farms, in the four seasons...... 83

Table 14 - Scavenger species captured through camera-trapping in winter seasons, in Alto dos Forninhos wind farm...... 85

Table 15 - Total number of captures of each species for winter seasons, in Alto dos Forninhos wind farms...... 85

Table 16 - Total number of captures of each species in winter seasons, in Alto dos Forninhos (AF), Vila Lobos (VL) and Lousã II (LO) wind farms...... 86

Table 17 - Results of the Stepwise process, for the final model, considering the variables season and type of carcass. The Autumn was considered as the reference season for the variable season, as it was <75 range for the distance variable. The statistical significant p values (<0.05) are indicated with a *...... 89

Table 18 - Results of the Stepwise process, for the final model, considering the variables season and type of carcass. The Autumn was considered as the reference season for the variable season. The statistical significant p values (<0.05) are indicated with a *. 94

Table 19 - Total number of models’ observations for each visibility class (I, II, III and IV) and for every models’ size (Small, Medium and Large). Table with the sums for each model type: the normally used ones (a) and the new ones (b) ...... 102

FCUP xvi Methodological and Ecological Insights on Wind Farm Environmental Impact Assessment

List of Figures

Fig.1 - Continental Portugal map showing the location of the five wind farms studied in this work: two in the north (Vila Lobos and Três Marcos wind farms), two in the centre (Lousã II and Picos - Vale do Chão wind farms) and one further south (Alto dos Forninhos wind farm). Each dot represents the middle point of the total extent of the wind farm. The quadrangular dots indicate the wind farms where new data was collected. The local of the used databases, provided by Bioinsight, are represented by circular dots...... 39

Fig.2 - Satellite images from Vila Lobos and Lousã II windfarms, on the right and left side, respectively...... 40

Fig.3 - Photo examples of the different composition (a, b, c and d) in Vila Lobos wind farm...... 41

Fig.4 - Photo examples of the different habitat composition (a, b, c and d) in Lousã II wind farm...... 42

Fig.5 - Habitat examples that can be found in Três Marcos wind farm. : Bioinsight company...... 43

Fig.6 - Different patches composition in Picos - Vale do Chão wind farm. Resource: Bioinsight company...... 44

Fig.7 - Photo examples of the different habitat composition (a, b, c and d) in Alto dos Forninhos wind farm. Resource: Bioinsight company...... 45

Fig.8 - Placement method performed on both Lousã II and Vila Lobos wind farm. Camera placed on Lousã mountain tree (a); on a wood stick on Vila Lobos (b) and camouflaged in Vila Lobos wind farm rock structure (c)...... 47

Fig.9 - Carcasses types (Mus musculus and Coturnix coturnix on the left and right photo, respectively) on placement site examples...... 48

Fig.10 - Example of the methodology for camera-traps random distribution. It is represented the 100 x 100 [m] grid; the 140 m buffer limitation and the possible camera placement sites...... 48

Fig.11 - Photos of Vila Lobos wind farm visibility classes. A total of 5 classes are presented (one of them inaccessible for survey) according to their vegetation FCUP xvii Methodological and Ecological Insights on Wind Farm Environmental Impact Assessment

characteristics: (a) Class 1 – easy; (b) Class 2 – moderate; (c) Class 3 – difficult; (d) Class 4 – very difficult and (e) Class 5 – inaccessible...... 51

Fig.12 - Models’ examples usually used in detectability trials to represent large, medium and small individuals, from left to right, respectively...... 52

Fig.13 - New models’ examples tested in detectability trials. Models attempt to represent large bird wood pigeon (Columba palumbus) (b), fieldfare (Turdus pilaris) as a medium size bird (c) and bats (d), from top to bottom on right side. Size proportion comparation is presented on (a)...... 52

Fig.14 - Probability of a carcass remaining in place t or more days in terms of the type of carcass, represented by the Kaplan-Meier curve and Log-normal model (final model), for birds (A) and bats (B), in Vila Lobos wind farm. In B, Level 1 – Autumn; Level 2 – Spring and Level 3 – Summer...... 61

Fig.15 - Probability of a carcass remaining in place t or more days in terms of type of carcass, represented by the Kaplan-Meier curve and Log-normal model (final model), for birds (A) and bats (B), in Lousã II wind farm. In B, Level 1 – Autumn; Level 2 – Spring and Level 3 – Summer...... 64

Fig.16 - Total frequencies of the five scavenger species most common, which were captured with camera-trap method, during the four seasons, in Lousã II wind farm. ... 66

Fig.17 - Photo examples of the scavenger species found in Vila Lobos wind farm: (a) the carrion crow; (b) the common kestrel, (c) Felis sp. and (d) the Egyptian mongoose. ... 68

Fig.18 - Photo examples of the common species between Vila Lobos (VL) and Lousã II (LO) wind farms. Photos on the left and right side are from VL and LO wind farms, respectively. Species represented are the domestic dog (a and b), the wild boar (c and d) and red fox (e and f)...... 69

Fig.19 - Photo examples of the scavenger species found in Lousã II wind farm: (a) the common buzzard; (b) the Eurasian jay, (c) the common genet and (d) the stone marten...... 70

Fig.20 - Red fox feeding in front of the camera placement originating feather spots (a) and carrying away the carcass (b) from its original placement site...... 71 FCUP xviii Methodological and Ecological Insights on Wind Farm Environmental Impact Assessment

Fig.21 - Examples of staring behaviour by scavengers when they perceived camera trap presence. It is shown a red fox (Vulpes Vulpes) on the left photo and a stone marten (Martes foina) on the right one, captured in Lousã II wind farm...... 72

Fig.22 - Example of territory marking by red fox (Vulpes Vulpes) and stone marten (Martes foina), on the left and right photo, respectively, captured during carcass removal trials...... 72

Fig.23 - Mean persistence time, in days (t)̅ (± standard error), registered in the four seasons, in Picos - Vale do Chão (PVC) wind farm and in Lousã II (LO) one. The number of days is indicated above the standard error bars...... 77

Fig.24 - Mean persistence time, in days (t)̅ (± standard error), according to Huso 2010 estimator, for birds, for Vila Lobos (VL) and Lousã II (LO) wind farms. The number of days is indicated above the standard error bars...... 81

Fig.25 - Mean persistence time, in days (t)̅ (± standard error), according to Huso 2010 estimator, for mice, for Vila Lobos (VL) and Lousã II (LO) wind farms. The number of days is indicated above the standard error bars...... 82

Fig.26 - Mean persistence time, in days(t),̅ registered in the four seasons, in three wind farms: Alto dos Forninhos (AF); Vila Lobos (VL) and Lousã II (LO)...... 84

Fig.27 - Mean persistence time, in days (t)̅ (Huso, 2010) (± standard error) calculated for Vila Lobos wind farm, for each distance intervals: < 75 m (A); 75 – 115 m (B) and > 115 m (C), for the four seasons. The number of days is indicated above the standard error bars...... 90

Fig.28 - Probability of a carcass remaining in place t or more days depending on its distance to the reference turbine, for each time of year, represented by the Kaplan-Meier Curve and Log-normal model (final model)...... 91

Fig.29 - Mean persistence time, in days (t)̅ (Huso, 2010) (± standard error) calculated for Vila Lobos wind farm, for each season, according to distance intervals: < 75 m (A); 75 – 115 m (B) and > 115 m (C). The number of days is indicated above the standard error bars...... 92

Fig.30 - Photo examples of cow and sheep herds using Vila Lobos wind farm territory...... 93 FCUP xix Methodological and Ecological Insights on Wind Farm Environmental Impact Assessment

Fig.31 - Detection probability of the usual and new models, according to each visibility class (1 - easy, 2 - moderate, 3 - difficult and 4 - very difficult)...... 97

Fig.32 - Detection probability of the usual and new models, according to each models’ size (large, medium and small)...... 98

Fig.33 - Detection probability when combining both factors: visibility class and models’ size. The first number indicates the visibility class and the following letter indicates the model’s size (L – Large; M – Medium; S – Small)...... 99

Fig.34 - Detectability according to the defined four visibility classes, expressed through the mean number of models observed during search trials. Data collected with the usual and new detectability models are presented on the right and left graphic, respectively...... 100

Fig.35 - Detectability according to the size (small, medium or large) of the models used during trials, expressed through the mean number of models observed during search trials. Data collected with the usual and new models are presented on the right and left graphic, respectively...... 101

(All photos in this work are of own or Bioinsight authorship; All satellite images have been taken from Google Earth (https://earth.google.com)) FCUP xx Methodological and Ecological Insights on Wind Farm Environmental Impact Assessment

Abbreviations

CO2 – Carbon Dioxide

GHGs – Gases and Greenhouse Gases

B.C – Before Christ

USA – United States of America

PNAER – Plano Nacional de Ação para as Energias Renováveis

REN – Redes Energéticas Nacionais

EU – European Union

EUROBATS – Agreement on the Conservation of Populations of European Bats

EIA – Environmental Impact Assessment

APA – Agência Portuguesa do Ambiente

ICNF – Instituto da Conservação da Natureza e das Florestas

USGS – United States Geological Survey

IDI – Investigação, Desenvolvimento e Inovação

EEA – European Environmental Agency

e2p – Energias Endógenas de Portugal

IPMA – Instituto Português do Mar e da Atmosfera

ICNB – Instituto da Conservação da Natureza e das Florestas

GPS – Global Positioning System

WFE – Wildlife Fatality Estimator

AIC – Akaike Information Criterion

ANOVA – Analysis of Variance

CIBIO – Centro de Investigação em Biodiversidade e Recursos Genéticos

HSD – Honestly Significant Difference

FCUP xxi Methodological and Ecological Insights on Wind Farm Environmental Impact Assessment

FCUP 1 Methodological and Ecological Insights on Wind Farm Environmental Impact Assessment

1. Introduction

1.1 The Energetic Challenge

Energy requirements have been increasing day by day as the human population grows and technology advances. According to the International Energy Outlook 2017 modelled projections, the energy consumption will grow by 28% between 2015 and 2040 (U.S. EIA 2017). Therefore, the need for new sources of energy is a very important subject nowadays, especially renewable ones. Environmental concerns have been raising as the consequences of fossil fuels to the environment start to be evident and, secondly since fossil fuel duration is limited and unable to respond to the increasing energy demand of the current century (Kaplan 2015). Concerning the first motive, as it is known, the main sources of energy imply petroleum, coal and natural gas combustion and this leads to a

continuous increase of carbon dioxide (퐶푂2) emissions as well as other toxic gases and greenhouse gasses (GHGs), which contribute to global warming (Kikuchi 2008; Leung and Yang 2012; Dai et al. 2015; Kumar et al. 2016). In order to overcome this major environmental problem, several decision makers such as energy researchers, industrial professionals and government officials have been seeking a long term environmentally friendly alternative energy source, such as geothermal, hydro, solar, marine, wind energy and bioenergy (Ellabban et al. 2014; Dai et al. 2015), known as the “green” and “clean” energy, attempting to reduce reliance on fossil fuels (Dai et al. 2015). Among all the renewable energy sources, wind energy is one of the most promising, mainly because it is seen as an infinite resource, which does not require fossil fuel and almost no water, and generates near-zero emission that could cause acid rain or GHGs production (Kikuchi 2008; Ledec et al. 2011; Wagner and Mathur 2013; Mascarenhas et al. 2018). Basically, the main advantage is the absence of air pollution, since it does not use fossil fuel as energy resource, and does not produce solid waste or discharges to soil and/or water (Ledec et al. 2011; American Wind Wildlife Institute

(AWWI) 2014; Ellabban et al. 2014). In fact, wind energy avoided 166m tonnes of CO2 emissions in Europe in 2016 (Wind Europe 2018). The continuing growth of this energy sector is also due to other several positive entails. Additionally to environmental motives, the technology available and the economically feasible energy generation are also strong points in favour, despite being highly variable in terms of both temporal and geographical aspects (Akdağ and Güler 2018). Moreover, once it is generally installed on rural areas, it creates jobs for both industry and rural economies and can revitalize the rural communities’ economies (Wagner and Mathur 2013; Ellabban et al. 2014; Kaplan 2015). Furthermore, it does not necessary imply disablement of the surrounded area, since FCUP 2 Methodological and Ecological Insights on Wind Farm Environmental Impact Assessment

agriculture and animal husbandry can be practised there (Ellabban et al. 2014; Kaplan 2015). Due to these advantages, wind energy had an accelerated growth during the past decade (Dai et al. 2015) and became the most explored of all renewable energy sources for electricity generation today (Tabassum et al. 2014). According to the latest Global Wind Report, more than 54 GW of clean renewable wind power was installed across the global market in the last year, summarizing a total global wind power installation of 486.8 GW (Global Wind Energy Council 2016). Wind energy resource has been developing since Egyptians (5000 B.C.), where it was used to propel boats along the Nile river (Kumar et al. 2016; Wind Energy Foundation 2016). In the late 1970s begun its notable use, as governments promoted and invested in wind energy, in response to oil crises in 1973 and 1979 (Bourillon 1999). It was in California where it appeared the first significant commercial market in the 1980s (Bourillon 1999) and since then the technological and logistic developments have been facilitating the cost-effective installation and maintenance of wind power. In Europe a remarkable development of modern wind turbine technology occurred, especially in Denmark, Spain, and Germany, where the necessary conditions were reunited: an established technology base, local meteorological conditions, green politics and government investment (Tavner 2008). Countries mentalities are a particular challenge. In the old days, it was a common thought that fighting against climate change was a responsibility only of the advanced economies, but since Paris Agreement, climate policies are a transcontinental concern (Obama 2017). Nowadays, countries are moving forward in terms of clean energy in a constant clean-energy race. Moreover, in the Marrakesh meeting in the 2016 year fall, more than 110 countries, including Portugal, agreed that the 2017 year was an extraordinary momentum on climatic change worldwide and so, it is necessary to work together to reduce greenhouse gas emissions and, in this way, benefit the 2030 Agenda for Sustainable Development Goals (United Nations 2016).

1.1.1 Wind Energy in Portugal

Worldwide, Europe is pioneer in the use of renewable energy and it is included in the three major markets for global wind power generation, together with USA and China (Kaplan 2015). In Portugal, together with Uruguay and Ireland, wind power penetration levels continue to increase (20% in the 2016 year), only behind Denmark with 40% (Global Wind Energy Council 2016). Currently, throughout all Portuguese territory there are 250 active wind farms, mainly located on the north and interior centre, as well as near the south-west coast and in the south-west tip of the country (e2p, 2017), which FCUP 3 Methodological and Ecological Insights on Wind Farm Environmental Impact Assessment

may indicate that Portugal has almost reached the limit of its wind energy capacity, since the majority of Portuguese mountainous ridges are occupied by on-shore wind turbines (Mascarenhas et al. 2018). Over the last decade, by increasing the installed capacity, Portugal is closer and closer to achieve the target set by the National Action Plan for Renewable Energies (PNAER 2020) of 5300 MW, since in 2016 it already bypassed 5 GW (5079 MW) (Whiteman et al. 2016). In fact, in 2015, Portugal published the Green Growth Commitment, where it is stated the target of 31% of renewables contribution to the gross final energy consumption in 2020 and 40% in 2030. In fact, one of the main Portuguese goals is to become independent in terms of external energy supply and, at the present moment, it has already reached the 52% renewable production in 2015 (Mascarenhas et al. 2018). In the last year (2017), in Portugal, 40% of the total energy consumption, plus the exported amount, was supplied by renewable sources, and wind energy was responsible for the major amount (23%) (REN 2018).

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1.2 Wind Energy Impacts

Although wind power development brings many environmental and social benefits, it is fundamental that decision makers attempt to minimize its various inherent disadvantages, so that the maximum sustainability is assured in an environmental and social manner. The impacts of wind farms have long been investigated and monitored, both on onshore and offshore. Although these last two infrastructures belong to the same industry, the effects and, consequently, the impacts related to each one of them, have much in common, they have some dissimilarities due to the considerable existent differences between the where they are implemented. For example, the construction phase underwater (offshore) implies the removal of sediments which results in a direct loss of and an increase in local water turbidity due to suspended solids (Gill 2005). Moreover, this gives the opportunity for undersoil contaminants dispersal within the sediments (Gill 2005). The similar effect prevails on the interference with the landscape aesthetics as with mankind comfort (Kaldellis et al. 2016). However, the wildlife groups affected vary between them. In offshore wind farm cases, migratory birds and marine mammals are the major concern. The habitat loss and/or change, the effect due to, e.g., noise, reflexes, and shadows, the barrier effect and collision, are the effects that migratory birds may suffer from offshore wind farms (Huppop et al. 2006; Furness et al. 2013). Although collision is a concerning topic, the overall fatality rates seem to be lower than expected, once migratory birds may demonstrate an avoidance behaviour (Lindeboom et al. 2011; Plonczkier and Simms 2012). Regarding marine mammals, the displacement and/or avoidance or behavioural disturbance due to underwater noise during construction and operation phases are examples of negative effects (Tougaard et al. 2012; Dähne et al. 2013; Kastelein et al. 2013). Pile-driving, for example, has been subject of several studies where it was observed to cause significant avoidance behaviour (Brandt et al. 2011; Dähne et al. 2013). In Bailey et al. (2010), it was concluded that for bottlenose dolphins, auditory injury would only have occurred within 100 m of the pile-driving and behavioural disturbance could have occurred up to 50 km away. In fact, the underwater noise emission has been the focus of most studies on marine mammals (Schuster et al. 2015) and there is still no consensus regarding this topic. Not only the noise but also the emitted electromagnetic field will interfere with aquatic fauna since some species use the Earth’s magnetic field to navigate (Gill 2005; Kaldellis et al. 2016). However, some positive effects can be also felt by marine mammals. The resulting habitat change may lead to an increase in prey FCUP 5 Methodological and Ecological Insights on Wind Farm Environmental Impact Assessment

availability due to the exclusion of fishery or even to an artificial reef-effect due to the addition of hard substrates (Evans 2008; Scheidat et al. 2011; Kaldellis et al. 2016). Another possible benefit is the sheltering effect, since the buffer zone surrounding the wind turbines may become a marine reserve, as the exclusion of boats within this zone would reduce disturbance from shipping (Bailey et al. 2014). Basically, whereas the construction phase brings a negative impact, the pressures felt during the operational one, depending on local environmental conditions, may impose both negative and positive effects (Bergström et al. 2014). Once this study will focus on the onshore wind farms, its impacts are more explored below. The landscape and, consequently, habitat interference, are the first negative impacts that come to mind due to its direct relation with the wind farm on land, followed by a clear visual impact. However, disturbances to human populations nearby and to telecommunications, climatic influence and, finally, wildlife impacts (Leung and Yang 2012; Ellabban et al. 2014; Dai et al. 2015) can also be mentioned. Concerning the first topic, although each machine does not require an extensive area, the length of the mountain ridges occupied by the entire wind farm may lead to a negative visual impact in the nearest location. Besides, the deforestation and soil erosion (habitat interference) caused during the wind farm construction phase, due to heavy machinery, leads to a disturbed eco-balance (Dai et al. 2015). Visual impact is a controversial topic, since its perception may vary according to different communities, situations or contexts. This means that there are different opinions about it: it can be associated to development in an efficient and safe manner or it can be assumed simply as a visually conspicuous structure (Leung and Yang 2012; Tabassum et al. 2014; Kumar et al. 2016). Among the perception influencing factors, turbine size, number, colour and the specific landscape context in which they are constructed can be mentioned (Devine-Wright 2005). As an example, the choice of turbines placement: if they are in an open area instead of a closed mountain range, the visual impact is much smaller (Tabassum et al. 2014). These landscape disturbances may also affect tourism in several ways and so, it should be another factor to take in consideration. Several studies observed that wind farms act as tourist attractions (e.g. (Delicado et al. 2015)) and it is already called the “energy tourism” (Frantál and Urbánková 2017). In a Portuguese case study, residents’ and visitors’ perceptions and attitudes were analysed concerning wind energy and, in both cases, opinions diverged (Silva and Delicado 2017). Most residents showed opposition to wind energy facility due to the proximity to the medieval architecture, which is one of the tourists’ attractions (Silva and Delicado 2017). FCUP 6 Methodological and Ecological Insights on Wind Farm Environmental Impact Assessment

This means that wind turbines distribution plays a significant role in explaining acceptance and opposition. Most visitors were also appalled by the proximity of wind turbines to medieval buildings, but the majority declared themselves in favour of wind energy generation (Silva and Delicado 2017). Besides all the setbacks, in general, people tend to support the concept and implementation of wind energy as it was demonstrated in Tampakis et al. (2013), where citizens were positively disposed towards the installation of more wind parks in Andros island, especially because of their familiarisation with the already installed wind farm, which has made them more willing to accept wind power. However, many of the communities which accept wind energy are against their construction near their households (Kaldellis et al. 2013), because of their concerns about the unfavourable effects (Dai et al. 2015), referred below. In terms of resident human population impact, the mechanical or aerodynamic noise produced, the visual impact and the shadow by rotating blades plays an apparent role. Concerning turbine noise, it may cause some sleep disturbances (Punch et al. 2010; Paula et al. 2018). However, studies have not yet proved that these noises cause direct health hazards (Dai et al. 2015). Indeed, this is a variable to take into consideration when deciding the distance between wind farms and households and so it usually exists government or medical distance recommendations for each country or region (Dai et al. 2015). The flicker from turbines, another negative aspect mentioned, interrupts or reflects sunlight at different frequencies and when they are greater than 3 Hz it poses a potential risk of inducing photosensitive seizures on residents over considerable distances from the turbine (Harding et al. 2008). This flicker frequency is also the principal cause of annoyance and so it should be kept at no more than three blade's passes per second or 60 rpm for a three-bladed turbine (Tabassum et al. 2014). As mentioned before, visual impact is another factor that, generally, makes people have a negative opinion of the wind energy industry (Wolsink 2007). However, in some cases, as Kaldellis et al. (2013), respondents expressed a positive trend for the visual impact of the wind farms (65%) by either stating that they were not affected visually by its presence (49%) or that wind turbines make their territory attractive (16%). Such as visual impact, noise or shadow by rotating blades, are subject to human tolerance and level of annoyance (Tabassum et al. 2014). A proof of this is that stress symptoms such as headaches appeared in residents who were annoyed by wind turbines presence (Pedersen 2011). According to Langer et al. (2016), the influencing factors respecting wind energy acceptance can be grouped into four categories: perceived side effects, process-related variables, personal characteristics, and technical FCUP 7 Methodological and Ecological Insights on Wind Farm Environmental Impact Assessment

and geographical issues. Two years later, Langer et al. (2018), revealed that perceived side effects and process-related variables play an essential role on community thought and that the fear of infrasound has the most significant negative influence on wind energy acceptance. However, an absence of annoyance (92%) was already registered concerning noise produced during operation, due to different reasons, since residents claim they did not feel any disturbances or that the noise is covered by the surroundings (Kaldellis et al. 2013). In fact, this annoyance appears to be more strongly related to visual cues and attitudes than to noise itself (Knopper and Ollson 2011). Regarding telecommunications, wind turbines can cause electromagnetic interference by distorting the transmissions of existing radio or television stations, or even by generating their own electromagnetic radiation, when the signal passes through the rotating blades (Tabassum et al. 2014). Moreover, humans are exposed to these additional electromagnetic radiations, which is associated with cancer risk, among other health implications, as evidenced by mobile devices use effects (Kim et al. 2016). This is, however, a conflicting association, since it was already registered that magnetic field levels near wind turbines were lower than those produced by household electrical devices and were below any existing regulatory guidelines (McCallum et al. 2014). This has been achieved through the substitution of metallic blades for synthetic ones (Tabassum et al. 2014; Dai et al. 2015). The impact referred as climatic change has already been observed at a local and regional scale (Leung and Yang 2012). Until now, some works have alerted to this problem. According to the three-dimensional climate model, used in Wang and Prinn (2010), if 10% or more of the global energy demand in 2100 is ensured by wind turbines, a surface warming exceeding 1°C could be felt over land installations, on land areas with variable spatial extents, topographies or hydrological properties, so the climate effects could presumably be significant. This was noticed at San Gorgonio wind fields in the United States, where local temperatures raised at night and cooled in daytime (Baidya Roy and Traiteur 2010). However, this direct correlation needs yet more attention. Finally, the wind farms potential to impact wildlife populations is known and well- studied in varied groups, from plants and invertebrates to the flying and terrestrial vertebrates (Pereira et al. 2018). Among all the taxonomic groups, the effects on bats, breeding and resting birds, raptors, migratory birds offshore, and marine mammals can be outlined (Schuster et al. 2015). However, concerning onshore wind energy, bats and birds communities are the two major and particularly vulnerable taxonomic groups affected by the wind energy industry (Laranjeiro et al. 2018; Millon et al. 2018). FCUP 8 Methodological and Ecological Insights on Wind Farm Environmental Impact Assessment

The impacts resulting from wind farm operation can have two different natures: direct or indirect impacts (Millon et al. 2018). These effects, which some are already foreseen, are usually detected in the disturbed area and are then validated through a required environmental monitoring program during operational phase (APA 2018). In several cases, this validation may depend on a comparison with undisturbed areas (control sites) (Pearce-Higgins et al. 2009). When fatalities are caused by collision with wind turbine rotating blades it is considered a direct impact (Grodsky et al. 2011). On the other hand, there is a diverse range of indirect impacts related to habitat and behavioural effects (Pereira et al. 2018). Regarding indirect effects, and/or loss; disturbance and/or displacement, barrier effect, and decline of breeding success can be mentioned (Drewitt and Langston 2006; Gove et al. 2013; Laranjeiro et al. 2018). Habitat fragmentation and/or loss is inevitable during and after the wind farm construction since land transformation is necessary (Drewitt and Langston 2006) for the setting up of wind turbines and associated infrastructure, such as, for example, the power lines. This habitat transformation or loss, which is one of the biggest threats to fauna does not directly imply negative impacts on the existing community (Kuvlesky et al. 2007; Lovich and Ennen 2013; Atienza et al. 2014). It all depends, for example, on which species rely on that area: the existence of threatened species, in danger of extinction or sensitive to habitat modification, increases the importance of those areas (Lovich and Ennen 2013; Atienza et al. 2014). Moreover, for most land mammals, which use larger areas, this loss is probably of marginal importance, particularly in comparison with the large-scale landscape conversion caused by, for example, modern agriculture and forestry (Helldin et al. 2012). The establishment of new access roads, when not previously present, also leads to the fragmentation, loss or degradation of natural habitats, since land clearing, wood cutting, and informal mining may be required (Ledec et al. 2011; Lovich and Ennen 2013). An indirect result of this improved access is the promotion of human activities such as recreation or hunting (Ledec et al. 2011; Łopucki and Mróz 2016), which can increase the pressure on wildlife. Moreover, the wind farm access roads, induce direct mortality (Glista et al. 2009). Once it is a land disturbance, it could be expected a high impact on terrestrial biodiversity. However, it especially affects birds and bats, since, for example, the wind turbine construction itself, implies the establishment of an air obstacle, that may interfere with bats migration (Cryan 2011). Another example is described in Millon et al. (2018) where Miniopterus sp. bat activity was 20-fold lower in wind turbine sites than at the control one, in a tropical hotspot. This means that the habitat loss may result in local FCUP 9 Methodological and Ecological Insights on Wind Farm Environmental Impact Assessment

displacement, another topic to explore further. The big concern with habitat fragmentation relies on the huge risk that it may represent to species, once it can create physical barriers which can significantly reflect in their dynamics (density, sex ratio, , and mortality rates) as well as demographic and genetic properties of animal populations, whenever the exchange of individuals is reduced but not completely inhibited (Seiler 2002). This can divide populations into smaller isolated subpopulations contributing to a higher inbreeding and, in extreme cases, it may lead to a risk of local extinctions (Keller and Waller 2002; Seiler 2002). This event is mainly felt and described for roads as it can be understood by the several examples given on Kuvlesky et al. (2007) despite, until today, at least no published study, has described significant influences on wind farm wildlife populations. The disturbance and/or displacement may also occur in both construction and operation phases, because flying vertebrates and terrestrial mammals have to change their living routine due to their sensitivity to machinery noise and movement, or just because of the turbines presence, with their inherent sound, visual flicker and shadow effect (Drewitt and Langston 2006; Pereira et al. 2018). In fact, displacement is considered a behavioural avoidance of a suitable habitat due to wind facility infrastructures (Strickland et al. 2011). In Pearce-higgins et al. (2009), there is evidence of localized reductions in breeding bird density on upland wind farms (more precisely, in at least 7 of the 12 species studied, significant avoidance was registered). The same displacement response was registered in a south-eastern Wisconsin wind farm, where raptor post-construction was reduced by 47% compared to pre-construction levels, and most individuals remained at a distance of at least 100 m from turbines (Garvin et al. 2011). However, studies concerning this subject had already pointed in different directions. While in some studies a decrease was detected concerning raptor abundance, such as in Hull and Muir (2013), where avoidance rates were 81% - 97%, despite differing between species and sites, in others, this trend was not verified (Dahl et al. 2013). Shaffer and Buhl (2016), for example, despite documenting displacement behaviour for 3 of 9 species, also registered the attraction of 2 species. In conclusion, the level of disturbance will depend on site- and species-specific factors and so it must be evaluated for each wind farm placement site (Drewitt and Langston 2006; Abdullah et al. 2017). In Minderman et al. (2017), for example, there is evidence for species- specific factors, since Pipistrellus pygmaeus was potentially more strongly affected by multiple small wind turbines than Pipistrellus pipistrellus. Another behavioural impact is the barrier effect, a hindrance to animals’ free passage caused by the continuous close turbine line and so, it may create gaps between FCUP 10 Methodological and Ecological Insights on Wind Farm Environmental Impact Assessment

feeding, roosting and nesting areas (Masden et al. 2010a; Gove et al. 2013; Atienza et al. 2014). It was already observed that some bird species avoid wind farm areas. For example, in Spain, raptors used the space around the wind farm with lower frequency than prior to its existence, representing a displacement of the home range of these species (Farfán et al. 2009). This last aspect can be interpreted as a positive or negative aspect, as discussed in Pereira et al. (2018). In other words, if flying vertebrates tend to avoid these areas, then it could decrease the collision risk, but in a finest scale, if there are more impassable areas, they must fly more often between feeding sites, thus overriding the benefit. Besides that, these additional movements imply increasing the distances travelled and so spending extra energy (Masden et al. 2010b; Atienza et al. 2014), which may have negative impacts on breeding performance. The increased flight costs may be detrimental and can impact reproductive output or and even cause higher mortality (Masden et al. 2010b, a; Gove et al. 2013). Some studies have already proved the impact of wind energy facilities in bird breeding performance, such as LeBeau et al. (2014), where through survival models it was detected a greater relative probability of nest and brood failure in habitats within close proximity to turbines. On the other hand, Kolar and Bechard (2016) found no effect of turbines on nest success for two Buteo species. These habitat interferences may affect species differently according to their habitat specificity. This means that specialist species are more vulnerable to the implicit effects of habitat fragmentation and/or loss than wide-ranging and generalist species (Keinath et al. 2017; Sverdrup-Thygeson et al. 2017). Most studies on wind farms impacts focus on birds (Marques et al. 2014) and bats communities (Kunz et al. 2007b), devaluing non-volant terrestrial wildlife (Lovich and Ennen 2013). In fact, few papers exist proving the significant effect of wind power development on such animals (Łopucki et al. 2018). One possible reason is that indirect impacts are more difficult to quantify and determine with certainty (Marques et al. 2018b). However, the impacts on these communities are diverse. Besides habitat fragmentation, terrestrial animals (such as mentioned for birds case) can experience, as explored by Lovich and Ennen (2013), noise effects (because the sound can disrupt animal vocal communication or impair the animals’ ability to hear approaching predators), vibration and flicker effects, electromagnetic field generation consequences, macro- and micro- climate effects, a higher fire risk and predator attraction. Rabin et al. (2006) is the reference example for noise effects, once they describe the antipredator behaviour modification of the Spermophilus beecheyi squirrel, by stifling their predator alert vocalizations. Other types of vocalisation may be masked or FCUP 11 Methodological and Ecological Insights on Wind Farm Environmental Impact Assessment

disturbed, like the ones to establish territory, to attract partners or keep the group together (Helldin et al. 2012; Chen and Koprowski 2015). The permanent exposure to the aerodynamic noise of wind turbines and episodes of mechanical noise was, possibly, the responsible for the corticosterone levels increment in common vole species (Łopucki et al. 2018). The electromagnetic field effects are not well validated because power lines are underground, covered with soil (which serves as isolation), and so the levels on top of the surface can be considered negligible (Oddy and Brien 2011). In fact, Glista et al. (2009), found no results indicating that electromagnetism in wind farms should have any measurable effect on terrestrial mammals. Concerning predators’ attraction, various species of potentially predatory animals may act as scavengers (Lovich and Ennen 2013). Ravens (Corvus corax) is a common species which is attracted to areas of human activity (as wind farms) and also a scavenger species (Kristan and Boarman 2003). The opposite effect is also felt, since, during the construction phase, large-mammal and ungulates may temporarily avoid wind farms (Łopucki and Mróz 2016) due to an increase in human activity within the area (Helldin et al. 2012). Due to the already mentioned factors such as the destruction of habitat, vibration and noise effects or an increase in human activity, the area around turbines or whole wind farms can presumably become a less suitable habitat patch than unaffected areas for non-volant wildlife (Łopucki et al. 2018). The existence of a significant effect on this wildlife group it is not a consensual topic since some studies show that wind farms had no significant effect (Walter et al. 2006; Łopucki and Mróz 2016) and others which demonstrate the opposite (Rabin et al. 2006; Lovich et al. 2011). Said that, this topic raises some concern, once wind farms may also significantly alter habitat use of terrestrials animals, possibly leading to avoidance behaviours of large or medium-size mammals during the construction or operational phase (Helldin et al. 2012; Łopucki and Mróz 2016; Łopucki et al. 2017). M. Santos et al. (2010) demonstrated that in a mountain of North-western Portugal, for which there was data on habitat structure (including distance to turbines) as well as on the distribution of amphibians, reptiles, birds, and mammals, vertebrate species suffered a decline in richness when the wind farm installation was simulated. In addition to that, other factors such as direct disturbance and structural habitat changes were also responsible for this decline. Moreover, Łopucki and Mróz (2016) verified that wind farm operations affected terrestrial animals, not only in wind farm vicinity but also within at least a 700 m buffer zone around each wind farm, but in both cases, animal reaction (avoidance or preference, or no effect) to wind turbines FCUP 12 Methodological and Ecological Insights on Wind Farm Environmental Impact Assessment

was species specific. This means that different species will manifest distinct behaviours and tolerances, reflecting on the habitat use around turbines. These behaviours may be affected by methods and environmental factors, such as predator presence (Łopucki et al. 2017). On the other hand, the absence of adverse effects by wind-power development was already observed for Rocky Mountain elk (Cervus elaphus), even though disturbance and loss of some grassland habitat were also apparent (Walter et al. 2006). The inexistence of a response to the disturbance could be explained by several reasons, and the simpler one is when the habitat modification is not strong enough to be felt by the local communities. Another possible explanation for this observation is the acclimation process. Acclimation to the wind energy infrastructure may be possible when human presence ceases (Łopucki and Mróz 2016; Costa et al. 2018) or after prolonged or repeated exposure to human disturbance (Helldin et al. 2012). The species’ responses may change according to the species itself, sex, age, individual, time of the year or type of disturbance (Stankowich 2008; Helldin et al. 2012; Costa et al. 2018). In Madsen and Boertmann (2008), for example, pink-footed geese (Anser brachyrhynchus) degree of habituation seems to be dependent on the height of the wind turbines. Animals’ adaptation to this human disturbance may include both behaviourally and physiologically changes (Helldin et al. 2012). Taking into consideration these distinct behaviours and tolerances of fauna species when confronted with human infrastructures, it would be interesting to understand how these reactions may influence death estimates, as done in current wind farm monitoring. All the direct and indirect impacts explored on this subchapter can have cumulative impacts, especially at the wildlife population level (Bailey et al. 2014). In fact, the collision caused mortality can have a high impact on bat populations, for example (Brinkmann and Bontadina 2006). Moreover, the cumulative effects on local populations can be accentuated by the presence of multiple wind farms in an area (Garvin et al. 2011), and so, cumulative impact assessments should always be considered (Bailey et al. 2014). As a final remark, it is important to highlight that wind energy as a renewable source does not only entail negative impacts, since, primarily, it allows us to reduce greenhouse gases emissions, and its implementation does not directly mean that it will induce negative effects on wildlife communities or in the local human population. Therefore, it is important to ensure the compliance with some guidelines such as, for example, avoiding areas with high bird/bat use; inhabited by sensitive species; with high FCUP 13 Methodological and Ecological Insights on Wind Farm Environmental Impact Assessment

biodiversity or even selecting or modifying infrastructure to minimize collision risk or indirect effects (Pearce-Higgins et al. 2012; Laranjeiro et al. 2018), to a more final positive balance.

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1.3 Bird and Bat Fatalities

From all the above described wind farm impacts, there is no doubt that direct fatalities are the most harmful ones for birds (Erickson et al. 2005) and bats communities (Brinkmann and Bontadina 2006; Arnett et al. 2016). Moreover, collision with the rotating blades or with the turbine tower can result in severe and traumatic injuries, which represent the major death cause of both communities (Rollins et al. 2012). Bird fatalities can be directly influenced by three main types of factors: species, wind farm location and wind farm itself (De Lucas et al. 2012; Marques et al. 2014), which are interconnected. Regarding species-specific factors, collision may increase according to morphological features, especially when taking into account size factors, such as weight, wing, tail and total bird length (Janss 2000). The combination of body weight and wing area give us a ratio called wing loading: the lower it is, the smaller is the colliding probability (Janss 2000; Herrera-Alsina et al. 2013). This is probably because birds with highest wing-loadings have a flight type more dependent on updrafts to gain altitude and to soar and also due to their lower manoeuvrability, typical of soaring birds (De Lucas et al. 2008). Besides morphologic features, sensorial perception has also an influence on collision rate, not just because they may flight in low visibility conditions but also as a consequence of some particularities of their vision field (Drewitt and Langston 2008), such as their frontal vision relatively small resolution (Martin 2011) or just because they tend to look downwards during hunting flights, which can bring undetectable objects directly ahead (Martin and Shaw 2010). Adding to those, phenology, avoidance behaviours, and bird abundance are also influencing species-specific factors. Phenology factors had already been reported, since, for example, raptor collision risk is higher for resident than for migrating species (Marques et al. 2014), such as verified by Johnston et al. (2014), where when visibility is favourable, golden eagles are able to adjust their flight altitudes so as to avoid closely approaching turbines. As mentioned in the last chapter (1.2) displacement and avoidance behaviours can be observed towards wind turbines. This behaviour was already reported for several species of small passerines (Abdullah et al. 2017) and raptors (Garvin et al. 2011; Hull and Muir 2013), for example. In this way, it may reduce collision risk, since birds can just alter their flight path (Villegas- Patraca et al. 2014) or define their breeding territory distanced of a wind turbine (Pearce- Higgins et al. 2009), as observed by Winder et al. (2014). Winder et al. (2014) reported the female prairie-chickens home range size expansion (nearly doubled) and that the females preferentially used space at greater distances from wind turbines during the breeding season after wind energy development. On the other hand, it was demonstrated that the construction and maintenance of wind farms did not seem to adversely affect FCUP 15 Methodological and Ecological Insights on Wind Farm Environmental Impact Assessment

nesting decisions of Circus pygargus in Tarifa (Spain) (Hernández-Pliego et al. 2015). For these reasons, not only the wind farm localization must be considered before its construction but also the existent bird species and for what reason they use that habitat (hunting, nesting, etc.). Just like displacement and avoidance factors, there is also no consensus about the relationship between bird abundance and collision fatality. While some believe that there is a direct relation (Langston and Pullan 2003; Carrete et al. 2012), others stated that bird collision probability is related to the differential wind farm area use, species’ flight behaviour (De Lucas et al. 2008) and turbine perception (Marques et al. 2014). As site-specific factors, the landscape features can be mentioned, as birds during hunting or migration may use some landforms more or less frequently, just as verified by Thelander et al. (2003) where Red-tailed hawks fatalities where more frequent on ridgelines than on hillsides and the opposite happened for golden eagles. As landscape features, wind farms crossing migrating paths are also a concerning factor for nocturnally migrating passerines, for example, which besides being the most abundant species at wind farms at certain times of the year, are also the most common fatalities (Strickland et al. 2011). The last two factors in site-specific category are food availability, since its unsuitability can function as a bird repellent (Hoover and Morrison 2005) and weather conditions, such as situations of strong wind or low altitude clouds (which force birds to adapt their flight altitude) that may lead to higher collision probabilities (Langston and Pullan 2003; Drewitt and Langston 2008). These site-specific factors are analysed during the project phase of the wind farm in an attempt to avoid the towers’ construction on areas with high collision probability. Finally, the last category, windfarm-specific factors, includes subjects more engineering related such as turbine features, blade visibility, wind farm configuration, and wind farm lights (Drewitt and Langston 2008; Marques et al. 2014). Bird vulnerability to collision, as mentioned before, differs between species but the most common bird group suffering avian fatalities is passerines, due to their high abundance, and, consequently, a high number of death records (Strickland et al. 2011). According to the wind farms analysed in Portugal, from 2005 to 2015, by Bioinsight company, two-thirds of all bird fatalities are mostly composed by ‘Larks’ and ‘Swallows, martins and swifts’ (Marques et al. 2018c). Small passerines are also the most vulnerable species in other countries. In Parisé and Walker (2017) they accounted for 62.5% of the 4975 observed fatalities; followed by upland game birds (8.2%) and diurnal raptors (7.8%). FCUP 16 Methodological and Ecological Insights on Wind Farm Environmental Impact Assessment

Larks are mostly resident species in Portugal (Svensson et al. 2009). The most affected species are the Eurasian skylark (Alauda arvensis) and the Woodlark (Lullula arborea) (Marques et al. 2018c). Although the Eurasian skylark is considered a ‘least concern’ species in Portugal, such as the Woodlark, they tend to select open mountain habitats that are heavily pressured by the increasing installation of wind farms. Once they perform vertical flights, they are more vulnerable to this anthropogenic disturbance and susceptible to cumulative consequences of persistent collision fatalities (Bastos et al. 2016). In fact, this species is mainly observed in highlands (over 800 m) from Spring to Autumn, in Northern and Central Portugal (Morinha et al. 2014). In Northern Portuguese wind farms, the skylark was the species with the highest collision mortality incidence (37.3%) among all bird carcasses (n = 59), followed by the common house martin (Delichon urbicum) as expected (Morinha et al. 2014). Concerning the second group, their high mortality values can be associated with their similar foraging and flight behaviour. This means that once insects are attracted to wind turbines (Long et al. 2011), then insectivorous birds will pass the collision risk zones. Although being breeding migrants, these species reach higher fatality rates than resident species (Marques et al. 2018b). Despite the fatality numbers are lower for the birds of prey, which are the other most affected bird group, collisions are a concerning topic, due to their higher longevity and lower breeding rates (Drewitt and Langston 2008; Wang and Wang 2015). This means that small increases in mortality of breeding adults may lead to population decline. According to Marques et al. (2018b) data, the common kestrel (Falco tinnunculus) has the highest weighted fatality (number of fatalities/turbine/search), followed by the common buzzard (Buteo buteo) and the griffon vulture (Gyps fulvus). These birds’ genus (Buteo and Falco) hunting technique makes them more vulnerable to collisions since it is characterized by high-speed approaches, which require high concentration levels (Travassos et al. 2005). In Morinha et al. (2014), for example, from 59 carcasses found in ten Northern Portuguese wind farms, 3.4% were Falco tinnunculus. In another revision work of monitoring reports between 2003 and 2010, in 62 Portuguese wind farms, the most affected bird species were, as expected, passerines: firstly, another swallow bird, the house martin (Delichon urbica); then the skylark and, lastly, the corn bunting (Emberiza calandra). From the total percentage, 15% (n = 200) of fatalities were birds of prey, with the common kestrel occupying the first position (Bernardino et al. 2012b). Besides passerines and birds of prey, big soaring birds like vultures are very susceptible to collision mainly due to their flight behaviour (Barrios and Rodríguez 2004; De Lucas et al. 2012; Rushworth and Krüger 2014). Soaring birds use either thermal or FCUP 17 Methodological and Ecological Insights on Wind Farm Environmental Impact Assessment

orographic lift for sustained flight, following the main wind currents in order to obtain a cost-efficient flight, and so, locations with greatest wind flow, which are also sought by wind energy development, are chosen by birds. Said that, wind farms represent a huge risk for this bird group. Although there are not many records of vultures’ deaths in Portugal, in Spain the scenery is different. Between the raptor species inhabiting Spanish wind farms, it is the griffon vulture (Gyps fulvus) that holds the highest mortality rates (Barrios and Rodríguez 2004; De Lucas et al. 2008, 2012). Corvids, a more frequent group in Portuguese wind farms territory, and especially the carrion crow (Corvus corone) (Aves de Portugal 2018), on the other hand, have a wander around behaviour, which safeguards them from high collision probabilities (Travassos et al. 2005). Although the registered deaths caused by wind farms, national and international laws were defined towards wild natural birds’ conservation. In Portugal, birds are under Birds Directive (2009/147/EC) protection as such as, in a more indirect way, by the Habitat Directive (92/43/EEC), the transposed European directive for national laws (Decree-Law no. 152B-2017) for EIA process and by the Berne Convection (Decree-Law no. 316/89, of September 22). Respecting bats, as already mentioned, the major fatality cause at wind energy facilities is direct collision especially with moving blades (Cryan and Barclay 2009). Although the reasons why bats are colliding have not yet been fully understood (Schuster et al. 2015), the collision event can be categorized into three classes: random collisions, coincidental collisions and collisions resulting from the attraction. The first category is due to chance events, which means that each individual has the same collision probability regardless of its sex, age, reproductive condition or time of year (Cryan and Barclay, 2009). On the other hand, the coincidental collisions imply certain behaviour aspects, which increase collision risk with turbines (Cryan and Barclay, 2009). A great example for explaining this last type of collisions is migration movements in Autumn, where most fatalities occur (Johnson et al. 2004; Arnett et al. 2008; Cryan and Barclay 2009; Baerwald and Barclay 2011). In North America, migratory bat species fatalities were much more frequent at wind farms than for the resident species, even in areas where the resident species are more common throughout the Summer (Arnett et al. 2008). Moreover, in Frick et al. (2017), by using elicitation and projections models, were able to understand that wind turbines may drastically reduce (90% in the next 50 years) migratory hoary bat population size. However, this relation between migration and bats death needs to be further explored, moreover because there is an emerging hypothesis which says that this vulnerability is higher and more related to bats who regularly move and feed in less cluttered and more open air-space, regardless of continent, habitat, FCUP 18 Methodological and Ecological Insights on Wind Farm Environmental Impact Assessment

migratory patterns, and roost preferences (Voigt and Kingston 2015). The search for mates could also aggravate coincidental collisions, since flight activity increases (Cryan 2008; Cryan and Barclay 2009). Differences in insect prey distribution can be added to this coincidental factor list (Kunz et al. 2007a), once feeding opportunities can be more likely to occur near turbines, depending on region, habitat or air layer (Horn et al. 2008; Cryan and Barclay 2009) due to insect attraction by turbine light sources (Arnett et al. 2005) or, possibly, the turbine tower colour (Long et al. 2011). Indeed, Grodsky et al. (2011) reported that 25% of bat fatalities had a full stomach. Lastly, bats may be drawn to wind turbines due to some attractor factor or the combination of some (Cryan and Barclay, 2009), either by curiosity or misperception, or just interpret turbines as a potential feeding, roosting, flocking, and mating area, for example, use the turbine nacelle for roost or base for nests (Arnett et al. 2005; Kunz et al. 2007b; Horn et al. 2008; Cryan and Barclay 2009; Hull and Cawthen 2013; Zimmerling and Francis 2016). The ultrasound emissions may also attract their curiosity, although it is still a hypothesis which needs more attention (Arnett et al. 2005; Horn et al. 2008), or even acoustically disorient them when near these structures during migration or feeding (Kunz et al. 2007b). Recently, another attraction factor has been identified towards wind farm towers. It appears that bats do not perceive differently the echoes produced by the smooth tower surfaces and by water (Hale et al. 2017). The study suggests that the water misperception may be contributing to bat fatalities at wind turbines as bats make frequent close passes and contacts with the smooth tower monopoles (Hale et al. 2017). These three theoretical collision categories are not a consensus subject since practical cases vary in their conclusions and there is little supporting evidence (Rydell et al. 2010). In addition to this three types of fatalities, even though it is evident that the majority of deaths are caused by traumatic injuries due to collision (Rollins et al. 2012), there exists the theory of barotrauma, and so, it is not a consensual topic between investigators. According to this theory, the abrupt air pressure changes generate a small vortex at the tip of each blade and this pressure differences cause internal tissue damage to air containing structures (Cryan and Barclay 2009; Grodsky et al. 2011; Rollins et al. 2012), such as the lungs, since small blood vessels are damaged, causing haemorrhaging into the thoracic cavity (Baerwald et al. 2008). Over the years, the importance magnitude given to this factor has been decreasing. Baerwald et al. (2008) said that barotrauma might cause up to 90% of fatalities in wind energy facilities, while Piorkowski and O’Connell (2010) recorded 82% dead bats with broken bones in opposition to 18% with no skeletal injuries, from a total of 17 individuals. Grodsky et al. FCUP 19 Methodological and Ecological Insights on Wind Farm Environmental Impact Assessment

(2011), presented results indicating that bone fractures from direct collision contribute to a higher percentage of bat deaths than expected, so it was not exclusively or predominantly barotrauma or direct collision but a combination of both. More recently, through an etiology study, it was concluded that the majority of wind farm bat deaths were caused by traumatic injury (73%, n = 262) (Rollins et al. 2012). It was also stated that, although not excluding barotrauma, it can occur with traumatic injury as a combined etiology and not exclusively (Rollins et al. 2012). Comparing both communities, bats are more threatened than birds (Zimmerling and Francis 2016), not just because the percentage of migratory birds’ total mortality is smaller (Zimmerling et al. 2013) or even because wind turbines are the largest source of anthropogenic bat mortality, but also, as already explored, due to their attraction to turbines (Cryan and Barclay 2009). At the population level, this raises a serious concern, since bats are long-lived with exceptionally low reproductive rates and relatively slow population growth, which gives them a limited capacity to recover from declines and maintain stable populations (Barclay and Lawrence 2003; Cryan 2011), possibly resulting in long-term population effects (Kunz et al. 2007b). Due to this high vulnerability, unlike most bird species, in the European Union, all species of bats are protected by law, being illegal to intentionally kill or harm bats (Voigt and Kingston 2015). In response to this, all members of the EU adopted European directives in their own legislation in order to contribute to higher levels of protection. In Portugal, bats are protected directly by the by EUROBATS agreement (Decree-Law no. 31/95, of August 18) developed in accordance with Bonn Convention (Decree no. 103/80, of October 11). However, this protection is reinforced, as it happens for birds, by the Habitat Directive (92/43/EEC), the transposed European directive for national laws (Decree-Law no. 152B-2017) for EIA process and by the Berne Convection (Decree-Law no. 316/89, of September 22). Despite all these national laws, from the 26 species known in Portugal (2 species only from the islands) (Palmeirim and Rodrigues 1992), 9 species are included on the “Livro Vermelho dos Vertebrados de Portugal” (Red Book of Portuguese Vertebrates (Cabral et al. 2005)) and wind farm establishment, as already explained here, does not facilitate the conservation of this fauna group. Taking into consideration bat fatalities, from 2003 to the end of 2016, 1032 carcasses were reported of a total of 8379 bat carcasses found in Europe (EUROBATS 2017). According to that, the most affected species in Portugal are the common pipistrelle (Pipistrellus pipistrellus), followed by the lesser noctule (Nyctalus leisleri). The first one is considered as Least Concern in Portugal (Cabral et al. 2005) and the lesser noctule species as a Data Deficient one, which does FCUP 20 Methodological and Ecological Insights on Wind Farm Environmental Impact Assessment

not raise high concerns, at least for the common pipistrelle. Their susceptibility may be due to a lack of avoidance behaviour (since they are species of high human tolerance) and due to their high flight altitudes, respectively (Marques et al. 2018b). Bernardino et al. 2012b proves this tendency concerning endangered species, once the common pipistrelle and lesser noctule were the most affected ones. As mentioned above, bats have a limited capacity to recover from declines and maintain stable populations. Since this not only happens with bats, the continuing expansion of wind farm developments may imply a major cumulative effect, where it seems to exist poor understanding and research (Masden et al. 2010a). Said that, some authors like Vasilakis et al. (2017) propose a methodological approach with the purpose of sustainable spatial planning of development projects, taking into account the cumulative effects on wildlife populations. In fact, they give the example that if all wind farms were to be authorized in south-eastern Europe, the cinereous vulture (Aegypius monachus) population would be at great risk of extinction soon.

FCUP 21 Methodological and Ecological Insights on Wind Farm Environmental Impact Assessment

1.4 Environmental Impact Assessment

The development and growth of the wind farm industry, as explored before, can presuppose several negative impacts and, still, it is an unstoppable process. Therefore, for a wind farm development project to be accepted, it is required, by the European directive (Directive nº 2014/52/UE after the Directive nº2011/92/UE reformulation), which was later transposed for national laws (Decree-Law no. 152B-2017), that an Environmental Impact Assessment Study (EIA) is conducted. The processes inherent to it have also to comply with legal obligations, national or European, ensuring the application of adequate methods. EIA should encompass every phase of wind farm developments (pre- construction, construction, and post-construction), in order to guarantee that impacts are minimized as much as possible and, if possible, avoided, namely those concerning biodiversity (Rodrigues et al. 2014). In fact, all projects likely to have significant effects on the environment must undergo through an evaluation process of this type and, wind energy it is not an exception (Decree-Law no. 152B/2017). Besides the national laws which obligate the responsible entities for the wind farm project to proceed in a certain way and time to EIA process, it may also exist some national guidelines and best practise recommendations for it. In the Portuguese case, after the knowledge combination of consultants and environmental authorities, several specific guidelines were developed and were made available by the Portuguese Environmental Agency (APA). The objectives that must be reached when implementing an EIA process are also available on APA (APA 2018) and must be applied independently of the project typology. In a summarized way, it is necessary to evaluate the possible significant environmental impacts, followed by the definition of measures aiming to avoid, minimize or compensate them. After that, it is required to validate the efficiency of the adopted measures and, finally, the EIA process has to ensure the public participation in the decision making (APA 2018). In order to comply with the third objective, biodiversity monitoring plans emerge, lasting some years during a wind farm operational cycle (APA 2018). Besides this legal framework, the Portuguese Environmental Agency presented a guidance and rules list, in 2010, as an easy utilization tool, aiming to increase the evaluation processes efficiency and quality and, on the other hand, promote the methodologies uniformity throughout all wind farm environmental impact projects, independently of who performs it. It includes, among many others, monitorization planning methodology for birds and bats, with particular highlight in this work (APA 2010a). FCUP 22 Methodological and Ecological Insights on Wind Farm Environmental Impact Assessment

Considering the three phases of project development, Portugal has also some guidelines for the time interval on which monitoring for bats, birds, and large carnivores should occur: one year for the pre-construction phase; three years for the post- construction one; continuously through all the construction phase (APA 2010b, c). In some specific cases, monitoring may be required for the entire lifecycle of the project. Monitoring plans should be reviewed annually and adjusted whenever needed (Santos et al. 2018). Among displacement, disturbance and barrier effect, direct mortality is a prominent negative impact that must be assessed during post-construction monitoring programmes, particularly concerning birds and bats (Drewitt and Langston 2006). Moreover, the observation of bird and bat fatalities is the most common indicator to determine the level of impact caused by wind energy in Portugal (Marques et al. 2018b). The problem is that the methodologies used for assessing direct fatalities have yet methodological particularities involved and there is no standard procedure mandatory by law (e.g. definition of a carcass search protocol for flying vertebrates killed, including survey techniques and frequency of the sampling design), though the Portuguese Environment Agency, as told before, has already produced guidelines for bird and bat monitoring programmes (APA 2010a; Santos et al. 2018). Nevertheless, the procedures basis is the same: researchers perform periodic carcass searches to detect and quantify the carcasses which fall near the turbine whenever there is a collision event. However, death estimates are dependent on several variables which introduce variability on its determination. These variability sources are described next. FCUP 23 Methodological and Ecological Insights on Wind Farm Environmental Impact Assessment

1.5 Monitoring Methodologies and Correctional Factors

The post-construction monitoring programmes, as mentioned in the last chapter, aims to know the actual changes in the environment, resulting from the implementation of the wind farm, comparing to the reference framework established in the called year 0 (APA 2010a). Among the possible perturbations, the mortality of bats and birds resulting from turbines collision is one of the assessed impacts (APA 2010a). The methodology to quantify the observed bats and birds’ mortality, as described in the chapter before, follows the same basic principle. However, the mortality estimation itself it is a more complex procedure since the observed mortality it is not equal to the real mortality and it can even change while technicians are not in the field. This happens due to some uncontrollable variables in the field. In order to reduce biases sources and improve fatality estimates, wind farm mortality determination includes the assessment of the two major sources of imperfect carcass detectability (Huso 2010), classified and known as the correctional factors: carcass removal and searcher efficiency. These two correctional factors, among others, are included in mortality estimators, which are further explored in chapter 1.6. The above correctional factors are related to the carcass detection itself since the removal by scavengers and the imperfect human detection are included on the several variability sources which can result in a misleading and biased estimate. Besides (1) removal by scavengers and (2) imperfect human detection; (3) site- and carcass-specific covariates, such as vegetation characteristics (height, type and density) or climate conditions and (4) fatalities or injured animals that may land or move outside search plots, are other relevant sources of variability (Morrison 2002; Arnett et al. 2008; Huso 2010; Korner-Nievergelt et al. 2011; Santos et al. 2018). In Grodsky et al. (2011), for example, bats were observed to exit the limited search area of mortality studies after a barotrauma or direct collision event with low severity. Therefore, mortality searches may not capture delayed lethal effects and, consequently, the impact prediction on bird and bat populations based on reported fatalities alone can result in an underestimated death rate (Slater 2002; Grodsky et al. 2011; Huso et al. 2016a; Santos et al. 2016).

1.5.1 Carcass Removal/Decay

Carcass removal happens due to scavenging or natural . The problem with both factors is that it may result on bird or bat carcasses removal before being found FCUP 24 Methodological and Ecological Insights on Wind Farm Environmental Impact Assessment

and counted by researchers and, thus, fatality rates will probably be underestimated (Kunz et al. 2007a). That being said, carcass removal trials, basically, evaluate the time interval that passes until there is no evidence of its presence on the placement site, in order to calculate the average persistence time (Huso 2010; Bernardino et al. 2013; Jenkins et al. 2015). Data collection is, normally, performed by one technician who does periodic visits to trial carcass on turbines placement site, until they are no longer present or the study period ends, with the purpose to check if they are still detectable or not, due to decomposition or disappearance (Huso 2010; Huso et al. 2016a; Santos et al. 2018). Carcass decomposition depends especially on climatic conditions, since, for example, weather conditions often mean that smaller carcasses disappear more quickly (or are more easily detectable by scavengers) (Atienza et al. 2014), especially during the rainy season, when carcass degradation is potentiated and its debris are washed away (Santos et al. 2011). Moreover, carcasses under humid conditions during the wet season, and higher temperatures during the dry season exhibit shorter persistence time due to higher degradation and desiccation, respectively (Santos et al. 2011). On the other hand, the rates at which scavenger (birds, mammals, and insects) remove or damage bird and bat carcasses is influenced by several factors: (1) geographic location, which implies that rates from one location should not be assumed correct for another (Kerns et al. 2005; Arnett et al. 2008); (2) habitat type; (3) season; (4) carcass type and (5) decomposition stage (Selva et al. 2005; Lynn and Auberle 2009; Villegas-Patraca et al. 2012; Paula et al. 2015; Costantini et al. 2016), or even by (6) scavengers’ community (Slater 2002; Arnett et al. 2005; Paula et al. 2015). According to APA (2010) and other international institutions (Lynn and Auberle 2009; Fish and Wildlife 2011), carcass removal trials should be performed in, at least, the seasons contemplated by fatality surveys (Spring, Summer, and Fall), to account for naturally occurring temporal and spatial variations, since there may be different removal/decay rates; and also to account for possible changes in predator activity throughout the year (ICNF 2017) Bat surveys are not requested in the winter season (ICNF 2017). This is because bats have evolved some survival strategies to overcome the harsh conditions and absence of prey during winter. This means that temperate zone bats tend to escape during winter conditions by hibernating in caves, mines or buildings that present a relatively constant microclimate (Popa-Lisseanu et al. 2009; Cryan 2011). There, bats minimize their metabolism and body temperatures to save energy during hibernation, consuming only stored fat reserves (Cryan 2011). These seasonal movements without FCUP 25 Methodological and Ecological Insights on Wind Farm Environmental Impact Assessment

a clear directional geographic trend, between Summer and Winter, are called regional migrations (Adams and Pedersen 2013). According to Adams and Pedersen (2013), roost temperature and associated metabolic advantages are key drivers for this type of migration since bats searched for the roosts most thermally suited for each phase of their life cycle. As referred above, scavenger’s community characteristics have a crucial role on carcass removal rate. Different species composition of bird and mammal scavengers, for example, may lead to differences in scavenging rates between different sites (Slater 2002; Arnett et al. 2005; Paula et al. 2015; Santos et al. 2018), as it was already observed in Arnett et al. (2005), where corvids (ravens and crows) presence resulted in different carcass removal rates. This would be interesting to test in Portuguese territory, since it is located in a geographically privileged position in a transition zone between two major climatic biogeographic regions (Atlantic and Mediterranean) and, consequently, the resulting higher habitat diversity harbours a greater diversity of fauna with diverse environmental preferences. Another cause for the differences in scavenging rates may be due to evidenced behavioural changes over time (Lynn and Auberle 2009). In the USA, for example, the percentage of carcasses remaining was significantly lower during Autumn and higher during Summer, which may denote the necessity of scavengers to build body fat for the winter (Smallwood 2007), evidencing the influence of seasons on scavengers behaviour. The execution of these trials requires real carcasses, which, normally, are grouped in three representative size classes, associated with small, medium and large birds, with the following measures: small: ≤ 15 cm; medium: between 15 and 25 cm and large: > 25 cm (Environmental Research and Associates 2000; Bispo 2012; Bernardino et al. 2013; Paula et al. 2018). These three size classes are representative of the Portuguese avifauna and, on the other hand, avoids unnecessary kills. In Portugal, as mentioned before, bats are a faunistic group protected by law, which means that there is the need for the utilization of surrogates during trials. This was already proved to be a viable method since Silva et al. (2008) did not detect any differences in removal taxa results between using real bats, mice, and parakeets. Mice carcasses are commonly used as bats surrogates. The carcasses used should be fresh, since it was already proved by Kerns et al. (2005) that the utilization of frozen animals, at least for bats, can result in lower removal rates and so it is a source of bias. This may happen due to the altered odor perception and tissue attractiveness (Smallwood 2007). However, if frozen carcasses are used, they should be thawed a few hours prior to the beginning of the trial (Fish and Wildlife 2011; ICNF 2017). FCUP 26 Methodological and Ecological Insights on Wind Farm Environmental Impact Assessment

The number of carcasses placed on the field must be restricted, so as not to create an artificial situation where predators are attracted and, consequently, misrepresent the normal speed at which the carcasses would be removed, biasing removal taxa (Travassos et al. 2005; APA 2010c; Smallwood et al. 2010). The only existing recommendations come from the Portuguese Institute of Nature and Forests conservation (“Instituto da Conservação da Natureza e das Florestas” – ICNF), which recommends the use of 20 carcasses for bat species, similarly to what is done in other countries (Gruver et al. 2010), and from APA, which only suggests that the number of corpses should be limited, so as not to create an artificial situation that biases removal rate. However, in other Portuguese studies, these numbers varied. Paula et al. (2015) used, in each wind farm, 60 fresh carcasses per trial (30 game birds and 30 mice). Bispo et al. (2013a), on the other hand, used a fluctuating number according to the size of the farm, which was reflected in a variation range of 60 carcasses (between 20 and 80). While in Bernardino et al., (2011) 10 carcasses of each size class (small, medium and large) were placed per season, comprising a total of 180 corpses, in Silva et al. (2008), 413 carcasses were distributed by three different types of visibility (high, medium and low). As specified before, for carcass removal trials it is necessary, at least, one technician who does periodic visits to the trial carcass, which supposes two main disadvantages. Firstly, the exact time of animal removal by a scavenger is not precise, since, due to the time elapsed between two consecutive carcass searches, removal could have happened immediately after the campaign beginning or closer to the next search (Paula et al. 2015). Secondly, the usual methodology implies a daily check, which, consequently, requires daily trips to the placement site and significant human effort. In fact, carcass survey and respective status register must be daily for a minimum of 15 days period (Bernardino et al. 2011). A less demanding solution has emerged, in the last decade, to optimize this conventional approach: camera-trapping, which is being used for carcass monitoring purpose (Smallwood et al. 2010; Paula et al. 2015; Abernethy et al. 2016; Devault et al. 2017; Ferreras et al. 2017). The major advantage of this method it is the possibility of recording the precise moment when a carcass is removed, which cannot be accomplished with the conventional approach. In this way, a minimal bias for posterior mortality estimation can be ensured (Bispo et al. 2013a; Huso et al. 2016b). Furthermore, it allows the study of the scavenger community in the area, through photo analysis, and individual identification (Paula et al. 2015, 2018). Moreover, this method minimizes the FCUP 27 Methodological and Ecological Insights on Wind Farm Environmental Impact Assessment

risk of human disturbance of the study area and possible consequent influence on animal behaviour, since the camera can be left for long periods on the field (Mccallum 2013). Confidence on the efficiency of this method has been increasing with the positive results from several studies, where 90% - 100% of the removal moments and responsible agents were captured by the camera (Smallwood et al. 2010; Paula et al. 2015). However, before opting for this methodology, it is necessary to take into consideration both positive and negative aspects. A comparative analysis has been made by Paula et al. (2015) and the first advantage found, as already mentioned, is the reduction of the number of placement visits, which reflects in a smaller amount of hours spent by technicians on the field. On the other hand, the time and effort necessary to analyse all the recorded photos or videos are high, as well as the equipment costs acquisition itself (Mccallum 2013). These expenses are only justified in cases where several trials are needed, for the initial investment to be recovered (Silveira et al. 2003; Paula et al. 2018). This means a substantial initial investment and simultaneously a risky one, due to the theft probability. As with all technological devices, some equipment flaws may occur or, as mentioned before, camera theft, leading to data loss (Paula et al. 2015; Caravaggi et al. 2017). Besides, the precise removal moment or agent may not be captured as a result of any of these situations: (1) the necrophage passage was too fast for sensor activation or (2) resulted in a blurred image making it impossible to identify the individual; (3) camera-recovery occurred during removal moment; (4) fulfil of memory cards; (5) depleted batteries and, finally, (6) camera theft. (Swann et al. 2004; Meek et al. 2014; Newey et al. 2015; Paula et al. 2015). The fulfil of memory cards can be due to constant triggering caused by wind blow that causes vegetation movements; sunlight reflection on the lens and/or high temperatures (Newey et al. 2015; Paula et al. 2015). One possible solution for this last situation and to optimise the performance of camera traps is the cleaning of the vegetation cover in front of them to avoid animal covering or reflection of the flash (Rovero et al. 2010; Meek et al. 2014).

1.5.2 Searcher efficiency

Searcher efficiency, or detectability, is the probability of a searcher observing an unscavenged carcass in the search area according to its ability, commitment, and experience (Ponce et al. 2010; Korner-Nievergelt et al. 2011; Grodsky et al. 2013; Costantini et al. 2016; Zimmerling and Francis 2016). However, it was already registered that searcher efficiency rate did not vary with initial searcher experience (Barrientos et al. 2018). Field data collection implies systematic trials in which an uninformed surveyor FCUP 28 Methodological and Ecological Insights on Wind Farm Environmental Impact Assessment

searches a determined area, normally around the wind turbines, under certain circumstances and study area characteristics (Huso 2010), following the same methodology as for corpses prospection. The aim is to determine the observer accuracy in finding previously placed carcasses that simulate different birds’ sizes and bat species. The carcasses should be randomly placed within the search area without observer knowledge of the placement sites, but their location must be recorded for retrieval if they are not discovered during the trial (Fish and Wildlife 2011). In the end, searcher- efficiency rate is calculated as the average proportion of carcasses found (Huso 2010), comparing the number of carcasses detected with the ones placed in the field (Reyes et al. 2016). The required number of carcasses for this type of studies is relevant (as explored below), since to obtain a statistical significance it is necessary to have a vast number of samples and replicates, which presuppose unnecessary animal sacrifice. In order to contradict this necessity, it is strictly recommended by national authorities to use objects or models similar to birds and bats (APA 2010a). However, this does not ensure a non- biased result. Smallwood (2013) verified that the model choice may vary: in some detection trials house mice (Mus musculus), rubber toys for bats, or paper machete models for birds are used; in others, conspicuous birds were used to represent more coloured birds’ species, typically killed by wind turbines. Moreover, in some situations, it is not known how long ago species were killed or species with quicker or slower removal times were used (Smallwood 2013). For searcher efficiency trials execution, normally, three size classes are used to simulate real corpses: small (≤ 15 cm and ≤ 50 g); medium (15 - 25 cm and 50 - 200 g);

large (≥ 25 cm and ≥ 200 g) (Bispo, 2012; Bernardino, 2011). Piorkowski and O’Connell (2010), for example, created models from black blotter paper (folded to simulate wings) on which tufts of grey rabbit pelt were glued. The pelts were cut with the same approximate torso and width length of the bat target species, according to the established intervals. The recommended model number varies according to authors, since, for example, Strickland et al. (2011) suggest at least 50 carcasses/models for each combination of factors (carcass/models size, type of vegetation, etc.) and the Portuguese national authorities favour the use of 20 carcasses/models (ICNF 2017). However, in Portugal, the number of carcasses or models used in monitoring programmes varies according to size classes considered and the homogeneity or heterogeneity of vegetation cover, which means that in homogeneous vegetation models are randomly distributed but in heterogeneous territory this variable is included in detectability calculations (Paula FCUP 29 Methodological and Ecological Insights on Wind Farm Environmental Impact Assessment

et al. 2018). In Bernardino et al. (2008), for example, 525 models were distributed for six different visibility classes. Regardless of using real carcasses or models, it is inevitable that some of the carcasses in the study zone will not be found and the reasons for this deficient carcass detection rate are dependent on several factors. As mentioned above, the inter-observer acuity skill variation (e.g. acuteness of eyesight or even fatigue factor) countermands standardization and, consequently, it acts as a source of bias when comparing studies (Atienza et al. 2014). Results from a Portuguese case study with a high level of effort in experimental design and trials execution confirmed that the observer detection probability is extremely reduced and yet it varied between 12.4% and 24.2%, which seemed to be overestimated (Bernardino et al. 2008). Besides personal experience, detectability efficiency is influenced by meteorological factors; vegetation cover characteristics and associated visibility determined by its density, height, and colour, which means that searcher efficiency varies with seasons (Arnett et al. 2008; Grodsky et al. 2013; Atienza et al. 2014; Rodrigues et al. 2014; Barrientos et al. 2017). Peters et al. (2014) verified that carcasses located on bare ground or grass are more likely to be detected than those on gravel. Stevens et al. (2011), for example, found that detectability was higher in little sagebrush (0.71) than in big sagebrush (0.36) habitats. For this reason, searcher efficiency trials should contemplate the different degrees of visibility of the existing habitats around the wind turbines (APA 2010a). To do so, before initiating the trials, it is necessary to map and delineate the searchable area of each turbine according to ground cover and vegetation density and size. As recommended by Strickland et al. (2011) no fewer than two and no more than four classes should be defined. The same author proposes four visibility classes that can be used in other projects. This characterization must contemplate the calculation of the representativeness of each type. The seasonal variations of the existing vegetation may lead to changes in visibility classes during seasons. Anticipating this variable, APA says that if there are not any significant variations between seasons, concerning vegetation structure and consequent growth (e.g. scrublands), then it is not required to repeat the trial (APA 2010a). Smallwood (2013) has indeed reported that searcher efficiency did not change over different seasons when controlled for the cover type. As mentioned above, this correction factor depends on habitat and personal attributes. However, the lighting conditions; topography; climatic conditions during searches (like fog presence); type of fatality (e.g., carcasses vs. feather spots); and differential human capacities (Arnett et al. 2005; Kunz et al. 2007a; Huso 2010; FCUP 30 Methodological and Ecological Insights on Wind Farm Environmental Impact Assessment

Barrientos et al. 2017) must be also considered. Besides these, the carcass characteristics may also determine its visibility such as colour, state of decomposition and size (Huso 2010; Bernardino et al. 2012a; Reyes et al. 2016), since it was already verified, for example, that larger carcasses are more likely to be detected than smaller ones (Teixeira et al. 2013; Peters et al. 2014; Santos et al. 2016) and also because they persist longer (more time to decompose) than small ones (Costantini et al. 2016). The observer detection variability caused by carcass characteristics, when using real carcasses, can be minimized through the utilization of trained dogs, whose searching efficiency is considerably higher when compared to humans (Barrientos et al. 2018). Arnett et al. (2005) noticed that trained dogs found 71% at Mountaineer (West Virginia) and 81% at Meyersdale (Pennsylvania) comparing to 42% and 14% for human searchers, respectively. This is notably useful when a high degree of search efficiency is required (e.g. surveys for rare species) (Mathews et al. 2013) and especially when vegetation coverage is high (Barrientos et al. 2018). Their utilisation can increase the recovery rate of carcasses, especially in heavy vegetation cover, although their capacity varies between them just like humans (Kunz et al. 2007a). Once these searcher efficiency trials rely on the same logic as carcass searches, it is necessary to understand how they are performed during the AIA process. Carcass searches, according to Environmental Portuguese Agency, for both bird and bat fatalities, should be performed around all wind turbines or, at least, in 70% of the total. The prospecting area should be adjusted according to habitat and biotopes composition and structure; land orography and especially wind turbines dimensions. Concerning the last one, a radius equivalent to tower height should be defined and this radius must have, at least, five (APA 2010c) or, ideally, 10 meters more than the length of the blade (ICNF 2017) for bat searches. Survey duration is recommended to last a minimum of 20 minutes within a 50 meters radius area (Travassos et al. 2005; Smallwood and Thelander 2008; Atienza et al. 2014; Barclay and Baerwald 2015), which means that time must be adjusted for the radius of the area to prospect. The same logic must be applied to the number of observers involved, that is, divide time for the number of technicians involved. During the searches, the technician must keep a constant velocity while covering the maximum possible area. The guidelines for the search method itself are not very strict since it is suggested that searches may be performed with parallel transects (5 m distanced), zig-zag route (APA 2010a; Atienza et al. 2014) or with other techniques. However, it was already demonstrated that there are no significant differences in the detection success whereas spiral, parallel or random search is used (Santos et al. 2017). This can be profitable whenever the local orography or vegetation structure does not FCUP 31 Methodological and Ecological Insights on Wind Farm Environmental Impact Assessment

present suitable characteristics for complete (and inexpensive) spiral or parallel searches, giving the possibility of choosing random walks (Santos et al. 2017). Whenever the territory exhibits inaccessible and low visibility features, it should be excluded from the survey. As a final remark, the need to account, not only for the persistence bias but also as important, for the detectability one, is evident, as it seems to be more variable and case-specific. In fact, Santos et al. (2016) reviewed that, for casualties in roads, the reported detectability values vary widely between 1% and 67%, which can be extrapolated to wind farm situations. On the other side, Smallwood (2007) summarized searcher detection rates reported on published works and unpublished reports, which highlight its inequality by species group and by the height of the vegetation cover (among small birds). Due to all the mentioned bias sources for fatalities detectability, it is still a challenge to improve the methodologies used, in an attempt to achieve more realistic results to, finally, include on death estimators. Besides taking into consideration this two correction factors, it is important to, once more, highlight the importance of accounting for seasonal variations and, consequently, habitat changes and for necrophages community characteristics during searcher efficiency and scavenging trials, in order to attain more reliable fatality estimates. However, these last two correction factors represent yet a challenge on impact prediction due to this lack of accuracy, which, consequently, makes it difficult to obtain realistic estimates of fatalities for certain site or season (Kunz et al. 2007a).

FCUP 32 Methodological and Ecological Insights on Wind Farm Environmental Impact Assessment

1.6 Mortality Estimators

The two correction factors explored on the last two subtopics, which, as mentioned before, rectify the expectable sources of biases, are combined with the results from the carcass searches into the mortality estimators towards an improved estimate of the real number of birds and bats killed at the monitored wind farm (Erickson et al. 2014; Rodrigues et al. 2014; Santos et al. 2018). Mortality estimators are mathematical formulas which allow death estimates calculation (Erickson et al. 2014). The scientific community, in the last years, has been paying attention and investigation effort to estimators’ logic and precision, so that estimates become more accurate. There is a considerable estimator variety, as a result from different methods to determine and incorporate correction factors, due to the technical and methodological assumptions and, consequently, their limitations (Marques et al. 2018a). Basically, the adjustments for the searched area; carcass removal; and imperfect detection are different between estimators (Bernardino et al. 2013). This development has been especially evident since 2013, as it can be concluded from the mortality estimators amount: Bastos et al. (2013); Etterson (2013); Korner-Nievergelt et al. (2013); Péron et al. (2013); Stevens ND Dennis (2013); Huso et al. (2015); Korner- Nievergelt et al. (2015) adapted from Korner-Nievergelt et al. (2011) and Wolpert (2015). As a result, agreement on a universal estimator is a challenging task, since there are no comparative studies that allow consistent estimates, with low bias, regardless particular wind farm particularities (ICNF 2017). This is because the accuracy of an estimator depends on how well its implicit assumptions match conditions in the field, which vary between years and season, for example (Huso et al. 2016a). With this standardization absence, the comparison of results obtained from different companies, for example, it becomes a more difficult task, although not impossible if the known limitations are taken into consideration. In an attempt to the posterior integration and comparison of standardized results, ICNF recommends that the mortality estimation associated to each wind farm be based on, at least, one of these estimators: Huso 2010 and Körner-Nievergelt et al. 2011. In fact, according to Marques et al. (2018a) analysis, this last estimator was among the estimators most frequent in reports published between 2013 and 2015. In Huso (2010) estimator, considering the search area, it takes into account the proportion of animals that die outside the search plot and the respective probability of including that plot in the total sample. It basically considers this partial coverage of the area beneath the turbines. Then, for search interval, contemplates the ‘effective search interval’ based on the length of time beyond which the probability of a carcass persisting FCUP 33 Methodological and Ecological Insights on Wind Farm Environmental Impact Assessment

is ≤ 1%. In terms of correctional factors error sources, it considers that carcass removal is the mean persistence time (in days), including right-censored observations and assumes that removal times follow an exponential distribution. For search detection probability, it is calculated the empirical proportion of carcasses detected by searchers and the carcasses that are overlooked are accepted to have zero probability of being found in subsequent trials. It results in the following equation (Equation 1):

Eq.1 1 퐶 ̂ 푀 = 푖 휋 − 푡̅ (1 − 푒 푡̅) 푝 [ ] 푣 푑

where 휋 is defined as the product of the proportion of the actual fatalities that is contained in the searched area of the plot and the probability of including that plot in the sample. The parameter 푑 = min (푖, 푖̃), where 푖̃ stands for the length of time beyond which the probability of a carcass persisting is ≤ 1% and, as it assumes an exponential distribution, it can be extrapolated that 푖̃ = − log(0.01) ∗ 푡.̅ On the other side, the ‘effective search interval’ (푖) may be calculated as 푣 = min(1, 푖/푖)̃ . Summing up, the average probability of persistence is defined as (Equation 2):

Eq.2 ̂ 푡̂ ̅ (1 − 푒−1/푡̅ ) 푟̂ = 푚푖푛 (퐼̃̂, 퐼) where 푡̂ ̅ is the average carcass persistence time, 퐼 is the actual search interval and 퐼̃̂

represents the effective search interval, calculated as 퐼̃̂ = − log(0.01) × 푡̂̅. And the estimated searcher efficiency is, on the other hand, described as (Equation 3):

푛 Eq.3 푝̂ = 푖 푘푖

where 푛푖 represents the number of trial carcasses found of each carcass category and

푘푖 is the number of carcasses available and placed on the field. The confidence intervals for Huso (2010) estimator are calculated using bootstrapping methods. Korner-Nievergelt (2011) estimator, on the other hand, is based on a conceptually different model than the one used by Huso (2010). In this case, the only variable not considered in the original formula, comparing with Huso (2010) estimator it is the area searched. However, the other adjustment terms and assumptions associated with this fatality estimator are very different: it implies regular search intervals; it assumes that a mean number of individuals are killed per day and that its removal is constant over time FCUP 34 Methodological and Ecological Insights on Wind Farm Environmental Impact Assessment

and similar for all carcasses. Concerning search detection probability, it considers the empirical proportion of carcasses detected by searchers and where carcasses are not found during the first search, they can be found in a subsequent one. Detectability may be treated as a constant or decreasing variable over time, it all depends on which one of the two formulas are used. Its basic formula, where it is assumed a constant detectability is defined as (Equation 4):

Eq.4 퐶 푀̂ = 1 − 푝푑푖 푠−1 푢 푝 (푝푑 ) (∑푢=0(푠 − 푢) [(1 − 푝)푝푑푖] ) 1 − 푝푑 푠푖

with 푝푑 representing the daily persistence rate (which is the probability of a carcass not being removed during 24 hours) and 푢 = 0, . . ., 푠 − 1 where 푠 is the number of searches performed at intervals of 푖 days. On the other hand, the formula that accounts for decreasing detection probability with the number of searches it is as follow (Equation 5):

Eq.5 퐶 푀̂ = 푠 푥−1 푥−푣 (푥−푣)푖 푥−푣−1 푢 푝퐵 + ∑푥=1 푝퐵 [1 + 푘푝푑푖(1 − 푝) + ∑푣=1 (푘 푝푑 ∏푢=0 (1 − 푝푘 ))] 푠푖

1−푝 푑푖 where 퐵 = 푝푑 is the proportion of carcasses that is expected to remain after 푖 days, 1−푝푑

once the proportion of 푝푑 animals persist daily, and 푘 determines the detection probability decrease along searches. Despite all differences, both estimators assume that the number of carcasses is zero at the beginning of the survey and whenever searcher detection probability is low, especially when the number of fatalities per year is low (< 10), fatality estimates can be strongly biased (Bernardino et al. 2013). These estimators would not be needed if there was a direct (linear) relationship between the number of observed carcasses and the number that were actually killed. Besides carcass persistence and searcher efficiency, four more covariates are included on mortality estimators: study length, wind farm, ecological characteristics and the availability of implementation software (Marques et al. 2018a). The effective proportion of area under study should be considered, which means taking into consideration the number of wind turbines and the size of the area around each one of them (Marques et al. 2018a). FCUP 35 Methodological and Ecological Insights on Wind Farm Environmental Impact Assessment

Due to the increasing complexity of the published estimators, a few software and free platforms to the help in mortality estimators’ implementation have already been developed, such as USGS Fatality Estimator (Huso et al. 2015); Wildlife Fatality Estimator (Huso et al. 2015); R package “Carcass” (Korner-Nievergelt et al. 2015; R Core Team 2015) and Evidence of Absence Software (Dalthorp et al. 2014a). The first one, as the rest of them, estimates the number of fatalities from the number of observed ones during searches, but, what it is special about this software is that it gives estimates that are fundamental to understand the precise and cumulative effects on wildlife populations since it has as objective to estimate the size of the existing superpopulation. It basically follows the conceptual framework explored in Huso (2010). The second one, Wildlife Fatality Estimator, it is an online platform, which allows the calculation of scavenging removal and searcher efficiency correction factors using proper Survival Analysis to model data (Bispo et al. 2010). Relatively to R package “Carcass”, it provides several functions for estimating the searcher probability of finding a dead bird or bat, as also to obtain a posterior distribution of the number of fatalities based on the number of carcasses found and the estimated detection probability to, finally, estimate the total mortality (Korner-Nievergelt et al. 2015). Finally, the Evidence of Absence Software has the particularity of being applicable even when zero carcasses have been found in searches. The advantage of this software is that it uses information about the search process and scavenging rates. Since it considers the search process effectiveness, it can be accepted or not that finding zero carcasses indicates that the overall mortality rate was near zero. In this way, it determines with the maximum credibility the number of fatalities, even when zero or few carcasses are observed (Dalthorp et al. 2014b). FCUP 36 Methodological and Ecological Insights on Wind Farm Environmental Impact Assessment

1.7 Bioinsight company

This masters dissertation was conducted under a partnership between the Sciences Faculty of Porto University and Bioinsight company. Bioinsight is an environmental consultancy group which supplies multiple biodiversity and conservation-related services, such as environmental impact assessment, biodiversity monitoring, and management planning, as well as, for example, economic valuation of biodiversity and scientific and technical advisory. It was first established in Lisbon, in 2005 (as Bio3), but since then, their borders have been expanded and new branch offices were settled in Aveiro (Portugal) and Cape Town (South Africa). This was only possible due to their international partners with large experience in , biodiversity, environmental sciences, technology, and engineering. Over these years, more than 500 projects were developed and 33% were in the wind energy sector. Besides external services, Bioinsight is committed to increasing its know-how and applying new knowledge in real life situations, focused on methods improvement and optimization, concerning more reliable results. To accomplish this, IDI (“Investigação, Desenvolvimento e Inovação”- Investigation, Development, and Innovation) projects are included as an integral part of the Bioinsight's action plan, in an attempt to answer internal goals. In this context, the opportunity for this master thesis appeared as a developing booster for one of these IDI projects. The general focus of this project is to promote the improvement and updating of the methodologies implemented in environmental impact monitoring projects in wind farms, carried out in the operational phase. It aims to use innovative methodologies with the secondary objectives of (1) evaluate and update the mortality estimators used on monitoring reports (in particular with new estimators developed since 2011); (2) update the carcass searches methodology to optimize the monitorization programme effort; (3) implementation of population analysis of target species to determine the impacts of wind farms at a macro level and (4) dissemination and publication of the obtained results. The data collected during this masterwork are part of the IDI project development. Collected data will enable the response to distinct objectives wind energy development related.

FCUP 37 Methodological and Ecological Insights on Wind Farm Environmental Impact Assessment

1.8 Thesis objectives

The objective of this thesis, on a broad scale, is to improve the applied methodology in post-construction impact assessment and increase the ecologic knowledge correlated with wind energy environmental impact assessment, in an attempt to upgrade the correctional factors used to estimate mortality in Wind Power Plants through post- construction monitoring programmes. Concerning the scavengers’ community that lives around wind farm habitat, as an ecological objective of this thesis, necrophages community studies were performed in different wind farms in Portugal, to assess the influence of the scavenger community composition on the removal rate of each site, using data collected from camera traps. Considering the possible interference of wind farm infrastructures on scavengers’ behaviour (avoidance or not) as already proved in the studies already mentioned, one of the aims of this study is to evaluate if the distances of the carcasses to the turbines influence its removal rate, through removal/decay trials, concerning scavengers’ acclimation or not. The last objective is related to methodology improvements on searcher efficiency trials, and, consequently, mortality estimates. In an effort to reduce searcher efficiency bias, new models were tested, and the obtained detectability was compared with the results from usual ones, in order to test if they provide better or worst detectability rates.

FCUP 38 Methodological and Ecological Insights on Wind Farm Environmental Impact Assessment

FCUP 39 Methodological and Ecological Insights on Wind Farm Environmental Impact Assessment

2. Material and Methods

2.1 Study Area

The target areas for data collection for this study were two wind farms located in the north of Portugal: Vila Lobos and Lousã II. Lousã II wind farm is located at the eastern extreme of Lousã mountain, continuing Serra da Estrela and Serra do Açor, in the sub-region of north interior pinewood (ENERSIS, 2006) (Figure 1). Vila Lobos wind farm, located both on Resende and Lamego counties, was established on Serra das Meadas mountain ridges (Figure 1). Both wind farms are included in Rede Natura 2000 of Portugal, more precisely in Serra da Lousã (PTCON0060) and Montemuro (PTCON0025) areas (EEA, 2000).

Fig.1 - Continental Portugal map showing the location of the five wind farms studied in this work: two in the north (Vila Lobos and Três Marcos wind farms), two in the centre (Lousã II and Picos - Vale do Chão wind farms) and one further south (Alto dos Forninhos wind farm). Each dot represents the middle point of the total extent of the wind farm. The quadrangular dots indicate the wind farms where new data was collected. The local of the used databases, provided by Bioinsight, are represented by circular dots.

FCUP 40 Methodological and Ecological Insights on Wind Farm Environmental Impact Assessment

According to Iberwind promoters and to e2p (2017) the Vila Lobos wind farm has an installed capacity of 10MW, produced by 3 turbines in total: 2 Senvion 3.4M114 turbines and another Senvion 3.2M114 turbine. Each turbine has a hub with 90 m high, with a 55.8 m blade length, and a total height of 145.8 m at the tip of the blade. The blade swept area is 9781.79 m2 since rotor diameter is 114 m. The Lousã II wind farm has 20 Nordex N90-R80 and 5 Senvion MM100 turbines. Both models are smaller than the ones in Vila Lobos: the first ones have an 80 m high hub, with a 45 m blade length, culminating in a total height of 125 m and, consequently, a smaller swept area of 6361.73 m2, with a 90 m rotor diameter (Iberwind 2018a). The five new ones are also composed by an 80 m high hub, but with a largest blade length of 50 m resulting in a total height of 130 m and a 100 m rotor diameter forming a swept area of 7853.98 m2 (Iberwind 2018a). The installed capacity in Lousã II is 60 MW (e2p, 2017). Regarding climatic conditions, Vila Lobos is characterized by a continental climatic type, representing a Csb type in Köppen e Geiger classification. During Summer, the average maximum temperature of the hottest month is 21.3 ºC, although 30 ºC have been recorded, whereas average Winter temperature is 6.6 ºC. Concerning total annual rainfall, it varies between 12-179 mm (Merkel 2018). On the other hand, Lousã presents a Csa climatic type, where average Summer and Winter temperatures are 22.7 ºC and 9.3 ºC, respectively. In the current year, the maximum air temperature reached 43.6 ºC in August (IPMA 2017a). January has the maximum average precipitation (136 mm), contrasting with 10 mm average minimum during July (Merkel 2018). The difference between the two locations relies on summer intensity: hot and temperate summer, on Lousã and Lamego, respectively. Vegetation composition and density are distinct in both wind farms, which is evident even only with the observation of satellite images (Figure 2).

Fig.2 - Satellite images from Vila Lobos and Lousã II windfarms, on the right and left side, respectively.

FCUP 41 Methodological and Ecological Insights on Wind Farm Environmental Impact Assessment

Soil occupation in Vila Lobos exhibits a reduced tree and shrub diversity. The presence of burned vegetation structures is still evident (Figure 3a) and, consequently, the ecologic succession is at an early stage. That being said, Vila Lobos is dominated by an Agrostis spp. herbaceous stratum, mixed in a heterogeneous matrix with spread Genista cinerascens agglomerations (Figure 3b), ferns (Pteridium aquilinum L.), with typical shrubs of Ulex europaeus L. and spontaneous Colchium multiflorum (Figure 3c). Armeria transmontana (Samp., G.H.M.Lawrence) is also a common species in between this matrix (Figure 3b). There are also vast areas of revolved soil (Figure 3d), which resulted from windfarm repowering works because not enough time has passed for

(a) (b)

(c) (d)

Fig.3 - Photo examples of the different habitat composition (a, b, c and d) in Vila Lobos wind farm. ecologic succession to recover the affected area. Granitic outcrops are present with variable extensions and densities (Figure 3d). In general, Vila Lobos presents a low habitat and fauna biodiversity (PROCESL. 2013).

On the other hand, the turbines under study in Lousã II, are surrounded mainly by pine trees (Pinus sylsvestris L.), whether from human-made monocultures or naturally grown (Figure 4a). In this last case, some oaks (Quercus robur), birches (Betula pubescens) and another pine tree species (Pinus nigra) occasionally occur (Costa et al. FCUP 42 Methodological and Ecological Insights on Wind Farm Environmental Impact Assessment

1998; Bernardino et al. 2011) (Figure 4c). In both cases, ferns (Pteridium aquilinum L.) occur in more or less dense formations (Costa et al. 1998; Bernardino et al. 2011). Whenever there are no closed pine tree canopy shrubs develop, especially hygrophilous heather dominated by heath (Erica ciliaris L.) and gorse (Ulex minor), interleaved with

(a) (b)

(c) (d)

Fig.4 - Photo examples of the different habitat composition (a, b, c and d) in Lousã II wind farm. Pterospartum tridentatum (L.), with a higher density of bushes than Vila Lobos (Figure 4b). There are also some areas where Cupressaceae (Chamaecyparis sp. and Juniperos sp.) trees are the dominant ones (Figure 4d).

Due to the 2017 year fires in Lousã, the southern part of the park was included on the 781 ha of forest area totally burned and so, for the development of this study, these areas were avoided. In order to respond to the ecologic objective, it was necessary to use data from other Portuguese wind farm projects with different dominant biotopes. Information from three wind farms was used: Três Marcos wind farm located in Vila Nova de Paiva; Picos - Vale do Chão wind farm built in Castanheira de Pêra county; and Alto dos Forninhos in Portalegre. The first one is located in the same district (Viseu) as Vila Lobos wind farm, in the centre region of Portugal. The same situation occurs for the Picos - Vale do Chão wind farm which is located in the low Mondego region, in Coimbra district, as Lousã II FCUP 43 Methodological and Ecological Insights on Wind Farm Environmental Impact Assessment

wind farm. The last one is located on S. Mamede mountain natural park, in the North Alentejo region. In total, information from five wind farms, more or less equally distant from each other was used (Figure 1). It is expected that wind farms in the same biogeographic area present the same dominant biotopes, except when human intervention determined a biotope modification. Três Marcos, despite being geographically close to Vila Lobos, presents a totally different habitat scenario, considering the ecologic succession stage (Figure 5).

Fig.5 - Habitat examples that can be found in Três Marcos wind farm. Resource: Bioinsight company.

According to the report of the environmental impact study performed on this wind farm (Recurso, 2013), detected species are common at national level, with the exception of two with a more restricted distribution: Silene angustifolia and Paradisea lusitanica. Três Marcos surrounding territory has a developed shrub layer, dominated by Pterospartum tridentatum heath, heathers (Erica australis, Erica umbellata) and two Cytisus genera species: C. multiflorus and C. Striatus. Forest plantations with Pinus pinaster, Eucalyptus globulus, and Cupressus sp. are also found there, as well as autochthonous tree species including three Quercus species (Q. pyrenaica, Q. robur, Q.rotundifolia), chestnut (Castanea sativa) and birches (Betula alba). Lastly, some exotic species are present, namely, Acacia dealbata and A. melanoxylon, Robinea pseudoacacia and Hakea sericea. Picos - Vale do Chão, is not very dissimilar from what is observed in Lousã mountain. The vegetal communities found are essentially of coniferous or eucalyptus with a typical low shrubs ground layer, whenever the forest canopy allows it (Figure 6). Mixed deciduous forests can also be found and, less frequently, herbaceous, riparian and fissuric vegetation (Santos, J. et al. 2006).

FCUP 44 Methodological and Ecological Insights on Wind Farm Environmental Impact Assessment

Fig.6 - Different patches composition in Picos - Vale do Chão wind farm. Resource: Bioinsight company.

Finally, Alto dos Forninhos represents an area of high habitat diversity, a consequence of being totally within the natural mountain park of S. Mamede limits and, consequently, belongs to Rede Natura 2000 (PTCON0007). This high biodiversity value is partially due to the fact that wind farm territory is influenced by both Atlantic and Mediterranean conditions, on northwestern and southeast territory, respectively (ICNB 2000). As a consequence, it constitutes the south limit for the distribution of several species with a predominant Atlantic distribution (Bioinsight, 2017a; Bioinsight, 2017b). Therefore, both Atlantic bushes of Erica ciliaris and Ulex minor (habitat 4020*) and Mediterranean ones like kermes oaks (Quercus coccifera), strawberry trees (Arbustus unedo) or Retama sphaerocarpa shrubs can be observed (habitat 5330) (Bioinsight, 2017b). Besides the shrub formations (Figure 7a), Pyrenean oak (Quercus pyrenaica) is also found in dehesa or, as it is called in Portugal, montados, which is very uncommon in national terms, and also corns (Quercus suber) (Figure 7b) and holm oak (Quercus rotundifilia), which are more common in the southern area of Portugal (Bioinsight, 2017b). However, similarly to Lousã, the area is dominated by pine tree (Pinus pinaster) for human production (Figure 7c), with dispersed Quercus and chestnut trees (Castanea sativa) (Bioinsight, 2017b) (Figure 7d). In areas with non-managed pine tree formations, the typical shrub species can be found (Erica sp., Cytisus sp., Ulex sp. and Pterospartum tridentatum). The presence of diverse exotic species it is not an exception in this wind farm, once the same species found in Três Marcos wind farm are present (Bioinsight, 2017a; Bioinsight, 2017b). FCUP 45 Methodological and Ecological Insights on Wind Farm Environmental Impact Assessment

(a) (b)

(c) (d)

Fig.7 - Photo examples of the different habitat composition (a, b, c and d) in Alto dos Forninhos wind farm. Resource: Bioinsight company.

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2.2 Carcass Removal Trials

Carcass removal trials, for birds and bats, occurred during an entire year, to ensure that the obtained results reflected all the expected variations, concerning climatic conditions and, consequently, vegetation cover, and changes in the and aerial scavenger pressure, which also may vary by season (Lynn and Auberle 2009; Smallwood 2013). This goes along with the Portuguese public institution's recommendations since they refer that sampling must occur once per season at each wind farm (APA 2010a; ICNF 2017). In total, four trials were performed for each carcass type, ensuring carcasses monitoring for 15 days, as explained in subchapter 1.5. Mice trials occurred from 4 to 18 September (2017); 6 to 20 November (2017); and, the last one, from 17 April to 1 May (2018). Quail trials were from 2 to 16 October (2017); 4 to 18 December (2017); 8 to 22 January (2018) and, finally, from 2 March to 4 April (2018). The trials were authorised both by the wind-farm developer and the Portuguese Institute for Nature Conservation and Forests (ICNF - Instituto da Conservação da Natureza e das Florestas). In this study, only two size types of carcasses were used, as applied in other projects, such as Enk et al. (2012). Mice (Mus musculus) were used as a surrogate to bat carcasses (Silva et al. 2008; Fish and Wildlife 2011) and quails (Coturnix coturnix Linnaeus) to represent medium size birds, as in Bernardino et al. (2011), Bispo et al. (2013) or Costantini et al. (2016). Medium size birds were chosen since, in Portugal, the bird species more affected by the wind farm structures do not differ substantially from game birds size (Mascarenhas et al. 2018). The mice and quail carcasses are used as feeder animals in wildlife rehabilitation centers or zoos, and for human alimentation purposes, respectively. All animal sacrifice practices followed the Portuguese national laws. All carcasses were frozen prior to use and thawed for 4 - 5 hours before the start of the trial (Arnett et al. 2005; Rodrigues et al. 2014). In terms of numbers, as recommended by Huso et al. (2016b), 30 carcasses of each type were used in each wind farm, in a total of 240 game birds and 180 mice, considering winter as a non-sampled season for bats (ICNF 2017). Carcasses handling was always performed with lab gloves to prevent human odour contamination (Bernardino et al. 2011). In each trial, carcasses were never mixed, which means that first a bird trial was conducted and them a bat one, or vice-versa. Carcasses were monitored through camera-trapping, a motion-activated method to check all placed carcasses. Each carcass was controlled by a single camera of one of the three following models: Reconix - PC90 Pro RapidFire Color Ir Camera; Keep Guard - KG-5CS (2Gb); LTL - Multi-PIR 12 5210M (4Gb); LTL - Acorn 5210A (2Gb) and FCUP 47 Methodological and Ecological Insights on Wind Farm Environmental Impact Assessment

LTL - Acorn 5310A (2Gb), equipped with SD cards (Nilox 2Gb; Lexar Multi-Use 4Gb; SanDisk 4Gb and Selecline 8Gb) with variable memory capacity. The cameras were attached differently according to the wind farm in question, depending on the existence of trees around the selected area. This means that, while in Lousã II wind farm the cameras were placed in the nearest trees, in Vila Lobos it was necessary to use wooden sticks or to build rock structures to them in the environment (Figure 8).

(a) (b) (c)

Fig.8 - Placement method performed on both Lousã II and Vila Lobos wind farm. Camera placed on Lousã mountain tree (a); on a wood stick on Vila Lobos (b) and camouflaged in Vila Lobos wind farm rock structure (c).

All cameras were positioned horizontally, i.e., lens and sensors perpendicular to the ground, and the majority were facing north, to minimize direct sunlight on the camera’s lens and infrared sensors (Smallwood et al. 2010). Cameras were placed 0.5 m above ground, on average, and at approximately 1-2 m from the carcasses (Rovero

et al. 2010; Smallwood et al. 2010; Meek et al. 2014). Cameras were programmed to take 3 consecutive photos for each infrared sensor trigger, with a 1 minute interval between each trigger, and their sensibility was set to normal. Camera front vegetation was partially removed to minimize blank images caused by wind-blowing (Meek et al. 2014). Furthermore, the carcasses, besides being centred within the camera field of view, were also placed in a rock or in front of some vegetal structure, to facilitate posterior photo analysis (Figure 9).

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Fig.9 - Carcasses types (Mus musculus and Coturnix coturnix on the left and right photo, respectively) on placement site examples.

Cameras and carcass distribution were within a turbine surrounding buffer, whose diameter varied according to the maximum projection of carcasses in collision situations and according to the total turbine height, as described in Hull and Muir (2010) ballistic studies. The Lousã II and Vila Lobos wind farms, under study on this work, both have high turbines, which translates to a buffer radius of 140 meters for each turbine (Hull and Muir 2010). Inside this buffer, a 100 m x 100 m grid was defined and every square centroid that was contained in buffer was also illustrated (Figure 10). Cameras were then distributed by these grid centroids in a random manner to assure a random distribution (Paula et al. 2018).

Fig.10 - Example of the methodology for camera-traps random distribution. It is represented the 100 x 100 [m] grid; the 140 m buffer limitation and the possible camera placement sites.

FCUP 49 Methodological and Ecological Insights on Wind Farm Environmental Impact Assessment

Therefore, both cameras and carcasses were distributed at different distance ranges from the turbine, in order to see if there is a relationship between distance and the carcass removal rate. All the procedures mentioned above, such as buffer and grid definition, and camera distances determination, were executed using Manifold ® v8.0 GIS software (Manifold Software Ltd.). This predetermined position could undergo changes according to land constraints. In these cases, new locations were recorded in the GPS (Garmin eTrex Legend GPS, Olathe, Kansas, USA), always respecting the minimal 100 meters distance between them (Paula et al. 2015; Abernethy et al. 2016). This contributed to minimising scavengers’ attraction due to the excessive food availability in such a constricted area and, consequently, the bias of the results by accelerating the carcass removal time interval (Travassos et al. 2005; Smallwood et al. 2010). Camera placement site and the battery status, as recommended for Portugal (APA 2010a; ICNF 2017) and verified by Bernardino et al. (2011) in the Lousã mountain ridge, were checked after 15 days as performed in previous studies (Curveira-Santos et al. 2017; Oberosler et al. 2017). Furthermore, it was registered if the carcasses were removed or not (Smallwood et al. 2010; Paula et al. 2015).

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2.3 Searcher Efficiency Trials

The detectability tests were performed during the cold season. It took place on the 5th and 6th December, and continued on the 19th, 20th, 28th, and 29th December, on Vila Lobos wind farm. Whenever the weather conditions did not allow the execution of detectability tests (dense close fog or strong rainfalls), they were rescheduled. In order to test carcass detectability, it was necessary to map the existing vegetation and, consequently, the biotopes richness of the wind farm. The information was registered through field observation and then digitalized in Manifold ® v8.0 GIS software (Manifold Software Ltd.). The resulting map allowed the territory classification according to different visibility classes, considering vegetation height and density. The classification scheme has the same 4 categories described in Strickland et al., (2011) and in Arnett et al., (2008): easy, moderate, difficult and very difficult. However, in the territory surrounding turbines, there were some patches (although with a little global representativeness) where the prospection was not feasible or even dangerous due to vegetation height and density (Huso and Dalthorp 2014); these areas were considered as inaccessible and so excluded from the searcher efficiency trials (APA 2010a), summarizing a total of five visibility classes (Table 1).

Table 1 - Visibility classes according to respective ground cover and vegetation characteristics.

Visibility classes

Visibility Class Ground cover Vegetation Class 1 Sparse with ≤ 15 cm tall ≥ 90% bare ground (easy) (i.e., gravel pad or dirt road). Class 2 Mostly sparse with ≤ 15 cm ≥ 25% bare ground (moderate) tall Class 3 ≤ 25% bare ground ≤ 25% > 30 cm tall (difficult) Class 4 Little or no bare ground ≥ 25% > 30 cm tall (very difficult) Class 5 Little or no bare ground ≥ 70% > 90 cm tall (inaccessible) Source: Adapted Strickland et al., (2011) and Arnett et al., (2008)

According to this classification, access roads and respective berms were included on the first class; followed by deforested areas of the previous locations of wind turbines on the second class; the third class includes mostly herbaceous meadows and, the fourth class, a homogeneous mixture of ferns and different shrubs species (Figure 11). FCUP 51 Methodological and Ecological Insights on Wind Farm Environmental Impact Assessment

(a) (b)

(c) (d)

(e)

Fig.11 - Photos of Vila Lobos wind farm visibility classes. A total of 5 classes are presented (one of them inaccessible for survey) according to their vegetation characteristics: (a) Class 1 – easy; (b) Class 2 – moderate; (c) Class 3 – difficult; (d) Class 4 – very difficult and (e) Class 5 – inaccessible.

As recommended by the Portuguese guidelines (as explained on chapter 1), normally, a 50 meters radius buffer is defined around the turbines (APA 2010a). In this study, however, for each visibility class, a homogeneous area with, approximately, 7854 m2 (equivalent to the area of the 50 m buffer previously mentioned) was randomly defined, to maintain the same models’ density. In each defined area, 10 models of each class size (small, medium and large), as mentioned on subchapter 1.5, were randomly distributed (Bernardino et al. 2011; Grodsky et al. 2013; Paula et al. 2018), summarizing a total of 30 models for visibility class. Each area was prospected for 20 minutes (APA 2010a). Whenever there was not enough area to the intended polygon, this was defined FCUP 52 Methodological and Ecological Insights on Wind Farm Environmental Impact Assessment

with half the area and, consequently, only 15 models were prospected, each time, for 10 minutes. Models for this study were used as recommended by national authorities, aiming to represent real carcasses in the field (APA 2010a). However, besides the undifferentiated models commonly used in detectability tests (Figure 12) new ones were tested (Figure 13).

Fig.12 - Models’ examples usually used in detectability trials to represent large, medium and small individuals, from left to right, respectively.

(a)

(b)

(c)

(d)

Fig.13 - New models’ examples tested in detectability trials. Models attempt to represent large bird wood pigeon (Columba palumbus) (b), fieldfare (Turdus pilaris) as a medium size bird (c) and bats (d), from top to bottom on right side. Size proportion comparation is presented on (a).

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Large and medium models were substituted by plastic hunting decoys similar to the wood pigeon (Columba palumbus) and fieldfare (Turdus pilaris), respectively. On the other hand, to represent bats, artificial models made of wire and tissue were used. The small model normally used represents both passerines and bats. However, in this specific work, there is no new model to simulate passerines and, consequently, the tissue bats will be called the small ones, despite not representing passerine species. The logic of this procedure requires, at least, two technicians. One for model distribution in the previously marked points along the selected area, using a GPS. In this work three GPS devices were used: a Garmin eTrex Legend®, a Garmin eTrex® 10 and a Garmin GPSmap 60® (Olathe, Kansas, USA). The other, without prior knowledge of the models’ location, searched the area delimited by other GPS, testing its detection capacity. To maximize field coverage, a random walk method was applied, whenever possible, by the field technician (Santos et al. 2017). In each prospection, the technician kept a constant velocity, covering as much of the area as possible within the defined range, spending 20 minutes in each prospection (APA 2010a). Inaccessible areas or with very low visibility were excluded from the prospection. Every time a model was found, the observer collected it and registered its identification code. For each visibility class, detection trials were repeated three or six times, when 30 and 15 models were placed, respectively, which means that each model type was tested 30 times. In order to prevent the observer’s memorization of the models’ placement site in some areas, for each one of these prospections, a different GPS shapefile was used, with different random models’ distribution. It summarized a total of 36 different shapefiles. However, throughout all surveys, observations were made by the same field technician intending to reduce error (Lynn and Auberle 2009) as executed by Piorkowski and O’Connell (2010).

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2.4 Data Treatment

To answer all the objectives, some previous data organization was required in order to fulfil the Wildlife Fatality Estimator (WFE) platform requirements. Concerning carcass removal data, a specific matrix organization was necessary, where data was categorized in respect to what was known about removal time. This means that different values were attributed according to the following situations: the carcass remained until the last verification day; the exact removal moment is known; carcass was removed between the placement day and the first verification or it was removed between verifications. Data treatment was performed separately for birds and bats (mice). In the case of searcher efficiency data, each model was associated with its size or other independent variables, such as the replication information, and with a binary response variable concerning model detection. Although no other data treatment was necessary to answer the objective where new models were tested; for the other ones, where directly or indirectly, removal data information was required, the analysis of all photos obtained through camera trapping method was done. Photo analysis implied two different proceedings. Firstly, registration of the removal day and hour, which is fundamental to calculate removal rate. Admitting that the camera trap method is effective, and to maintain a systematic error concerning removal interval time, it was assumed that the last photo containing the carcass corresponded to the approximate removal time. In situations where carcasses were eaten in front of the cameras, the logic was the same. When the first photo already did not contain the carcass, or when the light reflection did not allow the visualization of the first few photos, the removal time was considered as the first visible and perceptible photo. In order to meet the proposed ecological objective, it was necessary to analyze the local fauna community and, especially, the existing necrophage community in each wind farm. This meant that the individuals were identified through photo analysis, whenever possible. However, in some situations, individuals appeared only near the carcass and in the next photo both the carcass and individual were not present, which make them must probably the removal agent. On other situations it was possible to affirm the species responsible for the removal, since it were captured eating or carrying the carcasses. For analysis purposes, however, the photographed individual, regardless of the mentioned situation above, was considered as the removal agent. Finally, to verify if the removal rate varied along the distance to turbines, cameras were organized by distance ranges. These distance ranges were defined to capture the FCUP 55 Methodological and Ecological Insights on Wind Farm Environmental Impact Assessment

full distance range. Each interval included the same number of cameras, with all seasons covered: < 75 m; 75 – 115 m; >115 m. Cameras were distributed randomly, as referred before, and distances end up by varying from 38 m to 138 m in Vila Lobos wind farm and from 36 m to 158 m in Lousã II one.

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2.5 Data Analysis

2.5.1 Correctional Factor Calculation

The average time carcass persistence and searcher efficiency were calculated as recommended by ICNF and as performed by Bioinsight company. This ensures the comparability of these results with the ones already in Bioinsight’s possession, which guarantees the development of the IDI07 project. Correctional factors were calculated applying the modules available on the online platform Wildlife Fatality Estimator (www.wildlifefatalityestimator.com), producing carcass persistence and searcher efficiency factor according to the following estimators: Huso (2010), Korner-Nievergelt (2013) and Jain et al. (2007). The Carcass Persistence module is based on Survival Analysis techniques, since it is intended to analyze "lifetime" data, which is the time until the occurrence of a certain event, in this case, the removal of the carcass (Bispo et al. 2010). Once the empirical survival curves Ŝ(t) are estimated according to the non-parametric model (Kaplan-Meier Curves), one of the following parametric models is selected, according to the best adjustment to the probability of corpses remaining over time: Exponential, Weibull, Log- logistic and Log-normal distributions. This selection of the goodness-of-fit model is carried out through graphical analysis and likelihood criteria, which translates into the choosing the model with the least AIC (Akaike Information Criterion) value (Bispo et al. 2013a). The final model is defined using Stepwise procedures (based on AIC values) and the non-significant covariates should be excluded from it (Bispo et al. 2010). This module calculates both the mean persistence rate and the mean persistence time to account for carcass removal. While the mean persistence rate stands for the average proportion of carcasses that died between two consecutive searches and was not scavenged until the end of the search interval (Jain et al. 2007), the mean persistence time denotes the average number of days a carcass persists before it is removed by scavengers and/or decomposition (Huso 2010). These correctional factors: mean persistence time and mean persistence rate is then applied in Huso (2010) and Korner- Nievergelt (2011) estimators. In order to answer the proposed objectives in this study, only the mean persistence time was required, due to its better suitability to answer the proposed objectives of this master thesis. On the other hand, the Searcher efficiency module estimates the overall detection probability and the carcass detection probability for one or two independent variables FCUP 57 Methodological and Ecological Insights on Wind Farm Environmental Impact Assessment

that, in this work, are the models’ size and visibility class. In this module, binomial mixed models are used to estimate search efficiency. The obtained results for both correctional factors were used as an integral or partial part of the response to the proposed objectives.

2.5.2 Scavenger Communities

In order to understand if the local scavenger communities can induce differences in the carcass-persistence time between the analyzed wind farms, the carcass-persistence correctional factors values obtained from the WFE platform from Vila Lobos and Lousã II wind farm were compared with the same values from the other three projects mentioned above. This was a qualitative comparison since there is just a single value associated with each carcass type, season or a conjugation of both for each wind farm. Using data from other projects, with different methodologic approaches, such as different outputs available to compare, and different samples sizes, implied that the different influences of the independent variables on the results were taken into account. This means that not all the information available could be compared and, in some cases, the average value was calculated and used. The first analytical approach was to compare Vila Lobos and Lousã II values with wind farms geographical closer to them: Três Marcos and Picos - Vale do Chão wind farms, respectively. Then, differences and trends were analysed between them and the farthest one (Alto dos Forninhos wind farm). Scavenger community data (total frequencies and abundance), was associated with each carcass-persistence value.

2.5.3 Carcass - Persistence versus Turbine Distances

The variation of the carcass-persistence values with the proximity to the turbines was tested using the WFE platform. In order to test the influence of two variables (season and carcass type) in the carcass-persistence values by distance range, for Vila Lobos and Lousã II wind farm, four variable combinations were executed in WFE platform, separately. Whenever a variable had no influence on carcass removal, a new input matrix was tested without that variable.

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2.5.4 Use of New Models in Detectability Trials

To test the new models’ detectability and compare it with the detectability of the usual ones, additional tests were performed. With this objective, both parametric and non- parametric tests were used, with confidence intervals of 95%. When considering the models’ size or visibility class as the tested variable, a sample of 푛 = 12 or 푛 = 9 was used. To test for differences in mean detectability values among detectability of both models’ size and visibility classes, independently of each other, a one-way analysis of variance (ANOVA) was done. The equality of variances was previously tested using Levene’s Test to verify homoscedasticity. These tests were possible due to the same sample sizes. Whenever the ANOVA results were statistically significant, a post hoc analysis was performed (Tukey’s multiple comparison test). This procedure was performed independently for the new models’ tests and for the usual ones, from Bioinsight. To test the independence of the two classification criteria (models size and visibility class), a Chi-square test was applied, using a contingency table, for the new and the usual models separately. Finally, a non-parametric Mann–Whitney U test was conducted in order to test if there was any significant difference between models’ type, for the same visibility class. All analyzes were performed using IBM SPSS Statistics for Windows, version 24.0.

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3. Results and Discussion

3.1 Carcass Persistence Calculation

Carcass persistence was calculated for both Vila Lobos and Lousã II wind farms, separately for birds and bats (mice). For Vila Lobos wind farm, according to the WFE analysis, the parametric model with the smaller AIC value and so, with the better adjustment to removal times, was the Log normal model for both birds (AIC = 519.71) and bats (AIC = 375.18). According to Bispo et al. (2013b), this is one of the two regression models that fitted the removal time data better in most wind farms. It was, therefore, used in the subsequent analyses. In the bird case, stepwise results showed that removal of the variable season implied an increase in the AIC value, and, consequently, a worse adjustment of the curves to the data, keeping this variable in the final model (Table 2). On the other hand, in bat case, the removal of the variable season implied a decrease in the AIC value, and so it was removed from the analysis, since this means that, in this case, this variable did not affect carcass removal.

Table 2 - Results of the Stepwise process, for the final model. The Autumn season was considered as the reference season for the variable. The statistical significant p values (<0.05) are indicated with a *.

Covariables Estimate Standard Error z p Intercept 0.6158 0.1932 3.187 0.00144*

Spring -0.0861 0.2751 -0.313 0.75441

Summer -0.2291 0.2735 -0.838 0.40219

Winter 0.7513 0.2758 2.724 0.00645*

Log (scale) 0.0566 0.0649 0.873 0.38256

Basically, while in bird case carcass persistence was influenced by season, in bat group this was not verified. As can be seen in Table 2, although the intercept value is statistically significant, only in winter season this difference is relevant (p = 0.00645). This significantly higher winter persistence registered for birds is likely to be a consequence of the last winter season (2017/2018) that was characterised by below normal precipitation and temperature values leading to a colder and drier winter. In fact, the average value of the air temperature in February, in which fieldwork took place, was the 3rd lowest value since 2000 and the drought situation that has been observed since FCUP 60 Methodological and Ecological Insights on Wind Farm Environmental Impact Assessment

April 2017 has been maintained (IPMA 2018a). It was already verified in several studies that carcass-persistence times are dependent on seasons or other variables heavily dependent on those, such as temperature or rain (Smallwood et al. 2010; Villegas- Patraca et al. 2012; Paula et al. 2015; Costantini et al. 2016). It is known that temperature has a clear relationship with mammals carcass attraction since decomposition odour is the dominant stimulus used by them (DeVault et al. 2004). In this specific case, below normal cold temperatures may have resulted in a higher carcass-persistence, due to this lower scavenger attraction. Moreover, a slower carcass decomposition rate due to low temperatures release less odour and more slowly (Putman 1978; Devault and Jr 2002; Ponce et al. 2010). This tendency was not registered for mice carcasses since they were not included in winter sampling. The resulting mortality correctional factor, associated with the removal of corpses (mean persistence time, in days), taking into account the final model, are presented in Table 3. Survival Curves are also presented for birds and bats (Figure 14), according to the non-parametric (Kaplan-Meier) and parametric (Log-normal) models, for the final model.

Table 3 - Correction factor for carcass removal, required to estimate mortality using the estimator Huso 2010 (Mean Persistence Time, in days - 푡)̅ , for birds (A) and bats (B), for Vila Lobos wind farm. It is also presented the standard error (se) and the upper (UCI) and lower (LCI) 95% confidence intervals.

Correctional Factors

Season 푡 ̅ se LCI95 UCI95 Spring 3.00 0.79 1.45 4.55

Summer 2.78 0.80 1.21 4.36 A Autumn 4.03 0.99 2.10 5.97 Winter 7.55 1.18 5.24 9.86

Spring 4.03 0.98 2.12 5.95

B Summer 3.34 0.82 1.74 4.95 Autumn 4.69 1.03 2.66 6.71

Their analysis allows us to verify that, for birds, the removal is faster in Summer, followed by Spring, Autumn and finally slower in Winter, due to the increasing values of the mean persistence time. The same tendency is verified for bats, despite the absence of winter season. These findings are consistent with the results of other studies, developed in Portuguese wind farms (Bernardino et al. 2011; Bispo et al. 2013a) and in other countries such as, for example, USA (Olson et al. 2016). FCUP 61 Methodological and Ecological Insights on Wind Farm Environmental Impact Assessment

A

B

Fig.14 - Probability of a carcass remaining in place t or more days in terms of the type of carcass, represented by the Kaplan-Meier curve and Log-normal model (final model), for birds (A) and bats (B), in Vila Lobos wind farm. In B, Level 1 – Autumn; Level 2 – Spring and Level 3 – Summer.

This trend can be explained, as already mentioned by Morrison (2002), by

scavengers activity seasonality (movement and activity patterns), such as by the invertebrates one (Ray et al. 2014; Olson et al. 2016). Many mammalian scavengers are less active in winter while in spring/summer both mammalian and avian scavengers will have added nutritional demands by their growing young (Baker et al. 2001; Prosser et FCUP 62 Methodological and Ecological Insights on Wind Farm Environmental Impact Assessment

al. 2008; Soulsbury et al. 2008; Ruzicka and Conover 2012). Moreover, an increase in the number of foraging scavengers is felt after the young are weaned (Ruzicka and Conover 2012). This means that seasonal changes affect both the probability of carcass removal over time and the diversity of species that would take advantage of this new food source (Olson et al. 2016). However, the dynamics between mammals and insects it is also relevant on carcass removal. In red fox case, for example, it can be noticed the avoidance towards carcasses with high insect activity, since they will only start scavenging at a faster rate once insect activity has decreased and remains have begun to dry (Young et al. 2015). This could lead to most frequent removals during colder seasons (Young et al. 2015) instead of during the expected hotter ones. Following the same procedure as before, in Lousã II wind farm case, the

parametric model with the smaller AIC value was, as verified in Vila Lobos one, the same for both biological groups. However, in this case, the model with the better adjustment to removal times was the Log-logistic model for both birds (AIC = 486.22) and bats (AIC = 340.89). The Log-logistic model is the other regression model, together with Log-normal,

which best fitted the removal time in most wind farms (Bispo et al. 2013a). Lousã II stepwise results were the opposite of what was registered in Vila Lobos wind farm. While in the bird case, the removal of the variable season implied a decrease in the AIC value, in bat case an increase was registered, which implies maintaining this variable in the final model (Table 4). This means that the variable season did not have any influence on bird carcass persistence times, but its effect was felt for mice carcasses (Table 4). Through Table 4 observation, it is possible to admit that only Summer had a statistically significant influence on carcass persistence time.

Table 4 - Results of the Stepwise process, for the final model. The Autumn season was considered as the reference season for the variable. The statistical significant p values (<0.05) are indicated with a *.

Covariables Estimate Standard Error z p Intercept 0.2952 0.1465 2.0157 4.38푒−02*

Spring -0.0179 0.2078 -0.0862 9.31푒−01

Summer 0.5785 0.2290 2.5256 1.16푒−02*

Log (scale) -0.7028 0.0958 -7.3403 2.13푒−13*

As explained before, in Vila Lobos case, it was expected that during the hotter season the mean persistence time would be lower than in colder ones, due to insect communities or scavenger activity (Kočárek 2003; DeVault et al. 2004; Prosser et al. 2008; Bispo et al. 2013a). Moreover, the fact that it is a small carcass (mice) could result FCUP 63 Methodological and Ecological Insights on Wind Farm Environmental Impact Assessment

in a lower mean persistence time, since it was already observed in many studies that the removal rate increased with decreasing carcass size (Ponce et al. 2010; Villegas-Patraca et al. 2012). However, as it was already observed in other studies, the absence of influence by both variables (season and carcass size) on carcass-persistence time is possible (Silva et al. 2008; Borner et al. 2017; Barrientos et al. 2018). According to the non-parametric (Kaplan-Meier) and parametric (Log-logistic) models, survival curves are presented below for both birds and bats, for the final model (Figure 15). It is possible to affirm that, for birds, the removal is faster in Autumn. Then, as confirmed by the mean persistence time values, presented in Table 5, it is slower in Summer, followed by Spring and Winter appears to be the season when carcass persists more time in the field. Concerning bats, the faster removal season was also Autumn, although Spring presented a faster removal rate than Summer, the opposite of what was verified for birds.

FCUP 64 Methodological and Ecological Insights on Wind Farm Environmental Impact Assessment

A

B

Fig.15 - Probability of a carcass remaining in place t or more days in terms of type of carcass, represented by the Kaplan-Meier curve and Log-normal model (final model), for birds (A) and bats (B), in Lousã II wind farm. In B, Level 1 – Autumn; Level 2 – Spring and Level 3 – Summer.

In both cases, birds and mice, as mentioned before, results show an unexpected tendency, since mean persistence times (in days) were smallest in Autumn season (Table 5). Of the several possible factors that could justify these values, it was considered that in this case, (1) the scavenger community composition/diversity and respective abundance, and/or (2) the occurrence of uncontrolled environmental events, were the ones that could provide a plausible explanation. FCUP 65 Methodological and Ecological Insights on Wind Farm Environmental Impact Assessment

Table 5 - Correction factors for carcass removal, required to estimate mortality using the estimator Huso 2010 (Mean Persistence Time, in days - 푡),̅ for birds (A) and bats (B) for Lousã II wind farm.

Correctional Factors

Season 푡 ̅ se LCI95 UCI95 Spring 4.30 0.99 2.36 6.24 Summer

3.76 0.84 2.12 5.39 A Autumn 2.43 0.71 1.05 3.82 Winter 4.63 1.04 2.60 6.67 Spring 3.27 0.88 1.55 4.99

Summer B 5.09 0.98 3.18 7.00 Autumn 2.45 0.62 1.24 3.66

Concerning the first possible factor (scavenger community), it was observed that the red fox (Vulpes vulpes) appeared with a much higher frequency in Autumn (n = 313) than in Summer (n = 83) (Figure 16). However, it is during Spring that cubs birth occurs, and after, during Summer, they leave the den to initiate adult activities with their progenitors (Doncaster and Macdonald 1997). On the other hand, scavenging activity can suffer an increase during the dry season due to a later abundance of juveniles, and, consequently, greater energy needs of the offspring (Cavallini 1996) as a higher food pressure, which may explain lower carcass persistence probabilities (Borner et al. 2017). Regarding the other most relevant scavenger species in Lousã wind farm, there were no abnormal trends. Regardless, the total number of records is higher in Autumn (461), followed by Summer (317), Spring (211) and, finally, Winter (176). Basically, in this specific wind farm during these sample trials, the higher frequencies were observed during Autumn, especially due to the red fox. Looking at the second possible explanation, it is known that the last Autumn season (2017) in Portugal was hot and extremely dry. In fact, it was the 2nd driest Autumn and the hottest since 1931, once it has exceeded the highest maximum and minimum air temperature values for October (IPMA 2017b, c). These extreme conditions propelled the fires that occurred in Lousã mountain ridge, being considered as one of the major fires of the 2017 year, due to its extension and damage. Hundreds of hunting specimens have perished: 110 to 220 deer (Cervus elaphus), 50 to 90 bucks (Capreolus capreolus) and 90 to 180 wild boars (Sus scrofa) (Diário de Notícias 2017). This forest fire took place on October 15th, just three weeks before the beginning of the Autumn sampling trial. However, this fire took place on the opposite side where camera-traps FCUP 66 Methodological and Ecological Insights on Wind Farm Environmental Impact Assessment

were settled. Hereupon, these higher values of frequencies during Autumn may be, firstly, a result of an escaping behaviour due to the occurrence of fires and, also, a result of a shift in the foraging behaviour, since the burned areas do not have the necessary resources for wildlife. A third possible cause to the obtained tendency may be simply related to vegetation cover. During the dry season, the lesser vegetation cover might favour the carcass detection by scavengers since it was already verified that carcasses placed in high visibility habitats were removed faster of those in lower visibility ones (Arnett et al. 2005).

325 313 300 Red Fox 275 Wild Boar Dog 250 Stone Marten 225 203 Common Genet 200 175 162 150 120 125 99

TotalFrequencies 100 83 75 62 50 32 25 16 21 25 8 1 0 0 6 2 5 5 2 0 Spring Summer Autumn Winter

Fig.16 - Total frequencies of the five scavenger species most common, which were captured with camera-trap method, during the four seasons, in Lousã II wind farm.

In the bat removal simulation, following the smallest values of the mean persistence time observed in Autumn, it comes, as already mentioned, the Spring season instead of Summer, the opposite of what was registered for birds on the same location. Collected data, however, could not justify this difference between biological groups. Summing up the above results, it is possible to reaffirm that the influence of season on carcass-persistence time is not linear, due to the different tendencies observed in the same place, under the same meteorological conditions. Said that, the mean persistence time may be probably more dependent on the activity patterns of scavengers in a particular place rather than on environmental variables such as temperature, such as stated by several authors (Brown and Hamilton 2006; Bernardino et al. 2013; Costantini et al. 2016). This statement reinforces the relevance of trying to FCUP 67 Methodological and Ecological Insights on Wind Farm Environmental Impact Assessment

understand if there is any relationship between the community of scavengers and the rate of removal observed throughout the continental territory. FCUP 68 Methodological and Ecological Insights on Wind Farm Environmental Impact Assessment

3.2 Scavenger Communities

3.2.1 Photo Analysis

From both Vila Lobos and Lousã II wind farms, a total number of 111435 photos, from all seasons (Spring = 25823; Summer = 64864 photos; Autumn = 15715 photos; Winter = 5033 photos) was analysed. The camera traps placed for removal trial purposes, which resulted in the number of photos above, revealed the vertebrate community in all study areas (Attachment I), including some amphibians and reptiles. More importantly, it allowed the identification of the scavenger community, partially or totally. Firstly, in Vila Lobos, it was registered the presence of Corvus corone (Linnaeus), Falco tinnunculus (Linnaeus), Felis sp., and Herpestes ichneumon (Figure 17).

(a) (b)

(c) (d)

Fig.17 - Photo examples of the scavenger species found in Vila Lobos wind farm: (a) the carrion crow; (b) the common kestrel, (c) Felis sp. and (d) the Egyptian mongoose.

Besides these ones, more three species were detected, not only in Vila Lobos, but also in Lousã territory: Canis familiaris (Linnaeus), Sus scrofa (Linnaeus) and Vulpes Vulpes (Linnaeus) (Figure 18). These were the only detected species in common between the wind farms.

FCUP 69 Methodological and Ecological Insights on Wind Farm Environmental Impact Assessment

(a) (b)

(c) (d)

(e) (f)

Fig.18 - Photo examples of the common species between Vila Lobos (VL) and Lousã II (LO) wind farms. Photos on the left and right side are from VL and LO wind farms, respectively. Species represented are the domestic dog (a and b), the wild boar (c and d) and red fox (e and f).

In Lousã II wind farm, the species Buteo buteo (Linnaeus), Garrulus glandarius (Linnaeus), Genetta genetta (Linnaeus) and Martes foina (Erxleben,1777) (Figure 19), were the ones captured removing carcasses, exclusively from this wind farm territory, when compared with Vila Lobos one. FCUP 70 Methodological and Ecological Insights on Wind Farm Environmental Impact Assessment

(a) (b)

(c) (d)

Fig.19 - Photo examples of the scavenger species found in Lousã II wind farm: (a) the common buzzard; (b) the Eurasian jay, (c) the common genet and (d) the stone marten.

During trials, not all the exact removal moments where capture due to some variables (e.g. weather conditions), which affected trapping efficiency and sensitivity or equipment flaws (Paula et al. 2015; Anile and Devillard 2016). Of the total carcasses placed (푛 = 420), and considering the defined events type, this method allowed the capture of 244 exact removal moments (58.9%). When analysing the collected data separately for the two wind farms the percentages do not appear to be very distinct. In Vila Lobos wind farm, in 52.9% of the cases the exact carcass removal time is known, not very different from the 63.3% obtained in Lousã II wind farm. Regarding the cases when the carcasses were not fully removed or decomposed (right censored data), 2.9% and 2.4%, were registered on Vila Lobos and Lousã II, respectively. Following the same order and due to camera failure, in 30.9% and 21.4% of the cases, the carcass was removed between its placement and the first photograph (left censored data). This percentages also include the camera thefts events. Finally, in Vila Lobos, 13.3% of the carcasses were removed between two photos and in Lousã II wind farm it did not vary much (12.9%) (interval censored data). The obtained percentages for exact removal moments and equipment flaws or camera thefts are not very dissimilar of what was registered in other studies, such as FCUP 71 Methodological and Ecological Insights on Wind Farm Environmental Impact Assessment

Paula et al. (2015). Moreover, the camera trapping method, when compared to the conventional methodology, brings a meaningful improvement in carcass removal information. Regarding the theft events, in total, four and six cameras were stolen in Vila Lobos and Lousã II wind farm, respectively, as well as four memory cards at Vila Lobos wind farm, which interfere with the data collection. Photo analysis revealed multiple different behaviours at the carcass site placement. In both wind farms, most of the carcass removal occurred during night, since mammals are mainly active in the dark period of the night or in twilight, in particular, in the morning (Killengreen et al. 2012). However, the main scavenger species registered, the red fox, was captured both during the day and night. Although the red fox is mainly crepuscular and nocturnal (Díaz-Ruiz et al. 2016), other studies such as Monterroso et al. (2014), support that it is a ‘facultative nocturnal’ species. The data collected in this study just gives more evidence for this behaviour. It was also clear that there is a different activity pattern between birds and mammals since birds were registered close to the carcass placement site only during daylight. This difference is not unexpected due to the specific searching modes for carcasses: while birds mainly use vision, mammals tend to use olfaction (Killengreen et al. 2012). This would lead to a low probability for direct interspecific interaction between the two taxa. However, both on Vila Lobos and Lousã wind farm, despite the canopy presence, this was not the case, since the most common scavenger species, the red fox, also has this behaviour during daylight (Monterroso et al. 2014; Paula et al. 2015). Photo analysis also allowed to understand that the feather spots found resulted from scavenger feeding activity in front of the device (Figure 20), although the individuals majority carried away the whole carcasses (Figure 20).

Fig.20 - Red fox feeding in front of the camera placement originating feather spots (a) and carrying away the carcass (b) from its original placement site. FCUP 72 Methodological and Ecological Insights on Wind Farm Environmental Impact Assessment

In some situations, it was observed that potential scavengers did not remove carcasses after staring at the camera (Figure 21), which may suggest the possibility of discouragement. This possibility was already mentioned in other studies (Smallwood et al. 2010). In Meek et al. (2015), for example, it is said that in unpublished data, the presence of camera traps is known to affect foxes and this species was, in fact, observed several times running or with fear behaviour near camera-traps placement site. The present study just reinforces the need to understand if, in fact, this is frequent behaviour and if it can negatively bias population estimates, as also the opposite attraction behaviour.

Fig.21 - Examples of staring behaviour by scavengers when they perceived camera trap presence. It is shown a red fox (Vulpes Vulpes) on the left photo and a stone marten (Martes foina) on the right one, captured in Lousã II wind farm.

After carcass removal, several individuals, specifically red foxes and mustelids, were captured urinating/defecating on the carcass placement site (Figure 22), after site recognition, which may indicate territory marking (Soulsbury and Fawcett 2015; Mumm and Knörnschild 2018).

Fig.22 - Example of territory marking by red fox (Vulpes Vulpes) and stone marten (Martes foina), on the left and right photo, respectively, captured during carcass removal trials. FCUP 73 Methodological and Ecological Insights on Wind Farm Environmental Impact Assessment

3.2.2 Carcass Persistence Between Wind Farms

For comparisons between Vila Lobos and Três Marcos wind farm, it was only possible to use the correctional factor obtained for mice removal, since in Vila Lobos wind farm the variable season influence bird carcass removal and there is only a single value for Três Marcos case. Despite the relative geographical proximity between wind farms, it is notable the higher mean persistence time of mice carcasses in Três Marcos than in Vila Lobos wind farm (Table 6).

Table 6 - Correction factor for carcass removal, required to estimate mortality using the estimators Huso 2010 (푡)̅ , for bats, for Vila Lobos and Três Marcos wind farm. Correctional Factor

Wind Farm Mean Persistence Time, in days (푡)̅

Vila Lobos 4.02 Três Marcos 7.53

In this specific case, differences cannot be attributed to different years of the sampling or missing seasons, since sampling was performed in all of them, on the same year. However, this discrepancy may be attributed to other features. Firstly, these areas, as already explored in Chapter 2.1, although only, approximately, 25 km apart (straight line), exhibit distinct habitat composition. In one hand, Vila Lobos is under an ecological process of secondary succession, with only a developed shrub level; in the other hand, Três Marcos, besides a developed and more diverse shrub layer, has also an established forest canopy. The different composition of the plant community may lead to different response capacities to the fauna requirements and, consequently, support different scavenger communities. From the several factors that can affect scavenger community spatial distribution, food availability and cover play a critical role in this shaping process (Torres et al. 2011). An example of this is presented in Álvares and Brito (2006), where the areas with high cover of deciduous oak forests, with no agricultural fields and no pinewoods, and with high levels of annual rainfall were defined as suitable for the pine marten, in north-western Portugal, and that it tends to occur in autochthonous forests. On the other side, the availability of prey (livestock) revealed to be the most important factor for Iberian wolf presence in Alvão/Marão at central northern Portugal and that they can coexist with humans even in areas of poor land cover (Eggermann et al. 2011). FCUP 74 Methodological and Ecological Insights on Wind Farm Environmental Impact Assessment

In terms of scavenging community, when comparing the species captured on both wind farms, it is possible to see that varies widely between them. In Vila Lobos, it was only registered the carcass removal by the red fox (Vulpes Vulpes), which was responsible for, approximately, 89.5%; as also by the domestic dog (Canis lupus familiaris) and the carrion crow (Corvus corone), responsible for 5.7% and 4.8%, respectively (Table 7). The non-captured samples were not considered in this calculation, totalizing a n of 72.

Table 7 - Scavenger species captured through camera-trapping, in all seasons, in Vila Lobos wind farm.

Number of individuals

Scavenger species Spring Summer Autumn Winter Total

Carrion crow (Corvus corone) 2 3 0 0 5 Domestic dog (Canis lupus familiaris) 2 3 1 0 6 Red fox (Vulpes vulpes) 28 25 35 6 94

Comparing these observations with the ones registered in Três Marcos wind farm, the only species in common is the red fox. However, in this case, it was responsible for a lower carcass removal percentage (48.3%). However, while in Vila Lobos one, scavenger bird species was registered, and the domestic dog was the only terrestrial scavenger (Table 7); in Três Marcos the presence of mustelids (Martes sp. and Mustela nivalis) plays an important role in the removal of carcasses. Moreover, other terrestrial scavengers are also present, such as the the common genet (Genetta genetta), the Egyptian mongoose (Herpestes ichneumon) and the wild boar (Sus scrofa) (Table 8). In fact, approximately 37.9% of carcasses were removed by mustelids, followed by the Egyptian mongoose with 10.3% (n = 29).

Table 8 - Scavenger species captured through camera-trapping, in all the four seasons, in Três Marcos wind farm.

Number of individuals

Scavenger species Spring Summer Autumn Winter Total

Common genet (Genetta genetta) 0 2 0 3 5 Egyptian mongoose (Herpestes ichneumon) 1 1 1 0 3 Mustelids (Martes sp.) 0 2 0 0 2 Red fox (Vulpes vulpes) 2 6 5 1 14 Stone marten (Martes foina) 1 0 2 0 3 Wild boar (Sus scrofa) 0 1 0 0 1 FCUP 75 Methodological and Ecological Insights on Wind Farm Environmental Impact Assessment

Despite all these observations, the species captured removing carcasses do not necessary represent the existing scavengers’ species diversity as they do not allow the estimation of their abundance in the area. The total frequencies of the captured species are presented below, in Table 9, which allow different conclusions. Comparing these results, it is possible to see that, despite not being captured scavenging, wild boar and Egyptian mongoose also exist in Vila Lobos territory, but, apparently, in smaller proportions than in Três Marcos. However, the common buzzard, the common genet and, mainly, mustelids are still exclusive form Três Marcos territory (Table 9). It is also possible to affirm that, in Vila Lobos, the red fox appears to be more abundant (718) than in Três Marcos (95). Basically, both the habitat and the scavenger community differences may have led to the observed mean persistence time values discrepancy, as the interrelationships established between them. The wooded areas, such as Três Marcos wind farm, are expected to promote foraging behaviour, favouring it during adverse weather conditions, as it was already verified for birds group (Cassel and Wiehe 1980), which is not the case in Vila Lobos. Furthermore, under a tree canopy, the hot and dry conditions are not so extreme as the ones felt in open areas (such as Vila Lobos). Firstly, due to the shade effect on soil, and second, due to the pine’s high evapotranspiration rate (the dominant canopy in Três Marcos wind farm) (Leimona et al. 2015), which increases the air humidity. Under humid conditions, odours last longer in the air, ground and on vegetation; on the other hand, hot and dry conditions, besides not promoting bacterial degradation and, consequently, odorants release, also leads to a rapid vaporization and dispersion into the atmosphere, which means that only after a few minutes there is already little trace for mammals to follow (Conover 2007; Ruzicka and Conover 2012; Borner et al. 2017). Moreover, when compared with Vila Lobos wind farm, it can be classified as a place with a lower human presence, since Vila Lobos territory is intensively explored for and hunting, as observed during trials and through camera trap photos, and so, a more intense avoidance behaviour is expected. This was already verified, for example, for wild boars, where fleeing or hiding behaviour was observed in hunting activity presence. This leads them to use more covered habitats (e.g. forest), suggesting that the perception of an increased risk by wild boar makes them more cautious and increases the effect of fear on habitat use (Thurfjell et al. 2013). Besides all these factors, as it would be expected, richness was higher in Três Marcos wind farm, where there is a higher protective cover (Table 9) (Mangas et al. 2008).

FCUP 76 Methodological and Ecological Insights on Wind Farm Environmental Impact Assessment

Table 9 - Total number of captures of each species for all seasons, in Vila Lobos and Três Marcos wind farms. Species Vila Lobos Três Marcos Canidae 0 2 Carrion crow (Corvus corone) 19 8 Common buzzard (Buteo buteo) 0 1 Common genet (Genetta genetta) 0 23 Common kestrel (Falco tinnunculus) 2 0 Domestic dog (Canis lupus familiaris) 93 12 Egyptian mongoose (Herpestes ichneumon) 1 13 European pine marten (Martes martes) 0 1 Felis sp. 1 1 Least weasel (Mustela nivalis) 0 4 Martes sp. 0 5 Red fox (Vulpes Vulpes) 718 95 Stone marten (Martes foina) 0 42 Wild boar (Sus scrofa) 6 17 Total 840 224

Altogether, considering the question of the major determinant of the carcass- persistence, in this case, it appears to be more related with community abundance than diversity, since it was in Vila Lobos where the mean persistence times were lower. In Olson et al. (2016), for example, the same tendency was observed, since every one- scavenger increase in richness at a carcass resulted in a 6% increase in carcass consumption, although the average time (in hours) to carcass depletion was not affected by the richness of the local scavenger . The increasing scavenger community richness contributed just moderately to the carcass removal (Olson et al. 2016).

In the second comparison, between Lousã II and Picos - Vale do Chão wind farms, the logic followed was different from the previous one. In this case, assuming that the season had influenced the carcass-persistence of birds in Lousã II and not in Picos - Vale do Chão, it was only possible to compare WFE values for birds, since in Picos - Vale do Chão it was the only biological group sampled. Comparisons were performed separately for each season. Through the analysis of Figure 23, it is possible to affirm that carcass-persistence times are, generally, higher in Picos - Vale do Chão than in Lousã II, with the exception of the Autumn season, but, in this case, it can be considered a non-significant difference. Moreover, in both cases, a decrease in mean persistence time values was recorded, from Summer to Autumn; and a considerable increase in these values from Autumn to Winter, especially in Picos - Vale do Chão. FCUP 77 Methodological and Ecological Insights on Wind Farm Environmental Impact Assessment

14 12,00 PVC LO 12 8,70

10 7,70

8

4,63 6 4,30

3,76 Number of Days of Number 4 2,43 2,14 2

0 Spring Summer Autumn Winter Season Fig.23 - Mean persistence time, in days (푡)̅ (± standard error), registered in the four seasons, in Picos - Vale do Chão (PVC) wind farm and in Lousã II (LO) one. The number of days is indicated above the standard error bars.

Individually, while in Lousã II, such as described before, the mean persistence time is decreasing in the following order: Winter (4.63) – Spring (4.30) – Summer (3.76) – Autumn (2.43); in Picos - Vale do Chão there is an exchange between Spring and Summer season. However, in both places, and despite the one-year sampling distance between wind farms, the season with the smallest mean persistence time is Autumn. This result goes along to what was verified in the current fieldwork, reinforcing it. This may indicate that, in fact, this geographical area is under some specifically biogeographical circumstances or just houses a similar scavenger community in terms of composition and patterns. The fact that the sampling occurred one year before in Picos - Vale do Chão wind farm, gives less importance to the largest fire events in Lousã, which happened in the 2017 year. Habitat diversity and scavenger community composition, as explored in the previous case, may explain the differences between the carcass-persistence values. However, since Picos - Vale do Chão is located along a ridge of the Serra da Lousã, no significant visible differences on flora composition seems to exist between both wind farms. This could indicate that the scavenger community composition is more important than the habitat in the interpretation of the results. According to photo analysis, the most common scavenger in Picos - Vale do Chão (n = 28), was the red fox (50%), followed by stone marten (21.4%) and wild boar (17.9%) (Table 11).

FCUP 78 Methodological and Ecological Insights on Wind Farm Environmental Impact Assessment

Table 10 - Scavenger species captured through camera-trapping, in dry and wet season (July and October, respectively), in Picos - Vale do Chão wind farm.

Number of individuals Scavenger species July October Total

Common buzzard (Buteo buteo) 0 1 1 Garden dormouse (Elyomis quercinus) 1 1 2 Red fox (Vulpes vulpes) 7 7 14 Stone marten (Martes foina) 3 3 6 Wild boar (Sus scrofa) 3 2 5

Besides these three species, the common buzzard and the garden dormouse were also registered. The carnivorous mammals, in particular, the red fox and the stone marten, were responsible for 71% of the carcasses removal, followed by the wild boar (18%). The same conclusions regarding the first two mentioned species were taken during the monitoring programme of this wind farm in the first year of the exploration (2016), where they were responsible for 60% of the carcass removal (Bioinsight 2017). Garden dormouse was only registered on this wind farm and it was responsible for 2.6% of the carcass removal. According to Bertolino (2017), there are no recent data to confirm the presence of the garden dormouse in a wide area in the centre and south of Portugal, which is a necessary information since the species decline is a possibility (Cabral et al. 2005). Said that, the data collected in Picos - Vale do Chão project can be an important contribution to the species range knowledge. In Lousã II, the red fox holds the higher carcass removal percentage (66.9%), followed by the wild boar (22.6%) and by the stone marten (4.8%) (Table 11). A percentage of 1.6% was registered for the common buzzard and for the European hedgehog, and it was 0.8% for the Eurasian jay, the common genet and the domestic dog (Table 11). The European hedgehog, although being considered an insectivorous species, was already found feeding on bird’s eggs and on carrion, and so, their diet can be considered as opportunistic (Erritzoe et al. 2003; Haigh 2011; Ragg et al. 2011). The observations in this study just give more evidence on this behaviour. When analysing the detected scavenger community in both wind farms, there are four species in common: the red fox, the stone marten, the wild boar and the common buzzard (Tables 10 and 11). In fact, the species with the highest carcass removal percentages are the same on both wind farms, although in Picos - Vale do Chão the stone marten appears to have a more important role than the wild boar.

FCUP 79 Methodological and Ecological Insights on Wind Farm Environmental Impact Assessment

Table 11 - Scavenger species captured through camera-trapping in all seasons, in Lousã II wind farm.

Number of individuals

Scavenger species Spring Summer Autumn Winter Total

Common buzzard (Buteo buteo) 0 0 2 0 2 Common genet (Genetta genetta) 0 1 0 0 1 Domestic dog (Canis lupus familiaris) 0 0 0 1 1 Eurasian jay (Garrulus glandarius) 0 0 1 0 1 European hedgehog (Erinaceus europaeus) 0 1 1 0 2 Red fox (Vulpes vulpes) 23 18 33 9 83 Stone marten (Martes foina) 1 4 1 0 6 Wild boar (Sus scrofa) 1 12 8 7 28

To analyse the total frequencies of the observed species between these wind farms, for Lousã II wind farm case, only the captures that occurred during Summer and Autumn season were considered, in an attempt to minimize the discrepancies between samples, because the mean persistence values for the spring and winter seasons, in Picos - Vale do Chão, were not obtained through the camera-trap method. Moreover, the two wind farms were not sampled in the same year. Said that, it is presented in table 12, the observed species and respective frequencies, which allows to observe some similarities between community occurrences and enumerate several aspects that distinguish them.

Table 12 - Total number of captures of each species for all seasons, in Lousã II and Picos - Vale do Chão (PVC) wind farms. Species Lousã II PVC Common buzzard (Buteo buteo) 4 1 Common genet (Genetta genetta) 11 3 Domestic dog (Canis lupus familiaris) 0 2 Egyptian mongoose (Herpestes ichneumon) 0 1 Eurasian jay (Garrulus glandarius) 3 1 European hedgehog (Erinaceus europaeus) 3 1 Garden dormouse (Eliomys quercinus) 0 17 Martes sp. 29 0 Red Fox (Vulpes Vulpes) 396 40 Stone marten (Martes foina) 46 23 Wild boar (Sus scrofa) 323 15 Total 815 104

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The species with a higher frequency in both Lousã II (396) and Picos - Vale do Chão (40) wind farms is the red fox. The following species with higher frequency differs between them, since in Lousã II it was the wild boar and, in Picos - Vale do Chão, it was the stone marten. However, in Lousã II the next higher frequency is also occupied by the stone marten (Table 12). Concerning the community diversity, three species were not detected in Lousã II wind farm that were observed in Picos - Vale do Chão: the domestic dog, the Egyptian mongoose, and the garden dormouse, which frequency was high in this area. After analysing the scavenger community in both wind farms, a trustworthy cause-consequence relationship to explain the obtained carcass-persistence values cannot be established. Firstly, although the same camera trap number (n = 30) was used, the sampling in both wind farms differs by one year and, secondly, this last year (2017) was an atypical year, as already explained. However, as mentioned in the previous comparison between Vila Lobos and Três Marcos, carcass persistence appears to be more related with community abundance than diversity, thus reinforcing the need to explore this topic on future research. However, some explanations for the carcass-persistence differences between wind farms may reside on the competition factor. As already explained, 2017 year was defined as an extremely hot and dry year (IPMA 2017d) and these climatic conditions lead to an arid landscape. Since precipitation levels were much lower than normal (75% compared to normal in almost all regions), lower primary and higher mortality were the most widespread effects (Frédéric and Volkmar 2006). In fact, droughts may affect the upper trophic levels, not only through habitat modification, but also by changing food availability (Wetz et al. 2011) and, consequently, the species dependent on herbaceous and shrub layers may have suffered a population decline (Alho and Silva 2012; Crowther et al. 2018). Due to the interconnectivity between the different levels, the top predators (scavenger included) may undergo through a higher competition pressure, since prey availability is lower (Pereira 2010). Concerning the second possible explanation, although the Egyptian mongoose and the garden dormouse were two of the three divergent species between wind farms, the first has not been captured scavenging, and the second only twice, which cannot be considered as a significant contribution to the existing differences. However, the resulting frequencies were higher for Lousã II, which may explain why it was registered lower mean persistence time values. FCUP 81 Methodological and Ecological Insights on Wind Farm Environmental Impact Assessment

After a comparison by proximity, distanced wind farms were compared, more precisely, two comparisons were executed. The first one was between Vila Lobos and Lousã II wind farms. Although they were already analysed in chapter 3.1, to answer this objective they must be analysed together instead of individually. Since in both cases, data was collected within the scope of this work, following the same methodology, on the same days, the carcass-persistence differences cannot be attributed to sampling or methodological errors. However, as it was observed in chapter 3.1, the season has influence bird carcass-persistence on Vila Lobos and mice carcass-persistence on Lousã II, which should be taken into consideration. Considering quail removal, it is evident that in Spring and Summer, Vila Lobos presents lower mean persistence times comparatively with Lousã II, and for Autumn and Winter, the opposite tendency is observed (Figure 24). In this case, there seems to be a tendency for higher persistence in Lousã II during hot seasons and in Vila Lobos during cold seasons.

10 VL 9 7,55 LO 8

7

6 4,63 4,30 4,03 5 3,76

4 3,00 2,78

2,43 Number of Days of Number 3

2

1

0 Spring Summer Autumn Winter Season Fig.24 - Mean persistence time, in days (t)̅ (± standard error), according to Huso 2010 estimator, for birds, for Vila Lobos (VL) and Lousã II (LO) wind farms. The number of days is indicated above the standard error bars.

On the other hand, for mice removal, it was only in Summer when mean persistence time was higher in Lousã II than in Vila Lobos windfarms. On the other seasons, the opposite tendency is observed (Figure 25). FCUP 82 Methodological and Ecological Insights on Wind Farm Environmental Impact Assessment

7 VL 5,09 6 4,69 LO 4,03 5 3,27 3,34 4 2,45 3

Number of Days Numberof 2

1

0 Spring Summer Autumn Season

Fig.25 - Mean persistence time, in days (푡)̅ (± standard error), according to Huso 2010 estimator, for mice, for Vila Lobos (VL) and Lousã II (LO) wind farms. The number of days is indicated above the standard error bars.

When comparing what happened for birds and mice together, only in Spring season the tendency was different between the two wind farms, since in Summer and Autumn, Lousã II and Vila Lobos presented the higher persistence values, respectively (Figure 24 and 25). These differences can be related to scavenger community features. As already explored on chapter 3.1, the only scavenger species observed in Vila Lobos was the red fox, the domestic dog and the carrion crow (Table 7), while in Lousã II, besides the red fox, it was also observed the wild boar, the stone marten, the common buzzard, the European hedgehog and the Eurasian jay (Table 12). Concerning the different tendency observed for Spring season between birds and mice, there seems to be no explanation by scavenger community factors. There was no significant difference between red fox appearances between the two wind farms, and, concerning the domestic dog and the wild boar presence, they also present similar values on the mentioned season (Table 13). The differences that stand out rely on the stone marten (16) presence on Lousã II wind farm and the carrion crow (5) in Vila Lobos (Table 13).

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Table 13 - Total frequencies of the species observed at least one-time scavenging, in Vila Lobos (VL) and Lousã II (LO) wind farms, in the four seasons.

Number of individuals Spring Summer Autumn Winter VL LO VL LO VL LO VL LO Carrion crow (Corvus corone) 5 0 12 0 1 0 0 0 Common buzzard (Buteo buteo) 0 0 0 2 0 2 0 0 Common genet (Genetta genetta) 0 0 0 6 0 5 0 2 Common kestrel (Falco tinnunculus) 0 0 2 0 0 0 0 0 Domestic dog (Canis lupus familiaris) 36 1 32 0 23 2 2 5 Egyptian mongoose (Herpestes 0 0 0 0 1 0 0 0 ichneumon) Eurasian jay (Garrulus glandarius) 0 1 0 2 0 1 0 0 European hedgehog (Erinaceus 0 0 0 3 0 0 0 0 europaeus) Felis sp. 1 0 0 0 0 0 0 0 Red fox (Vulpes Vulpes) 177 162 198 83 252 313 91 99 Stone marten (Martes foina) 0 16 0 25 0 21 0 8 Wild boar (Sus scrofa) 0 32 3 203 3 120 0 62 Total 219 212 247 324 280 464 93 176

While the carrion crow was never observed scavenging birds, stone marten appears to have no preference between birds and mice. This can be explained by the simple fact that some carcasses are too large for certain scavengers and, in fact, Flint et al. (2010) distinguish this capacity between mammalian (e.g. red fox) and avian (such as crows) scavengers. This presupposes that larger carcasses present great effort for scavengers and, since it is in Spring when the food availability is higher, maybe the stone marten revealed a cost-benefit behaviour towards carcass. Although, as mentioned before, the common crow does not use quails in his diet, the stone marten has a higher frequency than this last, which could have led to a lower mice persistence on Lousã II wind farm, during Spring. Regarding the reversed trend between hot and cold season, there seems to be no evident motive, since it is in Lousã where the higher community diversity was registered as well as the higher frequencies, except on Spring where there is a difference of only 6 registers (Table 13). If only these features were taken into consideration, it would be expected that low mean persistence times appeared always on Lousã II wind farm compared with Vila Lobos, which is not the case for birds in cold seasons and bats in Summer. FCUP 84 Methodological and Ecological Insights on Wind Farm Environmental Impact Assessment

Due to data constrictions from the other projects, the second comparison was only possible between Alto dos Forninhos wind farm data, and data collected in this work. However, in Alto dos Forninhos wind farm, the variable season had an influence on carcass persistence times, which was not the case in Lousã II wind farm. This condition was taken into account through data analysis. Moreover, only the winter season was sampled with camera-trap methods, and so, comparisons were made with only winter data from the two wind farms of this study. When comparing the mean persistence time values between the three wind farms, it is possible to conclude that all the obtained values in Alto dos Forninhos wind farm (n = 24) are higher than the other two (Figure 26), and this difference is even greater when the camera-trap method was used (notably during winter season). Moreover, while in the hot seasons the mean persistence time values are lower in Vila Lobos (despite not very dissimilar from Lousã values on Spring season), the opposite was recorded in Lousã.

14,00 )

̅ AF t ( 11,5 12,00 VL

10,00 LO 7,55 8,00 7,2 5,9 6,00 4,60 4,43 4,36 4,63 3,52 3,79 4,00 3,06 2,44 2,00

Mean Persistence Time, in daysin Time, Persistence Mean 0,00 Spring Summer Autumn Winter Season Fig.26 - Mean persistence time, in days (푡),̅ registered in the four seasons, in three wind farms: Alto dos Forninhos (AF); Vila Lobos (VL) and Lousã II (LO).

Once more, an analysis of the ecosystem components was made regarding habitat and scavenger community. In Alto dos Forninhos, the most common scavenger species (n = 29), was the red fox (48.3%), followed by the common genet (13.8%), European badger (17.9%) and, finally, stone marten (17.9%) (Table 14). This was the only territory, analysed in this work, where the European badger appeared in camera-trap photos.

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Table 14 - Scavenger species captured through camera-trapping in winter seasons, in Alto dos Forninhos wind farm.

Scavenger species Number of individuals

Common genet (Genetta genetta) 2 Stone marten (Martes foina) 2 European badger (Meles meles) 1 Red fox (Vulpes Vulpes) 6

Considering that this wind farm belongs to the Natural Park of Serra de S. Mamede (north of Alentejo), it is expected that the high plant diversity holds distinct communities of animals, especially birds of prey (ICNF 2018). The Bonelli's eagle (Aquila fasciata), the griffon vulture (Gyps fulvus) and the cinereous vulture (Aegypius monachus) are examples of prey bird species that can be found in this territory (CIBIO and Universidade de Évora 2018). However, since the camera-trap methodology was only applied during the winter season, the species appearance is expected to be lower than in the other ones and there is a lower probability to capture the higher fauna diversity existent. As it can be understood through table 15 analysis, the only captured species that increases the existing local diversity is the domestic dog.

Table 15 - Total number of captures of each species for winter seasons, in Alto dos Forninhos wind farms.

Species Alto dos Forninhos Common genet (Genetta genetta) 5 Domestic dog (Canis lupus familiaris) 1 European badger (Meles meles) 1 Red fox (Vulpes Vulpes) 8 Stone marten (Martes foina) 10 Total 25

Although a higher community diversity was expected, it is in Lousã II that a higher diversity is observed (six species) (Table 16). The only absent species, when compared with Alto dos Forninhos, was the European badger, that, as already mentioned, was exclusive from this territory. It is on Vila Lobos, on the other hand, where the lower values are found (2 species). From Table 18 analysis, it appears that Alto dos Forninhos community is more similar to the one observed in Lousã since two species (common genet and stone marten) only exist in the two wind farms.

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Table 16 - Total number of captures of each species in winter seasons, in Alto dos Forninhos (AF), Vila Lobos (VL) and Lousã II (LO) wind farms.

Species AF VL LO Common genet (Genetta genetta) 5 0 2 Domestic dog (Canis lupus familiaris) 1 2 5 European badger (Meles meles) 1 0 0 Martes sp. 0 0 4 Red fox (Vulpes Vulpes) 8 91 99 Stone marten (Martes foina) 10 0 8 Wild boar (Sus scrofa) 0 0 62 Total 25 93 180

However, when mean persistence times are compared, the value proximity is higher towards Vila Lobos (Figure 26). This part of the work was an attempt to associate the local scavenger community with the calculated carcass-persistence values. However, some caution has to be considered in the analysis. Firstly, the available data of the projects used to make comparisons with the wind farms studied in this work was collected for other purposes, and as such, it was not possible to execute all the possible comparisons. Secondly, the 2017 year, where the major amount of data collection occurred, was an atypical year concerning weather conditions, which may have greatly influenced biotic and abiotic factors. The carcasses consumption by vertebrates is known to vary spatially and temporally, due to the different biotic and abiotic factors in a certain area, such as temperature, habitat, carcass size, human presence or other disturbances of the vertebrate scavenger community (Selva et al. 2005; Olson et al. 2012; Moleón et al. 2015; Abernethy et al. 2016; Smith et al. 2017; Turner et al. 2017). In fact, carcass use by facultative scavengers proved to be a non-random process in a natural temperate forest, since it depends on both extrinsic factors and behavioural scavengers adaptations (Selva et al. 2005). That being said, it would be expected that, within a certain landscape and weather conditions, carcasses assemble similar local scavenger communities (Killengreen et al. 2012), and, therefore, resulted in a similar persistence probability for sites near each other (Borner et al. 2017). However, this was not observed in this study case, for example in Lousã II and Picos - Vale do Chão wind farms, which are located on the same mountain. In Borner et al. (2017), for example, carcass persistence also highly varied even between powerlines that were close together. They hypothesize that this may be due to a more opportunistic behaviour by local scavengers or due to local FCUP 87 Methodological and Ecological Insights on Wind Farm Environmental Impact Assessment

variation in microhabitats. Turner et al. (2017) also include this complex interplay with microsite habitat attributes to the equation of carrion acquisition and vertebrate scavenging community dynamics, as a relevant variable on carcass persistence. They concluded that even slight variation in carcass placement allows a spectrum of species to access carrion differentially. In other words, their visibility and surrounding habitat features, such as local flora composition, terrain slope, orientation, etc., could have the potential to influence carcass detection by vertebrates. It was already observed that lynx hide or camouflage their kills in an attempt to restrain scavenging by birds (Jobin et al. 2000). This situation is not so relevant when scavenger species use their olfactory orientation, such as wild boar and red fox (Ray et al. 2014). However, the shape, size, and persistence of odor plumes originated at carcasses is also a micro-habitat shaped variable (Ruzicka and Conover 2012), which may have influenced carcass-persistence times in this study. This microsite (fine-scale) differences may also influence competition for carcasses (Armenter and MacMahon 2009; Beasley et al. 2016). The carcass-persistence results may also have been influenced by the seasonal variation in prey susceptibility to mortality, such as the migratory movements of some species, for example, (Pereira et al. 2014), which were not considered in this study. Beyond these variables, species behaviour may have influenced carcass removal. The stone marten, the European jay, and the wild boar avoided carcasses located in open areas, as registered in other studies (Selva et al. 2005). On the other hand, species like prey birds, common crow, and domestic dogs had no avoidance behaviour towards this type of areas (Selva et al. 2005). This was especially evident on Vila Lobos wind farm, where these species occurred regularly. However, the high presence of domestic dog may be a consequence of hunting and grazing activities on this wind farm. On the other hand, the red fox, known as a habitat generalist (Baker and Harris 2014), was captured scavenging in all types of habitats in this study. This observation may be a result of a human-altered landscape, the wind farm, since the interspecific competition favours fewer and more generalist consumers, such as the red fox, due to their ability to rely upon a broad array of food and habitat types (Swihart et al. 2003; Prange and Gehrt 2004; DeVault et al. 2011). Another aspect that can be mentioned is the presence of trap-shy scavengers that might have avoided its placement location. Moreover, scavengers may also avoid areas tainted with human odours (Kostecke et al. 2001). On the other hand, the attraction may also occur, and so, scavengers may have followed human trails to carcasses (Kostecke et al. 2001), and even form search images of the carcasses sites, which FCUP 88 Methodological and Ecological Insights on Wind Farm Environmental Impact Assessment

makes them more efficient at finding them as time progressed (Ruzicka and Conover 2012). Nams (1997), for example, demonstrated that skunks improved the distance at which they detected carcasses after repeated offerings of the same food type. However, these different behaviours toured camera-trapping methodology it may differ between species (Kostecke et al. 2001). All these possible variability sources make it difficult to compare mortality estimates between sites, even to the ones close to each other.

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3.3 Carcass-Persistence Versus Turbine Distances

Multiple regression models were used to verify which combination of two of these three variables – type of carcass and distance to the turbine; season and distance to the turbine – better explained the removal rate. Starting with Vila Lobos wind farm and with the type of carcass and distance to the turbine as independent variables, the parametric model with the smaller AIC value was the Log-normal (AIC = 712.6). Stepwise results, however, revealed that the removal of the variable type of carcass and distance implied a decrease of AIC values to 707.68 and 710.05, respectively. This means that both variables, in Vila Lobos case, had no influence on carcass removal. Considering the same wind farm, but now testing the variables season and distance to the turbine, the scenario changes. Although the best fitting parametric model is the Log-normal one, as in the case above (AIC = 711.08), the removal of the variables season and distance meant an increase in the AIC value (AIC = 712.21), and so, these variables must be kept in the final model. Regarding the results expressed in table 17, the only statistically significant value was obtained for the winter season in the distance range of 75 – 115 m (p = 0.025788), which means that in winter, persistence was significantly lower than in the other seasons.

Table 17 - Results of the Stepwise process, for the final model, considering the variables season and type of carcass. The Autumn was considered as the reference season for the variable season, as it was <75 range for the distance variable. The statistical significant p values (<0.05) are indicated with a *.

Covariables Estimate Standard Error z p Intercept 0.94869 0.2587 3.6678 0.000245* Spring -0.46350 0.3655 -1.2682 0.204714 Winter 0.71179 0.4491 1.5850 0.112971 > 115 -0.23112 0.3662 -0.6311 0.527951 75 - 115 -0.01771 0.3959 -0.0447 0.964318 Spring:>115 0.52079 0.5203 1.0008 0.316901 Summer:>115 0.25192 0.5182 0.4862 0.626832 Winter:>115 0.22005 0.6342 0.3470 0.728611 Spring:75-115 0.76908 0.5606 1.3720 0.170073 Summer:75-115 0.00807 0.5597 0.0144 0.988497 Winter:75-115 -1.52725 0.6851 -2.2294 0.025788* Log (scale) 0.03227 0.0571 0.5649 0.572146

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This observation is consistent with the results discussed in chapter 3.1 since it was observed that the winter season had influenced bird carcass-removal. This becomes more evident when analysing the mean persistence time values, since, in winter season, the standard error values tend to be either above or below the standard error values of the remaining seasons (Figure 27).

12 9,12

8,50 ) )

̅ 11 푡 ( 10 9 6,54 8 5,47 7 5,08 4,31 6 3,81 3,58 3,31 5 3,22 4 2,85 3 2 1,17

Mean persistence time, in days days in time, persistenceMean 1 0 A B C A B C A B C A B C Spring Summer Autumn Winter Season

Fig.27 - Mean persistence time, in days (푡)̅ (Huso, 2010) (± standard error) calculated for Vila Lobos wind farm, for each distance intervals: < 75 m (A); 75 – 115 m (B) and > 115 m (C), for the four seasons. The number of days is indicated above the standard error bars.

In the survival Curves presented next (Figure 28), according to the non- parametric (Kaplan-Meier) and parametric (Log-normal) models, for the final model, it is also evident that in the range between 75 – 115 m, for the winter season, carcasses take less time to be removed. When performing a per-season analysis, using figures 27 and 28, it is possible to observe some similarities and dissimilarities between the obtained results. In Spring and Summer season carcass removal was faster in < 75 m range; while in Autumn and in Winter, the faster removals were observed in > 115 m and in 75 – 115 m ranges, respectively. On the other hand, the slowest carcass removal in Spring and Summer was registered in the 75 – 115 m range; while in Autumn and Winter the mean persistence time was higher at < 75 m and at > 115 m ranges, respectively. Basically, in the Spring and Summer seasons, the tendency was the same: < 75 m, followed by > 115 m and, finally, higher persistence times in 75 – 115 m distance range. On the other hand, Autumn and Winter do not present any defined trend. FCUP 91 Methodological and Ecological Insights on Wind Farm Environmental Impact Assessment

Possible explanations for these observations can be related to the protection level fluctuation offered to carcasses by the existing rocks and vegetation. At greater distances, for example, the protection level can be probably higher than closer to turbines due to the existence of uncovered areas, which can probably lead to higher carcass removal rates, as it was registered on Spring and Summer. Also, the strong winds and snow felt during Spring (IPMA 2018b, c), may have an influence on the number and hunting range of scavengers. These climatic conditions may also influence the scavengers’ proximity to shelters since it can be an immediate refuge from extreme climatic conditions (Zalewski 1997), and these ones could be more present on the 75 – 115 m distance range, which could explain the tendency observed during Winter.

Fig.28 - Probability of a carcass remaining in place t or more days depending on its distance to the reference turbine, for each time of year, represented by the Kaplan-Meier Curve and Log-normal model (final model).

Despite all these differences between distance ranges along the four seasons, if the calculated mean persistence time for each season is compared, the average number of days that the carcasses remain undisturbed increases from Summer to Spring, then FCUP 92 Methodological and Ecological Insights on Wind Farm Environmental Impact Assessment

to Autumn and finally to Winter, as it would be expected and was already verified on chapter 3.1. When a distance range analysis is performed, the < 75 m and > 115 m ranges show the same tendency in relation to carcass removal, since it is slower in Winter and, then, begins to increase in Autumn, followed by Spring and, finally, is faster in Summer (Figure 29). Once more, this tendency agrees with the one calculated in chapter 3.1, and, as explained in that chapter, this tendency was expected. However, in the distance range between 75 m and 115 m, time for carcass removal decreases from Spring, followed by Autumn, Summer and, at last, with the lowest mean persistence time is Winter (Figure 29).

12 9,12

) ̅) 11 8,50 10 9 6,54 8 5,47 5,08 7 6 4,31 3,58 3,81 5 3,31 3,22 4 2,85 3 2 1,17 1

Mean persistence time, in days (t days in time, persistenceMean 0

Spring Winter Spring Winter Spring Winter

Autumn Autumn Autumn

Summer Summer Summer < 75 m 75 – 115 m > 115 m Season

Fig.29 - Mean persistence time, in days (푡)̅ (Huso, 2010) (± standard error) calculated for Vila Lobos wind farm, for each season, according to distance intervals: < 75 m (A); 75 – 115 m (B) and > 115 m (C). The number of days is indicated above the standard error bars.

It was expected that scavengers would tend to forage more intensively away from turbines, and so, carcass-persistence would be lower at greater distances (Helldin et al. 2012). Nevertheless, this was only registered in the Autumn season (Figure 29). The scavenger community present in Vila Lobos wind farm, as already analysed in chapter 3.2.2, is dominated by the red fox and the domestic dog (Table 7). Since the dogs belong to shepherds or hunters, they have no turbine avoidance behaviour, and so, have no interference on the carcass-removal rate concerning the distance to the wind turbine. However, concerning the red fox, the situation may be different. In Menzel and Pohlmeyer (1999), the same distribution and habitat use were observed between areas FCUP 93 Methodological and Ecological Insights on Wind Farm Environmental Impact Assessment

with wind farms and reference areas without them, for the red fox, even during winter, when harsh conditions are expected. Moreover, in the same study, within a radius of 10 - 1000 m from a wind turbine the distribution of evidence proofs was similar for all distances from the turbine. However, Vila Lobos wind farm territory, as already mentioned, is characterized by intense cow and sheep herding (Figure 30) as well as hunting activities.

Fig.30 - Photo examples of cow and sheep herds using Vila Lobos wind farm territory.

These activities imply frequent human presence and this may lead to the scavenger adaptation to it (Ponce et al. 2010); to a lower number of terrestrial scavengers; and to a disturbance in the foraging activities of bird scavengers (Costantini et al. 2016). However, this is a controversial topic since there are several studies which reported both situations. In Díaz-Ruiz et al. (2016) study, for example, the red fox activity decreased in areas closer to human settlements. Moreover, in Łopucki et al., (2017), but in an agricultural landscape, the red fox visited interior wind farm areas significantly less often than control areas, but, in this case, the higher prey availability in farm areas was probably the main reason. On the other hand, the red foxes may find advantageous the proximity to human presence, due to the subsidiary resources (Bino et al. 2010). In Łopucki et al., (2017), due to the lower track density of hare (fox’s important prey species) and lower success in preying on small mammals under hindered hearing from turbine noise, a relationship between track density and distance to turbines was expected. Nevertheless, the authors did not find such relation in their study, which can be translated into a neutral response of red foxes to wind turbines. These results seem to corroborate the results of the present work. In Lousã II wind farm case, WFE outputs show that the parametric model with the smaller AIC value was the Log-normal (AIC = 756.88). The results from the Stepwise procedure indicate that the removal of the variables type of carcass and distance implied FCUP 94 Methodological and Ecological Insights on Wind Farm Environmental Impact Assessment

a decrease of AIC values to 749.64 and 751.31, respectively. In other words, carcass removal did not vary significantly along distances, both for mice and quail carcasses. When testing the variable season instead of the type of carcass, together with distance, for Lousã II wind farm, the conclusion was different from those registered on Vila Lobos case. The parametric model with the better fitting to the available data was the Log- normal one, with the least AIC value (AIC = 755.93). Stepwise analysis showed that the removal of both variables together leads to a decrease in the AIC value (AIC = 746.35), which would indicate that these variables had no influence on carcass removal, and so, should be excluded from the analysis. However, when analysed separately, the variable distance caused a decrease in AIC value (AIC = 742.75) and the variable season an increase (AIC = 751.31). These results indicate that only the factor season was statistically significant on carcass removal, which can be proved by the obtained Stepwise results (Table 18). However, it is in Summer season when this statistically significant value prevails (p = 2.01푒−03∗).

Table 18 - Results of the Stepwise process, for the final model, considering the variables season and type of carcass. The Autumn was considered as the reference season for the variable season. The statistical significant p values (<0.05) are indicated with a *.

Covariables Estimate Standard Error z p Intercept 0.2349 0.1041 2.256 2.41푒−02* Spring 0.0582 0.1506 0.387 6.99푒−01 Summer 0.4937 0.1598 3.089 2.01푒−03∗ Winter 0.2595 0.2072 1.252 2.10푒−01 Log (scale) -0.7018 0.0651 -10.777 4.42푒−27∗

The seasonal influence on the carcass-persistence was already explored in chapter 3.1, and, it has no relevance for the purpose of this objective once the variable distance was the one on study. Looking at the scavenger community in Lousã II wind farm, where no avoidance behaviour was noted, besides the red fox that is also the most abundant species in Vila Lobos, the following two most important species concerning the removal of carcasses, as seen on chapter 3.2.2, were the wild boar and the stone marten (Table 12). The wild boar behavioural plasticity enables them to thrive in human-dominated landscapes, and their attraction by anthropogenic food sources may even lead them to the vicinity of peri- urban areas (Cahill et al. 2012). Stillfried et al., (2017) argues that the wild boar behavioural plasticity is suitable to adjust him to human-dominated environments, in a FCUP 95 Methodological and Ecological Insights on Wind Farm Environmental Impact Assessment

potentially evolutionary adaptive manner, due to their opportunistic diet (Schley and Roper 2003). In fact, this absence of behavioural changes was already registered in several studies. In Fattebert et al. (2017), for example, the wild boar range sizes were not influenced by the proximity to infrastructures as a refuge effect, and in a study developed in Germany, the wild boar high tolerance towards human presence was proved by a smaller travel distance and a reuse of the trapping locations (Stillfried et al. 2017). The same behavioural towards human presence is observed concerning the stone marten. Like the wild boar, it is adapted to anthropogenic habitats (Wereszczuk et al. 2017) and it is considered a habitat-opportunistic carnivore (Genovesi et al. 1996). Therefore, the absence of an avoidance behaviour towards wind turbines is not an unexpected result. Although no available studies focus on the behaviour response of the wild boar and the stone marten to wind turbines operation, it can be assumed that the habituation process would be similar, as it was observed in red fox case. Looking at the results obtained for both wind farms under study, the same conclusion can be drawn regarding the influence of the variables type of carcass and distance together, on the carcass-persistence values, since in both cases they did not significantly influence carcass-removal. Said that, it can be concluded that the type of carcass does not influence its removal rate along distances. However, the high abundance of the red fox in both wind farms may explain the inexistence of this type of differences, due to its generalist and opportunistic diet (Paula et al. 2015). When the interaction between the variables season and distance is measured, the conclusions are different. While in Lousã II wind farm only the season has an influence on carcass removal (as already verified on chapter 3.1), in Vila Lobos, the carcass-persistence varied with season along distances, but it was only statistically significant for the winter season. These differences between the two wind farms could be explained by the different number of operational years, and, consequently, by the different periods for scavenger habituation to the disturbance. While Lousã II wind farm started in 2009 (Iberwind 2018a), Vila Lobos first started in 1998, but it was totally replaced in 2017 (Iberwind 2018b). This probably means that the scavenger community in Vila Lobos may not have had enough adaptation time not only to the new infrastructures but mainly to the increased human presence related to wind farm construction and posterior operation. This possible explanation is based on the fact that it is expected that when animals experience a disturbance frequently, they tend to become used to it, as it was already observed, for example, for ungulates (Stankowich 2008). FCUP 96 Methodological and Ecological Insights on Wind Farm Environmental Impact Assessment

Moreover, this year, during January and February data collection, which were considered as the winter sampling season, the wind velocity reached 95 km/h and 80 km/h, respectively (IPMA 2018b, c). These are considered exceptionally strong winds (IPMA 2018d), and so, the velocity reached and, consequently, the noise produced by wind turbines may have been higher (Broneske 2014). This could have been one motive for the higher mean persistence values, registered in Vila Lobos, in < 75 m and >115 m distance ranges, due to higher protection on the 75 – 115 m range, as explained before. There is a clear lack of studies concerning the impacts of wind farm infrastructures on terrestrial mammals and, particularly, on the pattern of activities and ranges of species. These knowledge gaps provide guidance for future research. It would be interesting to understand how the habituation progresses along the operational years since the year zero; if this behavioural adaptation is equal for all the scavenger species; and how many time is required to develop a well-adapted community. In this way, the sampling protocol, and, more precisely, the cameras distribution, could be adjusted to incorporate this information and would be considered when calculating the correctional factor of carcass removal, in environmental post-construction impact assessment projects. Said that, further similar studies at different stages of wind farm development and use are needed because the impact on terrestrial mammals of wind turbines in newer farms may reveal different behaviours, and so, carcass removal factor may fluctuate over time.

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3.4 Use of New Detectability Models in Detectability Trials

According to the Wildlife Fatality Estimator platform analysis, the detection probability varied according to the visibility class, both for usual and new models (Figure 31). These results reaffirm what has been proved in several studies (Arnett et al. 2005, 2009; Smallwood 2013; Peters et al. 2014) and reinforce that this variable should be considered for detectability trials and, consequently, incorporated on fatality estimation.

100 Usual Models 95 90 New Models 85 80 75 70 65

60 Detection Probability (%) Probability Detection 55 50 1 2 3 4 Visibility Classes Fig.31 - Detection probability of the usual and new models, according to each visibility class (1 - easy, 2 - moderate, 3 - difficult and 4 - very difficult).

Independently of the model type (usual or new ones), the same tendency was verified regarding visibility classes (Figure 31), since detectability declined from visibility class one (easy) to two (moderate), slightly increased from class two to class three (difficult) and then decreased again between classes three and four (very difficult). The explanation for this trend may rely on the field characteristics, which will be explored ahead. Relatively to the second factor tested, which is models’ size, there was also a significant difference in the detection probability, independently of the visibility class, as was expected due to observations in real carcasses trials (Osborn et al. 2000; Ponce et al. 2010; Jenkins et al. 2015) (Figure 32). However, the significant decrease of carcass detection with decreasing carcass size (Borner et al. 2017) was more evident on the usual models since detection probability decreased from 89% (95% confidence interval: 0.82 - 0.93) to 78% (0.71 - 0.85) and then to 60% (0.53 - 0.68), while for new models despite of the reduction from 80% (0.63 - 0.96) to 65% (0.47 - 0.84) from large to medium models, from medium to small there was no variation (Figure 32). FCUP 98 Methodological and Ecological Insights on Wind Farm Environmental Impact Assessment

95 Usual Models 90 New Models 85 80 75 70 65

60 Detection Probability (%) Probability Detection 55 50 Large Medium Small Models' Size Fig.32 - Detection probability of the usual and new models, according to each models’ size (large, medium and small).

In this case, contrarily of what was denoted concerning the influence of visibility classes on detectability, the tendency was not the same in both type models (Figure 32). As registered on the usual detectability models, it was expected that the detectability increased with the models’ size increase. In Ponce et al. (2010), for example, despite carcass detection trials were performed with real ones and on an agriculture landscape, this tendency was observed, since large carcasses were detected in a higher proportion (71.7%) than medium (55.8%), small (32.1%) and very small (33.3%) carcasses. When combining the two factors (visibility class and models’ size) it is possible to verify that both for usual and new models, the higher detection probabilities appear for the visibility class one (easy) for the three models’ size, with probabilities close to 100% or even 100% (Figure 33). The following higher detection probabilities are those for large

models on visibility class three (91%, 퐶퐼95%=0.82 – 0.96) and two (90%, 퐶퐼95%=0.81 – 0.95), for the usual models. The same tendency is observed for the new ones with

probabilities of 93% (퐶퐼95%=0.84 – 0.97) and 91% (퐶퐼95%=0.82 – 0.96), for visibility class three and two, respectively. Concerning the usual models, the smallest detection probabilities were verified for the small models, both on visibility class two (56%,

퐶퐼95%=0.42 – 0.69), three (59%, 퐶퐼95%=0.45 – 0.72) and four (33%, 퐶퐼95%=0.22 – 0.47),

and also for the medium model size in visibility class four (58%, 퐶퐼95%=0.44 – 0.72). The new models, on the other hand, had the smallest probabilities for medium (70%,

퐶퐼95%=0.56 – 0.81; 67%, 퐶퐼95%=0.52 – 0.79) and small (69%, 퐶퐼95%=0.55 – 0.81; 66%,

퐶퐼95%=0.52 – 0.78) models in visibility class two and four, respectively (Figure 33).

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100 Usual Models New Models 90

80

70

60

50 Detection Probability (%) (%) Probability Detection 40

30 1L 1M 1S 2L 2M 2S 3L 3M 3S 4L 4M 4S Visibility Class + Model Size Fig.33 - Detection probability when combining both factors: visibility class and models’ size. The first number indicates the visibility class and the following letter indicates the model’s size (L – Large; M – Medium; S – Small).

Using parametric and non-parametric tests, it was possible to verify again that searcher detection of volitionally placed models varied with visibility classes, independently of the model size, both for usual (ANOVA 퐹= 8.103, df = 3, 푝 = 0.000) and new models (ANOVA 퐹= 4.473, df = 3, 푝 = 0.010), as perceived through WFE output. These results, once more, corroborate the dependence of detectability rate on the characteristics presented by trial localizations (Smallwood 2013; Peters et al. 2014). However, post-hoc analyses indicated that this difference is only significant between some visibility classes. Concerning the usual detectability models, differences between visibility classes were registered between class one (easy) and four (very difficult) (Tukey HSD post hoc tests; 푝 = 0.000). On the other hand, for the new models, the visibility class one (easy) had significant differences, not only with visibility class four (very difficult), (Tukey HSD post hoc tests; 푝 = 0.013), as verified for usual models, but also with visibility class two (moderate) (Tukey HSD post hoc tests; 푝 = 0.025). Although a significant difference between all visibility classes was not confirmed, the obtained P values allow establishing which visibility classes are, apparently, more similar or different, according to its implicit characteristics. For example, in the case of usual models, the obtained 푃 value between visibility class one (easy) and two (moderate) it is close to the threshold (푝 = 0.056), which suggest that the differences between these two classes are close to being significant. Said that, for the usual models, two homogenous groups were formed: the first group containing visibility class two, three and four; and the second one with visibility FCUP 100 Methodological and Ecological Insights on Wind Farm Environmental Impact Assessment

class one, two and three. On the other hand, for the new models, although one of the groups is exactly the same as a previous one (with visibility classes two, three and four), the other one misses visibility class two. These results show the same tendency in detectability, independently of the models’ type, since detectability in class three (difficult) was higher than in class two (moderate) (Figure 34). These observations seem to indicate that in Vila Lobos wind farm the standard visibility classes division was not appropriated. A possible explanation for these results is the territory characteristics themselves. As can be observed in Figure 5 on chapter 2.3, in class two (b) and three (c) photographs, the colours and texture observed are very different from each other. In fact, search trials were performed, as mentioned before, during the dry season and so, in visibility class three, the herbaceous meadows were dry and yellowish. On the other hand, in visibility class two, despite no significant herbaceous stratum was present, the soil was revolved, which creates several protrusions. Because of these characteristics, a plausible hypothesis is that a lighter soil (due to the herbaceous colour) creates more contrast with models, both for the usual ones (with brownish colours) and for the new ones. Moreover, the extremely irregular surface leads to a higher camouflage effect, mainly for the smaller models. Another factor that is known to affect models’ detectability is the existence of slopes on the searched surface (Strickland et al. 2011; Brown et al. 2013) and, in some studies such as Arnett et al., (2009), this variable is considered when defining visibility classes. The visibility class two considered in this study has indeed, although in a reduced proportion, some slopes. This may have contributed, although in a non- significant way, to the lower detectability in visibility class two than in three, since this last one has almost no slopes.

Fig.34 - Detectability according to the defined four visibility classes, expressed through the mean number of models observed during search trials. Data collected with the usual and new detectability models are presented on the right and left graphic, respectively. FCUP 101 Methodological and Ecological Insights on Wind Farm Environmental Impact Assessment

Through this type of analysis, as it was registered on WFE platform results, there was also a significant difference in the mean number of observed models, independently of the visibility class, between models’ size, as already reported (Jenkins et al. 2015). This variation was verified both on the usual models (ANOVA 퐹= 6.909, df = 2, 푝 = 0.003) and on the new ones (ANOVA 퐹= 3.615, df = 2, 푝 = 0.038). Regarding the usual models, post-hoc analyses indicated that this difference is significant between small and large ones (Tukey HSD post hoc tests; 푝 = 0.002). It was also evident that there is a detectability discrepancy, although not statistically different, between small and medium models (Tukey HSD post hoc tests; 푝 = 0.068). The detectability differences were irrelevant between medium and large models (Tukey HSD post hoc tests; 푝 = 0.370). Although new models showed a significant difference (ANOVA = 3.615, df = 2, p = 0.038), the Tukey test did not find any significant difference between size groups, despite the obtained p-value was on the threshold of significance (p = 0.06) when analysing large models. Once again, these tests confirm an unexpected detectability tendency concerning the models’ size, since small and medium models had the same detectability (Figure 35).

Fig.35 - Detectability according to the size (small, medium or large) of the models used during trials, expressed through the mean number of models observed during search trials. Data collected with the usual and new models are presented on the right and left graphic, respectively.

When both variables (visibility class and models’ size) were analysed together, only one significant statistical difference was found, for small models in visibility class three (Mann-Whitney test; 푝 = 0.043). Some relevant differences were also detected, although not statistically significant, for the small (Mann-Whitney test; 푝 = 0.050) and large (Mann-Whitney test; 푝 = 0.068) models in visibility class four. The efficiency of the new small models can also be verified with the results obtained through WFE platform, FCUP 102 Methodological and Ecological Insights on Wind Farm Environmental Impact Assessment

once the largest differences between detection probabilities prevail in this model size, for classes four (33%), three (16%) and two (16%). It appears that the new small model presents a better performance than the usual ones, and it goes along with what was verified on detectability according to models’ size (Figure 35), where small models detectability equalled to the medium one, since the new medium models did not seem to bring any significant advantage. When the total number of models detected by the searcher is analysed (Table 20), other conclusions may stand out, beyond those already mentioned. In fact, only the new medium model had the same, or worst performance, then the usual models. Small and large models always presented more detected models or only one with fewer detections, respectively, when comparing both trial results.

Table 19 - Total number of models’ observations for each visibility class (I, II, III and IV) and for every models’ size (Small, Medium and Large). Table with the sums for each model type: the normally used ones (a) and the new ones (b).

(a) Visibility Classes (b) Visibility Classes

I II III IV I II III IV Small 28 16 19 9 Small 30 17 25 21 Medium 30 23 23 18 Medium 30 23 23 17 Large 9 28 27 23 Large 30 29 25 29

The fact that the differences did not appear to be significant through all the parametric and non-parametric tests above, may be a result of the small sample size, once, for example, when comparing both variables together, only three values were obtained for the combination of two variables (e.g. small models in visibility class one), since only three replicates were performed. Comparing the analytical methods used with WFE web-based software and SPSS software, it can be asserted that the results are coincident, although through non- parametric tests it was possible to better understand where the observed differences were significant. Taking into account all the obtained results, and answering the proposed objective, it can be admitted that there is a higher detection probability with the new models’ utilization. Results show that new models present indeed a higher detection probability

(83%, 퐶퐼95%=0.79 – 0.87) than the normal used ones (76%, 퐶퐼95%=0.71 – 0.80). Despite not being a very dissenting result (a difference of 7%), it should anyway be considered when planning searcher efficiency trials, since this difference may result in different death FCUP 103 Methodological and Ecological Insights on Wind Farm Environmental Impact Assessment

rates. Although the substitution of real carcasses for decoys it is a strong point in favour of these trials, it is known that their realism is highly variable (Barrientos et al. 2018). This opens the discussion of which models should be used on searcher efficiency trials and the necessity to standardize the appearance they should have. Unfortunately, there are no published works studying the influence of the models’ appearance on correctional factor calculation or even proposing standardized models. Furthermore, the majority of the reports does not specify which models are used, which makes the discussion of this topic a really challenging one. This work has taken a step in that direction, inasmuch as the new models produced better detection rates, but it cannot be guaranteed that they do not underestimate or overestimate the real detection probability of real carcasses, despite the models used in this study share more characteristics with real carcasses than the ones normally used. In future works, concerning this objective, the detectability bias resulting from both models’ types should be compared with the real mortality rate of specific wind farms, in order to understand which of the models’ type gives the best fitting mortality rate. FCUP 104 Methodological and Ecological Insights on Wind Farm Environmental Impact Assessment

FCUP 105 Methodological and Ecological Insights on Wind Farm Environmental Impact Assessment

4. Conclusion

Carcass persistence calculation of Vila Lobos and Lousã II wind farms demonstrated that season can influence carcass persistence times, as verified in other studies (Ray et al. 2014; Turner et al. 2017). However, season acted differently for the carcass type, since it just explained carcass persistence for birds in Vila Lobos and for bats in Lousã II. This result does caution against extrapolations from one place to another because even territories geographically close already showed significant differences between them in other studies (Hull and Cawthen 2013). Another support for this necessity is demonstrated through carcass persistence time: while in Vila Lobos hot seasons led to lower persistence times for both carcass type, in Lousã this tendency was not observed. Both observations emphasize that the wind farm territory specific characteristics or punctual events may influence carcass removal, and so, the need to site-specific monitoring is reinforced. Although a wind farm comparison by proximity or distance was performed, it was not possible to establish a common relation between scavenger community composition and carcass-persistence, valid for all the available wind farms along the Portuguese territory, despite some observed similarities in some cases. Through the proximity comparison, the wind farm territories where the higher frequencies of scavengers were captured, revealed the fastest removal times. Only in Vila Lobos wind farm, it was detected a significant influence of turbines distance to carcass persistence, when considering season influence, more precisely in winter, on 75 – 115 m range. Moreover, only in Autumn season, the carcass persistence followed the proposed hypothesis (removal times decrease with distance increase) since carcass persistence was lower in more distanced samples. On the other hand, the variable combination of the type of carcass and turbine distance had no influence on both wind farms. The adaptation factor could have been the key factor in this case, since scavenger community in Vila Lobos may not have had enough adaptation time to the new infrastructures, and also, the existent scavenger community has been already reported as having human cohabiting species (Fattebert et al. 2017; Wereszczuk et al. 2017). Concerning detectability trials, the correspondent detectability varied both with visibility class and models’ size as it was demonstrated in other studies (Atienza et al. 2014; Rodrigues et al. 2014; Barrientos et al. 2018), for the usual and the new models. The results suggest that the new models have, in general, a better detectability than the usual ones, with the small one revealing the best performance. FCUP 106 Methodological and Ecological Insights on Wind Farm Environmental Impact Assessment

Current literature on the impacts of wind energy has two main research gaps which challenged the development of this study: the scarcity of studies on the impacts on terrestrial biodiversity and how the most common Portuguese species react to the wind farm presence. Moreover, no studies on the detectability models’ suitability for field trials were found. Said that, this is a first effort to provide more necessary background knowledge for the development of a more adjusted monitoring post-construction protocol, concerning birds and bats mortality. Furthermore, the correctional factors calculation under the new methodologic implementations in this study, such as a larger buffer for camera trap placement, the use of only two types of real carcasses in removal trials and the cameras visiting every 15 days, gives Bioinsight the opportunity to compare the obtained results with the ones previously calculated under the usual guidelines and, possibly, an opportunity to improve the cost-benefit relationship in wind farms environmental impact assessment projects, more precisely on post-construction monitoring processes. The next steps concerning the objectives developed on this work would be to design a sampling protocol to evaluate the relative importance for carcass removal time of the total number of individuals, species richness and diversity of the scavenger community, as well as, the effects of intra and interspecific competition. Also related to the scavenger communities’ objective, this was a first approach which should be replicated in other wind farms, not only along the Portuguese territory but also with different habitat types and scavenger communities. In this way, it would be possible to assign to each community a carcass removal taxa interval as a comparative tool. Regarding the second objective, in future works, different distance ranges (smaller ones, for example) could be tested and other variables such as species home range, prey availability, and road proximity could be included. Finally, concerning the attempt to use new detectability models, models for passerines could be also proposed and the applicability of the new ones tested here should be tested on other wind farms and types of habitat and analyse which ones’ bias less the real detectability.

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FCUP 131 Methodological and Ecological Insights on Wind Farm Environmental Impact Assessment

Attachments

Attachment I Species detected and corresponding total observations number through camera trap method, on the five wind farms selected: Vila Lobos (VL), Três Marcos (TM), Picos – Vale do Chão (PVC), Lousã II (LOII) and Alto dos Forninhos (AF). P means the presence of certain species on the wind farm and the * signal appears whenever the number of individuals was not counted with much precision since they appear in groups or in high numbers. The number of individuals counted follows the P letter.

Number of individuals

Scavengers species VL TM PVC LOII AF

Mammals

Canidae P (2) Cattle (cows, goats, and sheep) P * Cervidae P (29) Common genet (Genetta genetta) P (23) P (3) P (13) P (5) Domestic dog (Canis lupus familiaris) P (129) P (12) P (2) P (8) P (1) Egyptian mongoose (Herpestes ichneumon) P (1) P (13) European badger (Meles meles) P (1) European hedgehog (Erinaceus europaeus) P (1) P (3) European pine marten (Martes martes) P (1) European rabbit (Oryctolagus cuniculus) P (139) P (3) P (13) Felis sp. P (1) P (1) Garden dormouse (Eliomys quercinus) P * P (20) Least weasel (Mustela nivalis) P (1) Martes sp. P (6) Mustelids (Martes sp.) P (5) P (11) P (33) NI Carnivorous P (11) NI Mammal P (20) P (1) P (12) P (1) NI Micromammal P (12) P * P (16) P (26) P (1) Red deer (Cervus elaphus) P (6) P (209) Red fox (Vulpes vulpes) P (718) P (95) P (41) P (657) P (8) Red squirrel (Sciurus vulgaris) P (3) P (2) P (7) Roe deer (Capreolus capreolus) P (26) Sheep flocks P * Soricidae P (1) Stone marten (Martes foina) P (42) P (70) P (10) Wild boar (Sus scrofa) P (6) P (12)* P (15) P (417)

Birds

Alaudidae P (14) Black redstart (Phoenicurus ochruros) P (35) P (2) Caprimulgus sp. P (2) Carrion crow (Corvus corone) P (18) P (8) Coal tit (Periparus ater) P (2) Common blackbird (Turdus merula) P (2) P (10) P (1) P (4) Common buzzard (Buteo buteo) P (1) P (1) P (4) Common chaffinch (Fringilla coelebs) P (6) FCUP 132 Methodological and Ecological Insights on Wind Farm Environmental Impact Assessment

Number of individuals

Scavengers species VL TM PVC LOII AF

P (2) Common kestrel (Falco tinnunculus) Dartford warbler (Sylvia undata) P (1) Eurasian jay (Garrulus glandarius) P (14) P (1) P (4) Eurasian skylark (Alauda arvensis) P (1) Eurasian woodcock (Scolopax rusticola) P (3) P (2) Eurasian wren (Troglodytes troglodytes) Eurasian wryneck (Jynx torquilla) P (1) European crested tit (Lophophanes cristatus) P (1) European green woodpecker (Picus viridis) P (1) P (2) European robin (Erithacus rubecula) P (29) P (2) P (1) P (1) European stonechat (Saxicola rubicola) P (18) P (2) Great spotted woodpecker (Dendrocopos major) P (1) Long-tailed tit (Aegithalos caudatus) P (6) Mistle thrush (Turdus viscivorus) P (1) P (3) P (10) Motacillidae P (8) P (1) Non-identified P (35) P (21) P (6) P (4) Northern wheatear (Oenanthe oenanthe) P (35) Red-legged partridge (Alectoris rufa) P (6) P (3) Rock bunting (Emberiza cia) P (2) P (1) Whinchat (Saxicola rubetra) P (3) Woodlark (Lullula arborea) P (1)

Amphibians

Anura P (1) Fire salamander (Salamandra salamandra) P (1)

Reptiles

Iberian emerald lizard (Lacerta schreiberi) P (5) P (1) Large psammodromus (Psammodromus algirus)